Linear Mixed-Effects Models using 'Eigen' and S4

Fit linear and generalized linear mixed-effects models. The models and their components are represented using S4 classes and methods. The core computational algorithms are implemented using the 'Eigen' C++ library for numerical linear algebra and 'RcppEigen' "glue".

Estimated Marginal Means, aka Least-Squares Means

Obtain estimated marginal means (EMMs) for many linear, generalized linear, and mixed models. Compute contrasts or linear functions of EMMs, trends, and comparisons of slopes. Plots and other displays. Least-squares means are discussed, and the term "estimated marginal means" is suggested, in Searle, Speed, and Milliken (1980) Population marginal means in the linear model: An alternative to least squares means, The American Statistician 34(4), 216-221 .

Read and Write MAT Files and Call MATLAB from Within R

Methods readMat() and writeMat() for reading and writing MAT files. For user with MATLAB v6 or newer installed (either locally or on a remote host), the package also provides methods for controlling MATLAB (trademark) via R and sending and retrieving data between R and MATLAB.

A Future API for Parallel and Distributed Processing using 'batchtools'

Implementation of the Future API on top of the 'batchtools' package. This allows you to process futures, as defined by the 'future' package, in parallel out of the box, not only on your local machine or ad-hoc cluster of machines, but also via high-performance compute ('HPC') job schedulers such as 'LSF', 'OpenLava', 'Slurm', 'SGE', and 'TORQUE' / 'PBS', e.g. 'y <- future.apply::future_lapply(files, FUN = process)'.

Easy Handling of and Access to Files Organized in Structured Directories

A file set refers to a set of files located in one or more directories on the file system. This package provides classes and methods to locate, setup, subset, navigate and iterate such sets. The API is designed such that these classes can be extended via inheritance to provide a richer API for special file formats. Moreover, a specific name format is defined such that filenames and directories can be considered to have full names which consists of a name followed by comma-separated tags. This adds additional flexibility to identify file sets and individual files. NOTE: This package's API should be considered to be in an beta stage. Its main purpose is currently to support the aroma.* packages, where it is one of the main core components; if you decide to build on top of this package, please contact the author first.

Analysis of Factorial Experiments

Convenience functions for analyzing factorial experiments using ANOVA or mixed models. aov_ez(), aov_car(), and aov_4() allow specification of between, within (i.e., repeated-measures), or mixed (i.e., split-plot) ANOVAs for data in long format (i.e., one observation per row), automatically aggregating multiple observations per individual and cell of the design. mixed() fits mixed models using lme4::lmer() and computes p-values for all fixed effects using either Kenward-Roger or Satterthwaite approximation for degrees of freedom (LMM only), parametric bootstrap (LMMs and GLMMs), or likelihood ratio tests (LMMs and GLMMs). afex_plot() provides a high-level interface for interaction or one-way plots using ggplot2, combining raw data and model estimates. afex uses type 3 sums of squares as default (imitating commercial statistical software).

Core Methods and Classes Used by 'aroma.*' Packages Part of the Aroma Framework

Core methods and classes used by higher-level 'aroma.*' packages part of the Aroma Project, e.g. 'aroma.affymetrix' and 'aroma.cn'.

Simple Memory Profiling for R

A simple and light-weight API for memory profiling of R expressions. The profiling is built on top of R's built-in memory profiler ('utils::Rprofmem()'), which records every memory allocation done by R (also native code).

Analysis of Parent-Specific DNA Copy Numbers

Segmentation of allele-specific DNA copy number data and detection of regions with abnormal copy number within each parental chromosome. Both tumor-normal paired and tumor-only analyses are supported.

Create Compact Hash Digests of R Objects

Implementation of a function 'digest()' for the creation of hash digests of arbitrary R objects (using the 'md5', 'sha-1', 'sha-256', 'crc32', 'xxhash', 'murmurhash', 'spookyhash', 'blake3', 'crc32c', 'xxh3_64', and 'xxh3_128' algorithms) permitting easy comparison of R language objects, as well as functions such as'hmac()' to create hash-based message authentication code. Please note that this package is not meant to be deployed for cryptographic purposes for which more comprehensive (and widely tested) libraries such as 'OpenSSL' should be used.