Provide estimation and data generation tools for new multivariate frailty models. This version includes the gamma, inverse Gaussian, weighted Lindley, Birnbaum-Saunders, truncated normal, mixture of inverse Gaussian, mixture of Birnbaum-Saunders, generalized exponential, Jorgensen-Seshadri-Whitmore, weighted Akash, weighted Shanker and weighted Sujatha as the distribution for frailty terms. For the basal model, it is considered a parametric approach based on the exponential, Weibull and the piecewise exponential distributions as well as a semiparametric approach. For details, see Gallardo et al. (2024) <doi:10.1007/s11222-024-10458-w>, Gallardo et al. (2025) <doi:10.1002/bimj.70044>, Kiprotich et al. (2025) <doi:10.1177/09622802251338984>, Gallardo et al. (2025) <doi:10.1038/s41598-025-15903-y>, Kiprotich et al. (2026) <doi:10.1080/00949655.2025.2584734 and Mohammadi et al. (2026).

This package performs multi-omic differential network analysis by revealing differential interactions between molecular entities (genes, proteins, transcription factors, or other biomolecules) across the omic datasets provided. For each omic dataset, a differential network is constructed where links represent statistically significant differential interactions between entities. These networks are then integrated into a comprehensive visualization using distinct colors to distinguish interactions from different omic layers. This unified display allows interactive exploration of cross-omic patterns, such as differential interactions present at both transcript and protein levels. For each link, users can access differential statistical significance metrics (p values or adjusted p values, calculated via robust or traditional linear regression with interaction term) and differential regression plots. The methods implemented in this package are described in Sciacca et al. (2023) <doi:10.1093/bioinformatics/btad192>.

Calculates exact hypothesis tests to compare a treatment and a reference group with respect to multiple binary endpoints. The tested null hypothesis is an identical multidimensional distribution of successes and failures in both groups. The alternative hypothesis is a larger success proportion in the treatment group in at least one endpoint. The tests are based on the multivariate permutation distribution of subjects between the two groups. For this permutation distribution, rejection regions are calculated that satisfy one of different possible optimization criteria. In particular, regions with maximal exhaustion of the nominal significance level, maximal power under a specified alternative or maximal number of elements can be found. Optimization is achieved by a branch-and-bound algorithm. By application of the closed testing principle, the global hypothesis tests are extended to multiple testing procedures.

Fast randomization based two sample tests. Testing the hypothesis that two samples come from the same distribution using randomization to create p-values. Included tests are: Kolmogorov-Smirnov, Kuiper, Cramer-von Mises, Anderson-Darling, Wasserstein, and DTS. The default test (two_sample) is based on the DTS test statistic, as it is the most powerful, and thus most useful to most users. The DTS test statistic builds on the Wasserstein distance by using a weighting scheme like that of Anderson-Darling. See the companion paper at <arXiv:2007.01360> or <https://codowd.com/public/DTS.pdf> for details of that test statistic, and non-standard uses of the package (parallel for big N, weighted observations, one sample tests, etc). We also include the permutation scheme to make test building simple for others.

The package encompasses functions to find potential guide RNAs for the CRISPR-based genome-editing systems including the Base Editors and the Prime Editors when supplied with target sequences as input. Users have the flexibility to filter resulting guide RNAs based on parameters such as the absence of restriction enzyme cut sites or the lack of paired guide RNAs. The package also facilitates genome-wide exploration for off-targets, offering features to score and rank off-targets, retrieve flanking sequences, and indicate whether the hits are located within exon regions. All detected guide RNAs are annotated with the cumulative scores of the top5 and topN off-targets together with the detailed information such as mismatch sites and restrictuion enzyme cut sites. The package also outputs INDELs and their frequencies for Cas9 targeted sites.

flowcatchR is a set of tools to analyze in vivo microscopy imaging data, focused on tracking flowing blood cells. It guides the steps from segmentation to calculation of features, filtering out particles not of interest, providing also a set of utilities to help checking the quality of the performed operations (e.g. how good the segmentation was). It allows investigating the issue of tracking flowing cells such as in blood vessels, to categorize the particles in flowing, rolling and adherent. This classification is applied in the study of phenomena such as hemostasis and study of thrombosis development. Moreover, flowcatchR presents an integrated workflow solution, based on the integration with a Shiny App and Jupyter notebooks, which is delivered alongside the package, and can enable fully reproducible bioimage analysis in the R environment.

