Generate the optimal Latin Hypercube Designs (LHDs) for computer experiments with quantitative factors and the optimal Sliced Latin Hypercube Designs (SLHDs) for computer experiments with both quantitative and qualitative factors. Details of the algorithm can be found in Ba, S., Brenneman, W. A. and Myers, W. R. (2015), "Optimal Sliced Latin Hypercube Designs," Technometrics. Important function in this package is "maximinSLHD".

Imbibition causes seeds to expand, which results in the seed coat or testa being broken. Seed germination begins with imbibition. Imbibition aids in the transport of water into the developing ovules. Imbibition is required during the first stages of root water absorption.

Obtain parameters of Svensson's Method, including percentage agreement, systematic change and individual change. Also, the contingency table can be generated. Svensson's Method is a rank-invariant nonparametric method for the analysis of ordered scales which measures the level of change both from systematic and individual aspects. For the details, please refer to Svensson E. Analysis of systematic and random differences between paired ordinal categorical data [dissertation]. Stockholm: Almqvist & Wiksell International; 1993.

This package provides a pair of Shiny modules that let end users of a Shiny application author their own R functions directly in the browser. Host apps can expose these modules as extension points where user-supplied code augments or replaces built-in logic, without requiring users to modify the app's source. Each module embeds an Ace editor with a structured argument table, an in-frame R console rooted in the paused function's local environment, and a step debugger that handles for, while, repeat, and if/else blocks at any nesting depth. Two module flavours are provided: solo editors for testing a function in isolation with literal argument values, and embedded editors for pausing a function mid-execution inside a larger host program.

Extends the SelectBoost approach to Generalized Additive Models for Location, Scale and Shape (GAMLSS). Implements bootstrap stability-selection across parameter-specific formulas (mu, sigma, nu, tau) via gamlss::stepGAIC(). Includes optional standardization of predictors and helper functions for corrected AIC calculation. More details can be found in Bertrand and Maumy (2024) <https://hal.science/hal-05352041> that highlights correlation-aware resampling to improve variable selection for GAMLSS and quantile regression when predictors are numerous and highly correlated.

Add indicators (spinner, progress bar, gif) in your shiny applications to show the user that the server is busy. And other tools to let your users know something is happening (send notifications, reports, ...).

This package provides a seamless design that combines phase I dose escalation based on toxicity with phase II dose expansion and dose comparison based on efficacy.

This package provides most of the data files used in the textbook "Scientific Research and Methodology" by Dunn (2025, ISBN: 9781032496726).

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.

Take real or simulated data and salt it with errors commonly found in the wild, such as pseudo-OCR errors, Unicode problems, numeric fields with nonsensical punctuation, bad dates, etc.

This package implements the Shimazaki-Shinomoto method for optimizing the bin width of a histogram. This method minimizes the mean integrated squared error (MISE) and features a C++ backend for high performance and shift-averaging to remove edge-position bias. Ideally suits for time-dependent rate estimation and identifying intrinsic data structures. Supports both 1D and 2D data distributions. For more details see Shimazaki and Shinomoto (2007) "A Method for Selecting the Bin Size of a Time Histogram" <doi:10.1162/neco.2007.19.6.1503>.

This package provides methods to calculate sample size for single-arm survival studies using the arcsine transformation, incorporating uniform accrual and exponential survival assumptions. Includes functionality for detailed numerical integration and simulation. This method is based on Nagashima et al. (2021) <doi:10.1002/pst.2090>.

This package provides indices and tools for directed acyclic graphs (DAGs), particularly DAG representations of intermittent streams. A detailed introduction to the package can be found in the publication: "Non-perennial stream networks as directed acyclic graphs: The R-package streamDAG" (Aho et al., 2023) <doi:10.1016/j.envsoft.2023.105775>, and in the introductory package vignette.

This package implements a two-stage estimation approach for Cox regression using five-parameter M-spline functions to model the baseline hazard. It allows for flexible hazard shapes and model selection based on log-likelihood criteria as described in Teranishi et al.(2025). In addition, the package provides functions for constructing and evaluating B-spline copulas based on five M-spline or I-spline basis functions, allowing users to flexibly model and compute bivariate dependence structures. Both the copula function and its density can be evaluated. Furthermore, the package supports computation of dependence measures such as Kendall's tau and Spearman's rho, derived analytically from the copula parameters.

Algorithms to compute spherical k-means partitions. Features several methods, including a genetic and a fixed-point algorithm and an interface to the CLUTO vcluster program.

This package implements several methods to estimate effects of generalized time-varying treatment strategies on the mean of an outcome at one or more selected follow-up times of interest. Specifically, the package implements the time-smoothed inverse probability weighted estimators described in McGrath et al. (2025) <doi:10.48550/arXiv.2509.13971>. Outcomes may be repeatedly, non-monotonically, informatively, and sparsely measured in the data source. The package also supports settings where outcomes are truncated by death, i.e. some individuals die during follow-up which renders the outcome of interest undefined at the follow-up time of interest.

Use R and SAS within reproducible multilingual quarto documents. Run SAS code blocks interactively, send data back and forth between SAS and R, and render SAS output within quarto documents. SAS connections are established through a combination of SASPy and reticulate'.

Includes an interactive application designed to support educators in wide-ranging disciplines, with a particular focus on those teaching introductory statistical methods (descriptive and/or inferential) for data analysis. Users are able to randomly generate data, make new versions of existing data through common adjustments (e.g., add random normal noise and perform transformations), and check the suitability of the resulting data for statistical analyses.

This package provides image-related C/C++ header files from the stb single-file libraries for image loading, writing, and resizing.

Analysis of metacommunities based on functional traits and phylogeny of the community components. The functions that are offered here implement for the R environment methods that have been available in the SYNCSA application written in C++ (by Valerio Pillar, available at <http://ecoqua.ecologia.ufrgs.br/SYNCSA.html>).

To automated functional annotation of genetic variants and linked proxies. Linked SNPs in moderate to high linkage disequilibrium (e.g. r2>0.50) with the corresponding index SNPs will be selected for further analysis.

Small area estimation unit level models (Battese-Harter-Fuller model) with a Bayesian Hierarchical approach. See also Rao & Molina (2015, ISBN:978-1-118-73578-7) and Battese et al. (1988) <doi:10.1080/01621459.1988.10478561>.

This package provides functions to visually and statistically analyze single system data.

An implementation of ranked sparsity methods, including penalized regression methods such as the sparsity-ranked lasso, its non-convex alternatives, and elastic net, as well as the sparsity-ranked Bayesian Information Criterion. As described in Peterson and Cavanaugh (2022) <doi:10.1007/s10182-021-00431-7>, ranked sparsity is a philosophy with methods primarily useful for variable selection in the presence of prior informational asymmetry, which occurs in the context of trying to perform variable selection in the presence of interactions and/or polynomials. Ultimately, this package attempts to facilitate dealing with cumbersome interactions and polynomials while not avoiding them entirely. Typically, models selected under ranked sparsity principles will also be more transparent, having fewer falsely selected interactions and polynomials than other methods.