Title: A Package for Performing a Bayesian Analysis of Change Point Problems
Packaged: Mon Dec  3 17:23:52 2007; cle7

Author: Chandra Erdman and John W. emerson
Packaged: Tue Dec  4 15:02:00 2007; cle7

Packaged: Wed May 14 15:27:26 2008; cle7

Packaged: Tue Nov 13 14:08:50 2007; cle7

Packaged: Fri May 30 11:01:52 2008; cle7

Title: A Package for Performing a Bayesian Analysis of Change Point
Author: Chandra Erdman and John W. Emerson
Description: An implementation of the Barry and Hartigan (1993) product
partition model for the standard change point problem using
Packaged: Wed Mar 25 13:25:23 2009; cle7

Description: An implementation of an approximation to the Barry and
Hartigan (1993) product partition model for the standard change
point problem using Markov Chain Monte Carlo.

Packaged: Tue Jul 15 18:21:29 2008; cle7

Depends: methods, stats, graphics, foreach

Depends: methods, stats, graphics, foreach, grid, Rcpp (>= 0.9.2)
Description: This package provides an implementation of an
approximation to the Barry and Hartigan (1993) product
partition model for the normal errors change point problem
using Markov Chain Monte Carlo.  It also extends the
methodology to independent multivariate series with an assumed

Depends: methods, stats, graphics, foreach, iterators, grid, Rcpp (>=

Title: Bayesian Analysis of Change Point Problems
Author: Xiaofei Wang, Chandra Erdman, and John W. Emerson
Suggests: DNAcopy, coda, strucchange, vegan, ggplot2, igraph
Description: Provides an implementation of the Barry and Hartigan (1993) product partition model for the normal errors change point problem using Markov Chain Monte Carlo.  It also extends the methodology to regression models on a connected graph (Wang and Emerson, 2015); this allows estimation of change point models with multivariate responses. Parallel MCMC, previously available in bcp v.3.0.0, is currently not implemented.

Description: Provides an implementation of the Barry and Hartigan (1993) product
partition model for the normal errors change point problem using Markov Chain
Monte Carlo. It also extends the methodology to regression models on a connected
graph (Wang and Emerson, 2015); this allows estimation of change point models
with multivariate responses. Parallel MCMC, previously available in bcp v.3.0.0,