This package provides a compilation of tests for hypotheses regarding covariance and correlation matrices for one or more groups. The hypothesis can be specified through a corresponding hypothesis matrix and a vector or by choosing one of the basic hypotheses, while for the structure test, only the latter works. Thereby Monte-Carlo and Bootstrap-techniques are used, and the respective method must be chosen, and the functions provide p-values and mostly also estimators of calculated covariance matrices of test statistics. For more details on the methodology, see Sattler et al. (2022) <doi:10.1016/j.jspi.2021.12.001>, Sattler and Pauly (2024) <doi:10.1007/s11749-023-00906-6>, Sattler and Dobler (2026) <doi:10.1016/j.jmva.2025.105517>, and Sattler and Jedhoff (2025) <doi:10.48550/arXiv.2507.03406>.

In the context of paid research studies and clinical trials, budget considerations and patient sampling from available populations are subject to inherent constraints. We introduce the CDsampling package, which integrates optimal design theories within the framework of constrained sampling. This package offers the possibility to find both D-optimal approximate and exact allocations for samplings with or without constraints. Additionally, it provides functions to find constrained uniform sampling as a robust sampling strategy with limited model information. Our package offers functions for the computation of the Fisher information matrix under generalized linear models (including regular linear regression model) and multinomial logistic models.To demonstrate the applications, we also provide a simulated dataset and a real dataset embedded in the package. Yifei Huang, Liping Tong, and Jie Yang (2025)<doi:10.5705/ss.202022.0414>.

This package provides tools for visualization of, and inference on, the calibration of prediction models on the cumulative domain. This provides a method for evaluating calibration of risk prediction models without having to group the data or use tuning parameters (e.g., loess bandwidth). This package implements the methodology described in Sadatsafavi and Petkau (2024) <doi:10.1002/sim.10138>. The core of the package is cumulcalib(), which takes in vectors of binary responses and predicted risks. The package also implements non-parametric assessment of the calibration of individualized treatment effect (ITE) models using data from a randomized trial, via cumulcalibITE(), as described in Sadatsafavi et al. (2026) <doi:10.1002/sim.70724>. The plot() and summary() methods are implemented for the results returned by cumulcalib() and cumulcalibITE().

This package provides a decorator is a function that receives a function, extends its behaviour, and returned the altered function. Any caller that uses the decorated function uses the same interface as it were the original, undecorated function. Decorators serve two primary uses: (1) Enhancing the response of a function as it sends data to a second component; (2) Supporting multiple optional behaviours. An example of the first use is a timer decorator that runs a function, outputs its execution time on the console, and returns the original function's result. An example of the second use is input type validation decorator that during running time tests whether the caller has passed input arguments of a particular class. Decorators can reduce execution time, say by memoization, or reduce bugs by adding defensive programming routines.

This package implements the dynamically weighted modified maximum likelihood ridge (DWMMLR) regression estimator, a robust and multicollinearity-aware linear regression estimator that combines the DWMML3 weighting procedure of Sazak (2019) <doi:10.1080/00949655.2019.1571060> with ridge penalization to address both outlier sensitivity and variance inflation due to multicollinearity. The ridge parameter is selected automatically using the approach implemented in the ridgregextra package (Karadag, Sazak, and Aydin, 2023) <https://CRAN.R-project.org/package=ridgregextra>, described further in Karadag, Sazak, and Aydin (2026) <doi:10.1080/02664763.2026.2655681>, which targets a variance inflation factor (VIF) close to but not below 1, removing the need for manual tuning. Returns comprehensive outputs (coefficients, fitted values, residuals, mean squared error (MSE), standard errors, R-squared, and adjusted R-squared) through a simple x/y interface.

This package provides tools for measuring income and wealth inequality. Computes the Gini coefficient with bootstrap or asymptotic confidence intervals following Davidson (2009) <doi:10.1016/j.jeconom.2008.11.004>, the extended S-Gini family, Theil T and L indices (generalised entropy family), the Atkinson index, the Kolm absolute inequality index, Palma ratio, Hoover index, percentile ratios, and Lorenz curves. Supports between-within group decomposition following Bourguignon (1979) <doi:10.2307/1914138>, income share tabulation, concentration indices for health inequality with Erreygers (2009) correction, Kakwani tax progressivity and Reynolds-Smolensky redistribution indices, Foster-Greer-Thorbecke poverty measures including the Sen index, growth incidence curves following Ravallion and Chen (2003) <doi:10.1016/S0165-1765(02)00205-7>, and Wolfson polarisation. All functions accept optional survey weights and work with data from any source.

