Identifies constant, additive, multiplicative, and user-defined simplivariate components in numeric data matrices using a genetic algorithm. Supports flexible pattern definitions and provides visualization for general biclustering applications across diverse domains. The method builds on simplivariate models as introduced in Hageman et al. (2008) <doi:10.1371/journal.pone.0003259> and is related to biclustering frameworks as reviewed by Madeira and Oliveira (2004) <doi:10.1109/TCBB.2004.2>.

The sparse vector field consensus (SparseVFC) algorithm (Ma et al., 2013 <doi:10.1016/j.patcog.2013.05.017>) for robust vector field learning. Largely translated from the Matlab functions in <https://github.com/jiayi-ma/VFC>.

Easily create pretty popup messages (modals) in Shiny'. A modal can contain text, images, OK/Cancel buttons, an input to get a response from the user, and many more customizable options.

This package implements the algorithm described in Guo, H., and Li, J., "scSorter: assigning cells to known cell types according to known marker genes". Cluster cells to known cell types based on marker genes specified for each cell type.

Applies re-sampled kernel density method to detect vote fraud. It estimates the proportion of coarse vote-shares in the observed data relative to the null hypothesis of no fraud.

Automatically calculate direct, indirect, and total effects for piecewise structural equation models, comprising lists of fitted models representing structured equations (Lefcheck, 2016 <doi:10/f8s8rb>). Confidence intervals are provided via bootstrapping.

Dictionary-like reference for computing scoring rules in a wide range of situations. Covers both parametric forecast distributions (such as mixtures of Gaussians) and distributions generated via simulation. Further details can be found in the package vignettes <doi:10.18637/jss.v090.i12>, <doi:10.18637/jss.v110.i08>.

Fitting and plotting parametric or non-parametric size-biased non-negative distributions, with optional covariates if parametric. Rowcliffe, M. et al. (2016) <doi:10.1002/rse2.17>.

This package implements algorithms for terrestrial, mobile, and airborne lidar processing, tree detection, segmentation, and attribute estimation (Donager et al., 2021) <doi:10.3390/rs13122297>, and a hierarchical patch delineation algorithm PatchMorph (Girvetz & Greco, 2007) <doi:10.1007/s10980-007-9104-8>. Tree detection uses rasterized point cloud metrics (relative neighborhood density and verticality) combined with RANSAC cylinder fitting to locate tree boles and estimate diameter at breast height. Tree segmentation applies graph-theory approaches inspired by Tao et al. (2015) <doi:10.1016/j.isprsjprs.2015.08.007> with cylinder fitting methods from de Conto et al. (2017) <doi:10.1016/j.compag.2017.07.019>. PatchMorph delineates habitat patches across spatial scales using organism-specific thresholds. Built on lidR (Roussel et al., 2020) <doi:10.1016/j.rse.2020.112061>.

This package provides a comprehensive framework for quantifying the fundamental thermodynamic parameters of adsorption reactionsâ changes in the standard Gibbs free energy (delta G), enthalpy (delta H), and entropy (delta S)â is essential for understanding the spontaneity, heat effects, and molecular ordering associated with sorption processes. By analysing temperature-dependent equilibrium data, thermodynamic interpretation expands adsorption studies beyond conventional isotherm fitting, offering deeper insight into underlying mechanisms and surfaceâ solute interactions. Such an approach typically involves evaluating equilibrium coefficients across multiple temperatures and non-temperature treatments, deriving thermodynamic parameters using established thermodynamic relationships, and determining delta G as a temperature-specific indicator of adsorption favourability. This analytical pathway is widely applicable across environmental science, soil science, chemistry, materials science, and engineering, where reliable assessment of sorption behaviour is critical for examining contaminant retention, nutrient dynamics, and the behaviour of natural and engineered surfaces. By focusing specifically on thermodynamic inference, this framework complements existing adsorption isotherm-fitting packages such as âAdIsMFâ <https://CRAN.R-project.org/package=AdIsMF> <doi:10.32614/CRAN.package.AdIsMF>, and strengthens the scientific basis for interpreting adsorption energetics in both research and applied contexts. Details can be found in Roy et al. (2025) <doi:10.1007/s11270-025-07963-7>.

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

Enables the ability to change or flash the title of the browser window during a shiny session.

