Allows the user to manage easily R packages removal and installation. It offers many functions to display installed packages according to
    specific dates and removes them if needed. The user is always prompted when running the removal functions in order to confirm
    the required action. It also provides functions that will install 'Github' starred R packages whether available on 'CRAN' or not.

Functions to allow you to easily pass command-line arguments into R, and functions to aid in submitting your R code in parallel on a cluster and joining the results afterward (e.g. multiple parameter values for simulations running in parallel, splitting up a permutation test in parallel, etc.). See `parseCommandArgs(...)' for the main example of how to use this package.

Extends the BatchJobs package to run statistical experiments on
    batch computing clusters. For further details see the project web page.

Makes it easy to download financial data from Yahoo Finance <https://finance.yahoo.com/>.

Provides Map, Reduce and Filter variants to generate jobs on
    batch computing systems like PBS/Torque, LSF, SLURM and Sun Grid
    Engine.  Multicore and SSH systems are also supported. For further
    details see the project web page.

Provides consistent batch means estimation of Monte
    Carlo standard errors.

Semi-supervised and unsupervised Bayesian mixture models that
  simultaneously infer the cluster/class structure and a batch correction.
  Densities available are the multivariate normal and the multivariate t.
  The model sampler is implemented in C++. This package is aimed at analysis of
  low-dimensional data generated across several batches. See Coleman et al.
  (2022) <doi:10.1101/2022.01.14.476352> for details of the model.

Handy frameworks, such as error handling and log generation, for batch scripts.
	Use case: in scripts running in remote servers, set error handling mechanism for downloading and uploading and record operation log.

Different adjustment methods for batch effects in biomarker data,
  such as from tissue microarrays. Some methods attempt to retain differences 
  between batches that may be due to between-batch differences in "biological"
  factors that influence biomarker values.