Author: Will Beasley, Joe Rodgers, David Bard, and Kelly Meredith
Packaged: 2012-04-27 03:07:45 UTC; wibeasley

Packaged: 2012-05-20 20:30:15 UTC; wibeasley

Author: Will Beasley, Joe Rodgers, David Bard, Michael Hunter, and
Packaged: 2012-12-19 05:55:09 UTC; wibeasley

Title: Utilities and kinship information for behavior genetics and
Author: Will Beasley, Joe Rodgers, David Bard, Michael Hunter, and Kelly
Description: Utilities and kinship information for behavior genetics and
developmental research using the NLSY (http://www.bls.gov/nls/)
Suggests: devtools, ggplot2, knitr, plyr, scales, testit, testthat,

developmental research using the National Longitudinal Survey of Youth

Title: Utilities and Kinship Information for Research with the NLSY
Author: Will Beasley, Joe Rodgers, David Bard, Michael Hunter, S. Mason Garrison, and Kelly

Author: Will Beasley, Joe Rodgers, David Bard, Michael Hunter, S. Mason
Packaged: 2016-04-19 06:38:21 UTC; wibeasley

Title: Miscellaneous Functions Used at 'Numeract LLC'
person("Mike", "Badescu", email = "mike.badescu@numeract.com", role = c("aut", "cre")),
person("Ana-Maria", "Niculescu", role = "aut"),
person("Teodor", "Ciuraru", role = "ctb"),
Description: Contains functions useful for debugging, set operations on vectors,
and 'UTC' date and time functionality. It adds a few vector manipulation
verbs to 'purrr' and 'dplyr' packages. It can also generate an R file to
install and update packages to simplify deployment into production. The
functions were developed at the data science firm 'Numeract LLC' and are
Imports: dplyr, magrittr, purrr, rappdirs, rlang, tibble, stringr

Imports: dplyr, magrittr, purrr, rappdirs, rlang, tibble, tidyselect,

Title: Bayesian Inference on Univariate Normal Mixtures
Description: A program for Bayesian analysis of univariate normal mixtures
with an unknown number of components. This makes use of reversible jump
Markov chain Monte Carlo methods that are capable of jumping between
the parameter sub-spaces corresponding to different numbers of components
in the mixture. A sample from the full joint distribution of all unknown
variables is thereby generated, and this can be used as a basis for a
thorough presentation of many aspects of the posterior distribution.