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.

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.

Consistent Batch Means Estimation of Monte Carlo Standard Errors
Murali Haran <mharan@stat.psu.edu> and John Hughes
<hughesj@umn.edu>
batchmeans provides consistent batch means estimation of
Monte Carlo standard errors.

Murali Haran <mharan@stat.psu.edu> and John Hughes <hughesj@umn.edu>
batchmeans provides consistent batch means estimation of Monte
Carlo standard errors.

Murali Haran <mharan@stat.psu.edu> and John Hughes
<jphughesjr@gmail.com>
Provides consistent batch means estimation of Monte
Carlo standard errors.

Murali Haran <mharan@stat.psu.edu> and John Hughes
<drjphughesjr@gmail.com>

Semi-Supervised Bayesian Mixture Models Incorporating Batch
Correction
c(person(given = "Stephen", family = "Coleman",
email = "stephen.coleman@mrc-bsu.cam.ac.uk",
role = c("aut","cre")),
person(given = "Paul", family ="Kirk",
email = "paul.kirk@mrc-bsu.cam.ac.uk",
role = "aut"),
person(given = "Chris", family ="Wallace",
email = "cew54@cam.ac.uk",
role = "aut"))
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.
Stephen Coleman [aut, cre],
Paul Kirk [aut],
Chris Wallace [aut]