Systematic reviews should be described in a high degree of methodological detail. The PRISMA Statement calls for a high level of reporting detail in systematic reviews and meta-analyses. An integral part of the methodological description of a review is a flow diagram. This package produces an interactive flow diagram that conforms to the PRISMA2020 preprint. When made interactive, the reader/user can click on each box and be directed to another website or file online (e.g. a detailed description of the screening methods, or a list of excluded full texts), with a mouse-over tool tip that describes the information linked to in more detail. Interactive versions can be saved as HTML files, whilst static versions for inclusion in manuscripts can be saved as HTML, PDF, PNG, SVG, PS or WEBP files.

Rdiff-backup backs up one directory to another, possibly over a network. The target directory ends up a copy of the source directory, but extra reverse diffs are stored in a special subdirectory of that target directory, so you can still recover files lost some time ago. The idea is to combine the best features of a mirror and an incremental backup. Rdiff-backup also preserves subdirectories, hard links, dev files, permissions, uid/gid ownership, modification times, extended attributes, acls, and resource forks. Also, rdiff-backup can operate in a bandwidth efficient manner over a pipe, like rsync. Thus you can use rdiff-backup and ssh to securely back a hard drive up to a remote location, and only the differences will be transmitted. Finally, rdiff-backup is easy to use and settings have sensible defaults.

This package provides several functions to explore miRNA sponge (also called ceRNA or miRNA decoy) regulation from putative miRNA-target interactions or/and transcriptomics data (including bulk, single-cell and spatial gene expression data). It provides eight popular methods for identifying miRNA sponge interactions, and an integrative method to integrate miRNA sponge interactions from different methods, as well as the functions to validate miRNA sponge interactions, and infer miRNA sponge modules, conduct enrichment analysis of miRNA sponge modules, and conduct survival analysis of miRNA sponge modules. By using a sample control variable strategy, it provides a function to infer sample-specific miRNA sponge interactions. In terms of sample-specific miRNA sponge interactions, it implements three similarity methods to construct sample-sample correlation network.

Maps individual tree stem locations on field plots of the Forest Inventory and Analysis Program of USDA Forest Service (<https://research.fs.usda.gov/programs/nfi>). Stem locations are mapped in cartesian coordinate space based on field-measured distance and azimuth from subplot and microplot centers. Per-tree crown widths are estimated using a curated set of allometric equations with coverage for the conterminous US. Spatial descriptors of tree point pattern are computed at the whole plot level. Several stand height metrics are also computed and provided in the output. The spatial representation of modeled tree crowns is used to generate estimates of fractional tree canopy cover at the microplot, subplot and whole plot levels. Convenience functions are provided for efficient data processing. Exploratory data analysis is also facilitated via integration with the spatstat packages.

Implementation of a probabilistic method to calculate nicheROVER (_niche_ _r_egion and niche _over_lap) metrics using multidimensional niche indicator data (e.g., stable isotopes, environmental variables, etc.). The niche region is defined as the joint probability density function of the multidimensional niche indicators at a user-defined probability alpha (e.g., 95%). Uncertainty is accounted for in a Bayesian framework, and the method can be extended to three or more indicator dimensions. It provides directional estimates of niche overlap, accounts for species-specific distributions in multivariate niche space, and produces unique and consistent bivariate projections of the multivariate niche region. The article by Swanson et al. (2015) <doi:10.1890/14-0235.1> provides a detailed description of the methodology. See the package vignette for a worked example using fish stable isotope data.

This package provides a function to calculate multiple performance metrics for actual and predicted values. In total eight metrics will be calculated for particular actual and predicted series. Helps to describe a Statistical model's performance in predicting a data. Also helps to compare various models performance. The metrics are Root Mean Squared Error (RMSE), Relative Root Mean Squared Error (RRMSE), Mean absolute Error (MAE), Mean absolute percentage error (MAPE), Mean Absolute Scaled Error (MASE), Nash-Sutcliffe Efficiency (NSE), Willmottâ s Index (WI), and Legates and McCabe Index (LME). Among them, first five are expected to be lesser whereas, the last three are greater the better. More details can be found from Garai and Paul (2023) <doi:10.1016/j.iswa.2023.200202> and Garai et al. (2024) <doi:10.1007/s11063-024-11552-w>.

Procedures for simulating biomes by equilibrium vegetation models, with a special focus on paleoenvironmental applications. Three widely used equilibrium biome models are currently implemented in the package: the Holdridge Life Zone (HLZ) system (Holdridge 1947, <doi:10.1126/science.105.2727.367>), the Köppen-Geiger classification (KGC) system (Köppen 1936, <https://koeppen-geiger.vu-wien.ac.at/pdf/Koppen_1936.pdf>) and the BIOME model (Prentice et al. 1992, <doi:10.2307/2845499>). Three climatic forest-steppe models are also implemented. An approach for estimating monthly time series of relative sunshine duration from temperature and precipitation data (Yin 1999, <doi:10.1007/s007040050111>) is also adapted, allowing process-based biome models to be combined with high-resolution paleoclimate simulation datasets (e.g., CHELSA-TraCE21k v1.0 dataset: <https://chelsa-climate.org/chelsa-trace21k/>).

