This tool provides an algorithm to identify rare cell types in single-cell data. It also identifies abundant cell types. The method is based on transcript counts obtained with unique molecular identifies.

This package lets you compute and plot predictions, slopes, marginal means, and comparisons (contrasts, risk ratios, odds, etc.) for over 100 classes of statistical and machine learning models in R. Conduct linear and non-linear hypothesis tests, or equivalence tests. Calculate uncertainty estimates using the delta method, bootstrapping, or simulation-based inference. Details can be found in Arel-Bundock, Greifer, and Heiss (2024) <doi:10.18637/jss.v111.i09>.

This package provides the prediction() function, a type-safe alternative to predict() that always returns a data frame. The package currently supports common model types (e.g., "lm", "glm") from the stats package, as well as numerous other model classes from other add-on packages.

This package fits latent (hidden) Markov models on mixed categorical and continuous (time series) data, otherwise known as dependent mixture models.

This is a package supporting the analysis of multivariate dichotomous and polytomous data using latent trait models under the Item Response Theory approach. It includes the Rasch, the Two-Parameter Logistic, the Birnbaum's Three-Parameter, the Graded Response, and the Generalized Partial Credit Models.

This package lets you interface to Nocedal et al. L-BFGS-B.3.0 limited memory BFGS minimizer with bounds on parameters. This registers a R compatible C interface to L-BFGS-B.3.0 that uses the same function types and optimization as the optim() function. This package also adds more stopping criteria as well as allowing the adjustment of more tolerances.

This package provides a set of predicates and assertions for checking the properties of strings. This is mainly for use by other package developers who want to include run-time testing features in their own packages.

This package offers quick statistical hypothesis testing for matrix rows/columns. The main goals are speed through vectorization, detailed and user-friendly output, and compatibility with tests implemented in R.

This package provides header library and R functions to solve minimum cost bipartite matching problem using Huhn-Munkres algorithm (Hungarian algorithm; <https://en.wikipedia.org/wiki/Hungarian_algorithm>; Kuhn (1955) <doi:10.1002/nav.3800020109>).

Access to selected functions found in liblwgeom, the light-weight geometry library used by PostGIS.

This package provides a minimal R client to access the GitHub API.

This package provides kernel smoothers for univariate and multivariate data, including density functions, density derivatives, cumulative distributions, modal clustering, discriminant analysis, and two-sample hypothesis testing.

This package provides an enum-type representation of vectors and representation of intervals, including a method of coercing variables in data frames.

This package contains a collection of various functions to assist in R programming, such as tools to assist in developing, updating, and maintaining R and R packages, calculating the logit and inverse logit transformations, tests for whether a value is missing, empty or contains only NA and NULL values, and many more.

This package provides tools to create and modify network objects. The network class can represent a range of relational data types, and supports arbitrary vertex/edge/graph attributes.

This package provides functions for cognitive diagnosis modeling and multidimensional item response modeling for dichotomous and polytomous item responses. It enables the estimation of the DINA and DINO model, the multiple group (polytomous) GDINA model, the multiple choice DINA model, the general diagnostic model (GDM), the structured latent class model (SLCA), and regularized latent class analysis. See George, Robitzsch, Kiefer, Gross, and Uenlue (2017) doi:10.18637/jss.v074.i02 for further details on estimation and the package structure. For tutorials on how to use the CDM package see George and Robitzsch (2015, doi:10.20982/tqmp.11.3.p189) as well as Ravand and Robitzsch (2015).

This package performs projection predictive feature selection for generalized linear models and generalized linear and additive multilevel models. The package is compatible with the rstanarm and brms packages, but other reference models can also be used. See the package vignette for more information and examples.

This package provides lots of plotting, various labeling, axis and color scaling functions for R.

This package provides a complete environment for Bayesian inference using a variety of different samplers.

This package provides a generic infrastructure for creating and using R package registries.

This package provides functions for Constraint Based Simulation using Flux Balance Analysis and informative analysis of the data generated during simulation.

This package provides tools for maximum a posteriori estimation for linear and generalized linear mixed-effects models in a Bayesian setting. It extends the lme4 package.

This is a subset of the spatstat package, containing its functionality for spatial data on a linear network.

This package provides a toolbox for working with base types, core R features like the condition system, and core Tidyverse features like tidy evaluation.