Packaged: 2021-03-08 09:34:45 UTC; Administrateur

Title: Batching Routines in Parallel and Passing Command-Line Arguments
Description: Functions to allow you to easily pass command-line
arguments into R (primary use is linux), 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.

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

Description: 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.

Title: Consistent Batch Means Estimation of Monte Carlo Standard Errors
Author: Murali Haran <mharan@stat.psu.edu> and John Hughes
Description: batchmeans provides consistent batch means estimation of
Packaged: 2012-06-19 18:28:23 UTC; jphughesjr

Author: Murali Haran <mharan@stat.psu.edu> and John Hughes <hughesj@umn.edu>
Description: batchmeans provides consistent batch means estimation of Monte
Packaged: 2015-01-16 14:45:05 UTC; jphughesjr

Description: Provides consistent batch means estimation of Monte
Packaged: 2016-07-03 23:18:09 UTC; jphughesjr

Packaged: 2020-05-07 14:07:01 UTC; jphughesjr

Title: Semi-Supervised Bayesian Mixture Models Incorporating Batch
Authors@R: c(person(given = "Stephen", family = "Coleman",
person(given = "Chris", family ="Wallace",
Description: 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.
Imports: Rcpp (>= 1.0.5), tidyr, ggplot2