This package implements the bootstrap-calibrated local-influence goodness-of-fit test for simplex regression models with constant or varying dispersion, following the local influence approach of Zhu and Zhang (2004) <doi:10.1093/biomet/91.3.579> and the simplex regression model of Barndorff-Nielsen and Jorgensen (1991) <doi:10.1016/0047-259X(91)90008-P>. The test statistic aggregates individual local-influence measures under case-weight perturbation. Because the first-order asymptotic normal calibration is severely liberal in finite samples, a parametric bootstrap calibration is provided that restores accurate size control and delivers high power against omitted covariates, neglected dispersion, and distributional misspecification. Plotting functions reproduce the figures and tables of the companion methodological paper. Computational kernels are implemented in C++ via Rcpp and RcppArmadillo for speed, and two real datasets are bundled.

It computes two frequently applied actuarial measures, the expected shortfall and the value at risk. Seven well-known classical distributions in connection to the Bell generalized family are used as follows: Bell-exponential distribution, Bell-extended exponential distribution, Bell-Weibull distribution, Bell-extended Weibull distribution, Bell-Lomax distribution, Bell-Burr-12 distribution, and Bell-Burr-X distribution. Related works include: a) Fayomi, A., Tahir, M. H., Algarni, A., Imran, M., & Jamal, F. (2022). "A new useful exponential model with applications to quality control and actuarial data". Computational Intelligence and Neuroscience, 2022. <doi:10.1155/2022/2489998>. b) Alsadat, N., Imran, M., Tahir, M. H., Jamal, F., Ahmad, H., & Elgarhy, M. (2023). "Compounded Bell-G class of statistical models with applications to COVID-19 and actuarial data". Open Physics, 21(1), 20220242. <doi:10.1515/phys-2022-0242>.

Fits area-level geoadditive small area estimation (SAE) models by extending the Fay-Herriot area-level model with linear, nonlinear, and spatial effects. The Fay-Herriot model is described by Fay and Herriot (1979) <doi:10.1080/01621459.1979.10482505>. Geoadditive models combine nonlinear covariate effects and spatial variation as described by Kammann and Wand (2003) <doi:10.1111/1467-9876.00385>, while their application to small area estimation is discussed by Pusponegoro et al. (2019) <doi:10.21108/JDSA.2019.2.15>. Nonlinear covariate effects are represented using penalized splines, while spatial effects are represented using a smooth function of geographic coordinates. Models are estimated using restricted maximum likelihood (REML), and mean squared error (MSE) is estimated using a parametric bootstrap. The package also provides comparisons with the Fay-Herriot and spatial Fay-Herriot (SFH) models.

Tests for context-dependent selection on cancer driver genes using somatic mutation data. The package implements the DiffDriver statistical framework to assess whether the strength of positive selection on mutations in a driver gene is associated with tumor- or individual-level context variables, such as clinical traits, genomic features, or immune microenvironment subtypes. DiffDriver estimates individual- and position-specific background mutation rates, models selection as a deviation from the background rate using functional annotations, and tests context effects through a latent-variable logistic model. It provides utilities for preparing mutation and annotation data, fitting differential-selection models, running gene-level association tests, summarizing candidate genes, and visualizing mutation patterns. The method is described in Zhou et al. (2026) "Detecting context-dependent selection on cancer driver genes with DiffDriver" <doi:10.64898/2026.04.06.716771>.

Genie is a robust hierarchical clustering algorithm (Gagolewski, Bartoszuk, Cena, 2016 <DOI:10.1016/j.ins.2016.05.003>). genieclust is its faster, more capable implementation (Gagolewski, 2021 <DOI:10.1016/j.softx.2021.100722>). It enables clustering with respect to mutual reachability distances, allowing it to act as an alternative to HDBSCAN* that can identify any number of clusters or their entire hierarchy. When combined with the deadwood package, it can act as an outlier detector. Additional package features include the Gini and Bonferroni inequality indices, external cluster validity measures (e.g., the normalised clustering accuracy, the adjusted Rand index, the Fowlkes-Mallows index, and normalised mutual information), and internal cluster validity indices (e.g., the Calinski-Harabasz, Davies-Bouldin, Ball-Hall, Silhouette, and generalised Dunn indices). The Python version of genieclust is available via PyPI'.