Implementation of Sparse-group SLOPE (SGS) (Feser and Evangelou (2023) <doi:10.48550/arXiv.2305.09467>) models. Linear and logistic regression models are supported, both of which can be fit using k-fold cross-validation. Dense and sparse input matrices are supported. In addition, a general Adaptive Three Operator Splitting (ATOS) (Pedregosa and Gidel (2018) <doi:10.48550/arXiv.1804.02339>) implementation is provided. Group SLOPE (gSLOPE) (Brzyski et al. (2019) <doi:10.1080/01621459.2017.1411269>) and group-based OSCAR models (Feser and Evangelou (2024) <doi:10.48550/arXiv.2405.15357>) are also implemented. All models are available with strong screening rules (Feser and Evangelou (2024) <doi:10.48550/arXiv.2405.15357>) for computational speed-up.

Simple implementation of Semantic Versioning 2.0.0 ('SemVer') on the vctrs package. This package provides a simple way to create, compare, and manipulate semantic versions in R. It is designed to be lightweight and easy to use.

Simplicially constrained regression models for proportions in both sides. The constraint is always that the betas are non-negative and sum to 1. References: Iverson S.J.., Field C., Bowen W.D. and Blanchard W. (2004) "Quantitative Fatty Acid Signature Analysis: A New Method of Estimating Predator Diets". Ecological Monographs, 74(2): 211-235. <doi:10.1890/02-4105>.

Calculates graph theoretic scagnostics. Scagnostics describe various measures of interest for pairs of variables, based on their appearance on a scatterplot. They are useful tool for discovering interesting or unusual scatterplots from a scatterplot matrix, without having to look at every individual plot.

This package implements a suite of sensitivity analysis tools that extends the traditional omitted variable bias framework and makes it easier to understand the impact of omitted variables in regression models, as discussed in Cinelli, C. and Hazlett, C. (2020), "Making Sense of Sensitivity: Extending Omitted Variable Bias." Journal of the Royal Statistical Society, Series B (Statistical Methodology) <doi:10.1111/rssb.12348>.

This package provides a set of functions that can be used to spatially thin species occurrence data. The resulting thinned data can be used in ecological modeling, such as ecological niche modeling.

Efficient Markov chain Monte Carlo (MCMC) algorithms for fully Bayesian estimation of time-varying parameter models with shrinkage priors, both dynamic and static. Details on the algorithms used are provided in Bitto and Frühwirth-Schnatter (2019) <doi:10.1016/j.jeconom.2018.11.006> and Cadonna et al. (2020) <doi:10.3390/econometrics8020020> and Knaus and Frühwirth-Schnatter (2023) <doi:10.48550/arXiv.2312.10487>. For details on the package, please see Knaus et al. (2021) <doi:10.18637/jss.v100.i13>. For the multivariate extension, see the shrinkTVPVAR package.

Evaluating the consistency assumption of Network Meta-Analysis both globally and locally in the Bayesian framework. Inconsistencies are located by applying Bayesian variable selection to the inconsistency factors. The implementation of the method is described by Seitidis et al. (2023) <doi:10.1002/sim.9891>.

Generate objects that simulate survival times. Random values for the distributions are generated using the method described by Bender (2003) <https://epub.ub.uni-muenchen.de/id/eprint/1716> and Leemis (1987) in Operations Research, 35(6), 892â 894.

We have designed this package to address experimental scenarios involving multiple covariates. It focuses on construction of Optimal Covariate Designs (OCDs), checking space filling property of the developed design. The primary objective of the package is to generate OCDs using four methods viz., M array method, Juxtapose method, Orthogonal Integer Array and Hadamard method. The package also evaluates space filling properties of both the base design and OCDs using the MaxPro criterion, providing a meaningful basis for comparison. In addition, it includes tool to visualize the spread offered by the design points in the form of scatterplot, which help users to assess distribution and coverage of design points.

It helps in determination of sample size for estimation of population mean and proportion based upon the availability of prior information on coefficient of variation (CV) of the population under Simple Random Sampling (SRS) with or without replacement sampling design. If there is no prior information on the population CV, then a small preliminary sample of size is selected to estimate the population CV which is then used for determination of final sample size. If the final sample size is more than the preliminary sample size, then the preliminary sample is augmented by drawing additional units from the remaining population units so that the size of the augmented sample is equal to the final sample size. On the other hand, if the preliminary sample size is larger than the final sample size, then the preliminary sample is considered as the final sample.

Suite of helper functions for data wrangling and visualization. The only theme for these functions is that they tend towards simple, short, and narrowly-scoped. These functions are built for tasks that often recur but are not large enough in scope to warrant an ecosystem of interdependent functions.