This package provides the density, distribution, quantile and generation functions of some obscure probability distributions, including the doubly non-central t, F, Beta, and Eta distributions; the lambda-prime and K-prime; the upsilon distribution; the (weighted) sum of non-central chi-squares to a power; the (weighted) sum of log non-central chi-squares; the product of non-central chi-squares to powers; the product of doubly non-central F variables; the product of independent normals.

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.

Gives design points from a sequential full factorial-based Latin hypercube design, as described in Duan, Ankenman, Sanchez, and Sanchez (2015, Technometrics, <doi:10.1080/00401706.2015.1108233>).

This package provides functions and utilities to perform Statistical Analyses in the Six Sigma way. Through the DMAIC cycle (Define, Measure, Analyze, Improve, Control), you can manage several Quality Management studies: Gage R&R, Capability Analysis, Control Charts, Loss Function Analysis, etc. Data frames used in the books "Six Sigma with R" [ISBN 978-1-4614-3652-2] and "Quality Control with R" [ISBN 978-3-319-24046-6], are also included in the package.

This package provides a simple method to display and characterise the multidimensional ecological niche of a species. The method also estimates the optimums and amplitudes along each niche dimension. Give also an estimation of the degree of niche overlapping between species. See Kleparski and Beaugrand (2022) <doi:10.1002/ece3.8830> for further details.

Model stacking is an ensemble technique that involves training a model to combine the outputs of many diverse statistical models, and has been shown to improve predictive performance in a variety of settings. stacks implements a grammar for tidymodels'-aligned model stacking.

This package provides a collection of tools to access prepared air quality monitoring data files from web servers with ease and speed. Air quality data are sourced from open and publicly accessible repositories and can be found in these locations: <https://www.eea.europa.eu/data-and-maps/data/airbase-the-european-air-quality-database-8> and <https://discomap.eea.europa.eu/map/fme/AirQualityExportAirbase.htm>. The web server space has been provided by Ricardo Energy & Environment.

Vignettes for the survival package. Split from the survival package since the vignettes were getting large. Also, since survival is a recommended package it cannot make use of other packages outside of base+recommended (e.g. rmarkdown').

Maximum-likelihood estimation of the spatial autoregressive stochastic frontier model with individual fixed effects (removed by first differencing) and endogenous regressors handled through a Gaussian control function, as in Giannini (2025) <doi:10.1080/17421772.2024.2414962>. Returns coefficient estimates and standard errors, spatially-corrected technical (in)efficiency scores, and direct, indirect and total marginal impacts. Depends only on base R.

The developed function is designed for the generation of spatial grids based on user-specified longitude and latitude coordinates. The function first validates the input longitude and latitude values, ensuring they fall within the appropriate geographic ranges. It then creates a polygon from the coordinates and determines the appropriate Universal Transverse Mercator zone based on the provided hemisphere and longitude values. Subsequently, transforming the input Shapefile to the Universal Transverse Mercator projection when necessary. Finally, a spatial grid is generated with the specified interval and saved as a Shapefile. For method details see, Brus,D.J.(2022).<DOI:10.1201/9781003258940>. The function takes into account crucial parameters such as the hemisphere (north or south), desired grid interval, and the output Shapefile path. The developed function is an efficient tool, simplifying the process of empty spatial grid generation for applications such as, geo-statistical analysis, digital soil mapping product generation, etc. Whether for environmental studies, urban planning, or any other geo-spatial analysis, this package caters to the diverse needs of users working with spatial data, enhancing the accessibility and ease of spatial data processing and visualization.

Interface to the sTiles framework for tile-based sparse Cholesky factorization: log-determinants, selected inverse (marginal variances) and triangular solves, with symbolic reuse so that repeated factorization of matrices sharing one sparsity pattern pays the ordering cost only once, as in a hyperparameter sweep. The compiled glue in this package resolves its symbols at run time against the sTiles solver library ('libstiles'), which is a separate component distributed under its own terms and is not part of this package. Install it once with sTiles_install_library()', or point the package at a copy you already have with the STILES_LIB environment variable.

This package provides a modification of the preventive vaccine efficacy trial design of Gilbert, Grove et al. (2011, Statistical Communications in Infectious Diseases) is implemented, with application generally to individual-randomized clinical trials with multiple active treatment groups and a shared control group, and a study endpoint that is a time-to-event endpoint subject to right-censoring. The design accounts for the issues that the efficacy of the treatment/vaccine groups may take time to accrue while the multiple treatment administrations/vaccinations are given; there is interest in assessing the durability of treatment efficacy over time; and group sequential monitoring of each treatment group for potential harm, non-efficacy/efficacy futility, and high efficacy is warranted. The design divides the trial into two stages of time periods, where each treatment is first evaluated for efficacy in the first stage of follow-up, and, if and only if it shows significant treatment efficacy in stage one, it is evaluated for longer-term durability of efficacy in stage two. The package produces plots and tables describing operating characteristics of a specified design including an unconditional power for intention-to-treat and per-protocol/as-treated analyses; trial duration; probabilities of the different possible trial monitoring outcomes (e.g., stopping early for non-efficacy); unconditional power for comparing treatment efficacies; and distributions of numbers of endpoint events occurring after the treatments/vaccinations are given, useful as input parameters for the design of studies of the association of biomarkers with a clinical outcome (surrogate endpoint problem). The code can be used for a single active treatment versus control design and for a single-stage design.

