spBFA (1.0)

0 users

Spatial Bayesian Factor Analysis.


Implements a spatial Bayesian non-parametric factor analysis model with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC). Spatial correlation is introduced in the columns of the factor loadings matrix using a Bayesian non-parametric prior, the probit stick-breaking process. Areal spatial data is modeled using a conditional autoregressive (CAR) prior and point-referenced spatial data is treated using a Gaussian process. The response variable can be modeled as Gaussian, probit, Tobit, or Binomial (using Polya-Gamma augmentation). Temporal correlation is introduced for the latent factors through a hierarchical structure and can be specified as exponential or first-order autoregressive.

Maintainer: Samuel I. Berchuck
Author(s): Samuel I. Berchuck [aut, cre]

License: GPL (>= 2)

Uses: msm, mvtnorm, pgdraw, Rcpp, classInt, coda, knitr, rmarkdown, womblR

Released about 1 month ago.



  (0 votes)


  (0 votes)

Log in to vote.


No one has written a review of spBFA yet. Want to be the first? Write one now.

Related packages:(20 best matches, based on common tags.)

Search for spBFA on google, google scholar, r-help, r-devel.

Visit spBFA on R Graphical Manual.