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.

As a successor of the packages 'BatchJobs' and
    'BatchExperiments', this package provides a parallel implementation of
    the Map function for high performance computing systems managed by
    schedulers 'IBM Spectrum LSF'
    (<https://www.ibm.com/products/hpc-workload-management>), 'Univa Grid
    Engine'/'Oracle Grid Engine'
    (<https://altair.com/hpc-cloud-applications/>), 'Slurm'
    (<https://slurm.schedmd.com/>), 'TORQUE/PBS'
    (<https://adaptivecomputing.com/cherry-services/torque-resource-manager/>),
    or 'Docker Swarm' (<https://docs.docker.com/engine/swarm/>).  A
    multicore and socket mode allow the parallelization on a local
    machines, and multiple machines can be hooked up via SSH to create a
    makeshift cluster. Moreover, the package provides an abstraction
    mechanism to define large-scale computer experiments in a
    well-organized and reproducible way.

Compute bounds for the treatment effect 
    after adjusting for the presence of omitted variables in linear  
    econometric models, according to the method of Basu (2022) <arXiv:2203.12431>. 
    You supply the data, identify the outcome and 
    treatment variables and additional regressors. The main functions
    will compute bounds for the bias-adjusted treatment effect. Many
    plot functions allow easy visualization of results.