Google’s Compact Language Detector 3 (cld3)
Google’s Compact Language Detector 3 is a neural network model for language identification and the successor of ‘cld2’ (available from CRAN). The algorithm is still experimental and takes a novel approach to language detection with different properties and outcomes. It can be useful to combine this with the Bayesian classifier results from ‘cld2’. See <https://…/cld3#readme> for more information.

A Future API for Parallel and Distributed Processing using ‘batchtools’ (future.batchtools)
Implements 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_lapply(files, FUN = process)’.

R Bindings to Zstandard Compression Library (zstdr)
Provides R bindings to the ‘Zstandard’ compression library. ‘Zstandard’ is a real-time compression algorithm, providing high compression ratios. It offers a very wide range of compression / speed trade-off, while being backed by a very fast decoder. See <http://…/> for more information.

Robust Statistical Methods (walrus)
A toolbox of common robust statistical tests, including robust descriptives, robust t-tests, and robust ANOVA. It is also available as a module for ‘jamovi’ (see <> for more information). Walrus is based on the WRS2 package by Patrick Mair, which is in turn based on the scripts and work of Rand Wilcox. These analyses are described in depth in the book ‘Introduction to Robust Estimation & Hypothesis Testing’.

Generalized Multistate Simulation Model (gems)
Simulate and analyze multistate models with general hazard functions. gems provides functionality for the preparation of hazard functions and parameters, simulation from a general multistate model and predicting future events. The multistate model is not required to be a Markov model and may take the history of previous events into account. In the basic version, it allows to simulate from transition-specific hazard function, whose parameters are multivariable normally distributed.