Aggregates large single-cell data into metacell dataset by merging together gene expression of very similar cells. SuperCell uses velocyto.R <doi:10.1038/s41586-018-0414-6> <https://github.com/velocyto-team/velocyto.R> for RNA velocity and WeightedCluster <doi:10.12682/lives.2296-1658.2013.24> <https://mephisto.unige.ch/weightedcluster/> for weighted clustering on metacells. We also recommend installing scater Bioconductor package <doi:10.18129/B9.bioc.scater> <https://bioconductor.org/packages/release/bioc/html/scater.html>.

This package provides a-priori, post-hoc, and compromise power-analyses for structural equation models (SEM).

This package provides a collection of tools and functions to adjust a variety of stochastic blockmodels (SBM). Supports at the moment Simple, Bipartite, Multipartite and Multiplex SBM (undirected or directed with Bernoulli, Poisson or Gaussian emission laws on the edges, and possibly covariate for Simple and Bipartite SBM). See Léger (2016) <doi:10.48550/arXiv.1602.07587>, Barbillon et al. (2020) <doi:10.1111/rssa.12193> and Bar-Hen et al. (2020) <doi:10.48550/arXiv.1807.10138>.

Reimplementation of the svDialogs dialog boxes in Tcl/Tk.

Spatiotemporal individual-level model of seasonal infectious disease transmission within the Susceptible-Exposed-Infectious-Recovered-Susceptible (SEIRS) framework are applied to model seasonal infectious disease transmission. This package employs a likelihood based Monte Carlo Expectation Conditional Maximization (MCECM) algorithm for estimating model parameters. In addition to model fitting and parameter estimation, the package offers functions for calculating AIC using real pandemic data and conducting simulation studies customized to user-specified model configurations.

This package provides a dynamic timer control (DTC) is a shiny widget that enables time-based processes in applications. It allows users to execute these processes manually in individual steps or at customizable speeds. The timer can be paused, resumed, or restarted. This control is particularly well-suited for simulations, animations, countdowns, or interactive visualizations.

Generate and translate standard Universally Unique Identifiers (UUIDs) into shorter - or just different - formats and back. Also implements base58 encoders and decoders.

This htmlwidget provides pan and zoom interactivity to R graphics, including base', lattice', and ggplot2'. The interactivity is provided through the svg-pan-zoom.js library. Various options to the widget can tailor the pan and zoom experience to nearly any user desire.

Spatial versions of Regression Discontinuity Designs (RDDs) are becoming increasingly popular as tools for causal inference. However, conducting state-of-the-art analyses often involves tedious and time-consuming steps. This package offers comprehensive functionalities for executing all required spatial and econometric tasks in a streamlined manner. Moreover, it equips researchers with tools for performing essential placebo and balancing checks comprehensively. The fact that researchers do not have to rely on APIs of external GIS software ensures replicability and raises the standard for spatial RDDs.

This package provides a statistical disclosure control tool to protect frequency tables in cases where small values are sensitive. The function PLSrounding() performs small count rounding of necessary inner cells so that all small frequencies of cross-classifications to be published (publishable cells) are rounded. This is equivalent to changing micro data since frequencies of unique combinations are changed. Thus, additivity and consistency are guaranteed. The methodology is described in Langsrud and Heldal (2018) <https://www.researchgate.net/publication/327768398_An_Algorithm_for_Small_Count_Rounding_of_Tabular_Data>.

This package provides a shiny interface for a simpler use of the sbm R package. It also contains useful functions to easily explore the sbm package results. With this package you should be able to use the stochastic block model without any knowledge in R, get automatic reports and nice visuals, as well as learning the basic functions of sbm'.

This package provides utilities for generating SQL queries (particularly CREATE TABLE statements) from R model objects. The most important use case is generating SQL to score a generalized linear model or related model represented as an R object, in which case the package handles parsing formula operators and including the model's response function.

Simulates and plots quantities of interest (relative hazards, first differences, and hazard ratios) for linear coefficients, multiplicative interactions, polynomials, penalised splines, and non-proportional hazards, as well as stratified survival curves from Cox Proportional Hazard models. It also simulates and plots marginal effects for multiplicative interactions. Methods described in Gandrud (2015) <doi:10.18637/jss.v065.i03>.

This package implements the Stable Balancing Weights by Zubizarreta (2015) <DOI:10.1080/01621459.2015.1023805>. These are the weights of minimum variance that approximately balance the empirical distribution of the observed covariates. For an overview, see Chattopadhyay, Hase and Zubizarreta (2020) <DOI:10.1002/sim.8659>. To solve the optimization problem in sbw', the default solver is quadprog', which is readily available through CRAN. The solver osqp is also posted on CRAN. To enhance the performance of sbw', users are encouraged to install other solvers such as gurobi and Rmosek', which require special installation. For the installation of gurobi and pogs, please follow the instructions at <https://docs.gurobi.com/projects/optimizer/en/current/reference/r.html> and <http://foges.github.io/pogs/stp/r>.

It contains soft clustering algorithms, in particular approaches derived from rough set theory: Lingras & West original rough k-means, Peters refined rough k-means, and PI rough k-means. It also contains classic k-means and a corresponding illustrative demo.

This package provides the necessary sample size for a longitudinal study with binary outcome in order to attain a pre-specified power while strictly maintaining the Type I error rate. Kapur K, Bhaumik R, Tang XC, Hur K, Reda DJ, Bhaumik D (2014) <doi:10.1002/sim.6203>.

This is an interface for the Python package StepMix'. It is a Python package following the scikit-learn API for model-based clustering and generalized mixture modeling (latent class/profile analysis) of continuous and categorical data. StepMix handles missing values through Full Information Maximum Likelihood (FIML) and provides multiple stepwise Expectation-Maximization (EM) estimation methods based on pseudolikelihood theory. Additional features include support for covariates and distal outcomes, various simulation utilities, and non-parametric bootstrapping, which allows inference in semi-supervised and unsupervised settings. Software paper available at <doi:10.18637/jss.v113.i08>.

Generates, plays, and solves Sudoku puzzles. The GUI playSudoku() needs package "tkrplot" if you are not on Windows.

This package provides a tool for simulating rhythmic data: transcriptome data using Gaussian or negative binomial distributions, and behavioral activity data using Bernoulli or Poisson distributions. See Singer et al. (2019) <doi:10.7717/peerj.6985>.

Speeds up the process of loading raw data from MBA (Multiplex Bead Assay) examinations, performs quality control checks, and automatically normalises the data, preparing it for more advanced, downstream tasks. The main objective of the package is to create a simple environment for a user, who does not necessarily have experience with R language. The package is developed within the project PvSTATEM', which is an international project aiming for malaria elimination.

Renders plots to a temporary image using the ragg graphics device and returns knitr::include_graphics() output. Optionally saves the image to a specified path. This helps ensure consistent appearance across interactive sessions, saved files, and knitted documents. For more details see Pedersen and Shemanarev (2025) <doi: 10.32614/CRAN.package.ragg>.

We provide functions for estimation and inference of nonlinear and non-stationary time series regression using the sieve methods and bootstrapping procedure.

This package provides useful UI components and input widgets for Shiny applications. The offered components allow to apply non-standard operations and view to your Shiny application, but also help to overcome common performance issues.

Complementary indexes calculation to the Outlying Mean Index analysis to explore niche shift of a community and biological constraint within an Euclidean space, with graphical displays. For details see Karasiewicz et al. (2017) <doi:10.7717/peerj.3364>.