This package provides a collection of functions for processing raw data from Stream Temperature, Intermittency, and Conductivity (STIC) loggers. STICr (pronounced "sticker") includes functions for tidying, calibrating, classifying, and doing quality checks on data from STIC sensors. Some package functionality is described in Wheeler/Zipper et al. (2023) <doi:10.31223/X5636K>.

Setwise Hierarchical Rate of Erroneous Discovery (SHRED) methods for setwise variable selection with false discovery rate (FDR) control. Setwise variable selection means that sets of variables may be selected when the true variable cannot be identified. This allows us to maintain FDR control but increase power. Details of the SHRED methods are in Organ, Kenney & Gu (2026) <doi:10.48550/arXiv.2603.02160>.

This package provides dplyr and tidyr verbs, survey-aware recoding helpers, and row-wise statistics for survey design objects created with the surveycore package. filter() uses domain estimation to preserve variance estimation validity; other verbs preserve design variables and metadata automatically. Also supports survey_collection objects for applying the same operation across a list of surveys.

Set of tools to fit a linear multiple or semi-parametric regression models with the possibility of non-informative random right or left censoring. Under this setup, the localization parameter of the response variable distribution is modeled by using linear multiple regression or semi-parametric functions, whose non-parametric components may be approximated by natural cubic spline or P-splines. The supported distribution for the model error is a generalized log-gamma distribution which includes the generalized extreme value and standard normal distributions as important special cases. Inference is based on likelihood, penalized likelihood and bootstrap methods. Lastly, some numerical and graphical devices for diagnostic of the fitted models are offered.

Data wrangling and cleaning, quality control checks and implementation of machine learning classification algorithm.

Description: Provides functions for simulation and inference for stochastic differential equations (SDEs). It accompanies the book "Simulation and Inference for Stochastic Differential Equations: With R Examples" (Iacus, 2008, Springer; ISBN: 978-0-387-75838-1).

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

This package provides functions and classes for spatial resampling to use with the rsample package, such as spatial cross-validation (Brenning, 2012) <doi:10.1109/IGARSS.2012.6352393>. The scope of rsample and spatialsample is to provide the basic building blocks for creating and analyzing resamples of a spatial data set, but neither package includes functions for modeling or computing statistics. The resampled spatial data sets created by spatialsample do not contain much overhead in memory.

This package provides functions to test for a treatment effect in terms of the difference in survival between a treatment group and a control group using surrogate marker information obtained at some early time point in a time-to-event outcome setting. Nonparametric kernel estimation is used to estimate the test statistic and perturbation resampling is used for variance estimation. More details will be available in the future in: Parast L, Cai T, Tian L (2019) ``Using a Surrogate Marker for Early Testing of a Treatment Effect" Biometrics, 75(4):1253-1263. <doi:10.1111/biom.13067>.

This package provides tools for simulating, analysing, and fitting mobility-based SEAIR (Susceptible-Exposed-Asymptomatic-Infectious-Recovered) compartmental epidemic models with heterogeneous individual mobility. Each individual carries a fixed mobility trait that scales susceptibility and infectiousness through a rank-one kernel, extending the mobility-based compartmental framework of Jiang et al. (2025) <doi:10.1137/24M1691557> by adding a latent stage and a behavioural split between asymptomatic and symptomatic infectiousness. Provides a numerical solver for the underlying partial differential equation system, closed-form computation of the basic reproduction number R0 and the final epidemic size, and a parametric least-squares routine for recovering the mobility distribution from an observed aggregate symptomatic time series.

This package provides interface to sparsepp - fast, memory efficient hash map. It is derived from Google's excellent sparsehash implementation. We believe sparsepp provides an unparalleled combination of performance and memory usage, and will outperform your compiler's unordered_map on both counts. Only Google's dense_hash_map is consistently faster, at the cost of much greater memory usage (especially when the final size of the map is not known in advance).

This package provides the filtering algorithms for the state space models on the Stiefel manifold as well as the corresponding sampling algorithms for uniform, vector Langevin-Bingham and matrix Langevin-Bingham distributions on the Stiefel manifold.