This package lets you fit pedigree-based mixed-effects models.

This package provides tools used by organizational researchers for the analysis of multilevel data. It includes four broad sets of tools.

1. functions for estimating within-group agreement and reliability indices.

2. functions for manipulating multilevel and longitudinal (panel) data.

3. simulations for estimating power and generating multilevel data.

4. miscellaneous functions for estimating reliability and performing simple calculations and data transformations.

This package provides a mutation analysis tool that discovers cancer driver genes with frequent mutations in protein signalling sites such as post-translational modifications (phosphorylation, ubiquitination, etc). The Poisson generalized linear regression model identifies genes where cancer mutations in signalling sites are more frequent than expected from the sequence of the entire gene. Integration of mutations with signalling information helps find new driver genes and propose candidate mechanisms to known drivers.

This package provides a set of tools to facilitate package development and make R a more user-friendly place. It is intended mostly for developers (or anyone who writes/shares functions). It provides a simple, powerful and flexible way to check the arguments passed to functions. The developer can easily describe the type of argument needed. If the user provides a wrong argument, then an informative error message is prompted with the requested type and the problem clearly stated--saving the user a lot of time in debugging.

This package provides visualizations for SHAP (SHapley Additive exPlanations) such as waterfall plots, force plots, various types of importance plots, dependence plots, and interaction plots. These plots act on a shapviz object created from a matrix of SHAP values and a corresponding feature dataset. Wrappers for the R packages xgboost, lightgbm, fastshap, shapr, h2o, treeshap, DALEX, and kernelshap are added for convenience. By separating visualization and computation, it is possible to display factor variables in graphs, even if the SHAP values are calculated by a model that requires numerical features. The plots are inspired by those provided by the shap package in Python, but there is no dependency on it.

This package provides functions, data sets, examples, demos, and vignettes for the book Christian Kleiber and Achim Zeileis (2008), Applied Econometrics with R, Springer-Verlag, New York. ISBN 978-0-387-77316-2. (See the vignette "AER" for a package overview.)

This package provides functions to make useful (and pretty) plots for scientific plotting. Additional plotting features are added for base plotting, with particular emphasis on making attractive log axis plots.

This package provides a graph implementation that can be thought of as two tidy data frames describing node and edge data respectively. It provides an approach to manipulate these two virtual data frames using the API defined in the dplyr package, and it also provides tidy interfaces to a lot of common graph algorithms.

This is a companion package for the book "A Course in Statistics with R" (ISBN 978-1-119-15272-9.)

Data exploration and modelling is a process in which a lot of data artifacts are produced. Artifacts like: subsets, data aggregates, plots, statistical models, different versions of data sets and different versions of results. Archivist helps to store and manage artifacts created in R. It allows you to store selected artifacts as binary files together with their metadata and relations. Archivist allows sharing artifacts with others. It can look for already created artifacts by using its class, name, date of the creation or other properties. It also makes it easy to restore such artifacts.

This is an R package for the imputation of left-censored data under a compositional approach. The implemented methods consider aspects of relevance for a compositional approach such as scale invariance, subcompositional coherence or preserving the multivariate relative structure of the data. Based on solid statistical frameworks, it comprises the ability to deal with single and varying censoring thresholds, consistent treatment of closed and non-closed data, exploratory tools, multiple imputation, Markov Chain Monte Carlo (MCMC), robust and non-parametric alternatives, and recent proposals for count data.

This package provides the cumulative distribution function (CDF), quantile, and statistical power calculator for a collection of thresholding Fisher's p-value combination methods, including Fisher's p-value combination method, truncated product method and, in particular, soft-thresholding Fisher's p-value combination method which is proven to be optimal in some context of signal detection. The p-value calculator for the omnibus version of these tests are also included.

This package provides advanced tryCatch and try functions for better error handling (logging, stack trace with source code references and support for post-mortem analysis via dump files).

This package contains miscellaneous functions used to interpret and translate, factorize and negate Sum of Products expressions, for both binary and multi-value crisp sets, and to extract information (set names, set values) from those expressions. Other functions perform various other checks if possibly numeric (even if all numbers reside in a character vector) and coerce to numeric, or check if the numbers are whole. It also offers, among many others, a highly flexible recoding routine and a more flexible alternative to the base function with().

This package provides a violin plot, which is a combination of a box plot and a kernel density plot.

This package provides fast and efficient routines for common rolling / windowed operations. Routines for the efficient computation of windowed mean, median, sum, product, minimum, maximum, standard deviation and variance are provided.

This package provides an interface to the C code for Latent Dirichlet Allocation (LDA) models and Correlated Topics Models (CTM) by David M. Blei and co-authors and the C++ code for fitting LDA models using Gibbs sampling by Xuan-Hieu Phan and co-authors.

This package provides tools for defensive programming. It is inspired by purrr mappers and based on rlang. Attempt extends and facilitates defensive programming by providing a consistent grammar, and a set of functions for common tests and conditions. Attempt only depends on rlang, and focuses on speed, so it can be integrated with other functions and used in the data analysis.

This package provides functions to make zebra-striped tables (tables with alternating row colors) in LaTeX and HTML formats easily from data.frame, matrix, lm, aov, anova, glm, coxph, nls, fitdistr, mytable and cbind.mytable objects.

Servr provides an HTTP server in R to serve static files, or dynamic documents that can be converted to HTML files (e.g., R Markdown) under a given directory.

This package provides a basic set of R functions for querying the Cancer Genomics Data Server (CGDS), hosted by the Computational Biology Center at Memorial-Sloan-Kettering Cancer Center (MSKCC).

This package provides a collection of R functions to perform nonparametric analysis of covariance for regression curves or surfaces. Testing the equality or parallelism of nonparametric curves or surfaces is equivalent to analysis of variance (ANOVA) or analysis of covariance (ANCOVA) for one-sample functional data. Three different testing methods are available in the package, including one based on L-2 distance, one based on an ANOVA statistic, and one based on variance estimators.

Dunn's test computes stochastic dominance & reports pairwise comparisons. This is done following a Kruskal-Wallis test (Kruskal and Wallis, 1952). It employs Dunn's z-test-statistic approximations for rank statistics, conducting k(k-1)/2 comparisons. The null hypothesis assumes that the probability of a randomly selected value from the first group being larger than one from the second group is one half, similar to the Wilcoxon-Mann-Whitney test. Dunn's test serves as a test for median difference and takes into account tied ranks.

This package provides functions for computing the density and the distribution function of multivariate normal and "t" random variables, and for generating random vectors sampled from these distributions. Probabilities are computed via non-Monte Carlo methods.