Package: spBFA 1.2

spBFA: 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. Full details of the package can be found in the accompanying vignette. Furthermore, the details of the package can be found in "Bayesian Non-Parametric Factor Analysis for Longitudinal Spatial Surfaces", by Berchuck et al (2019), <arxiv:1911.04337>. The paper is in press at the journal Bayesian Analysis.

Authors:Samuel I. Berchuck [aut, cre]

spBFA_1.2.tar.gz
spBFA_1.2.zip(r-4.5)spBFA_1.2.zip(r-4.4)spBFA_1.2.zip(r-4.3)
spBFA_1.2.tgz(r-4.4-x86_64)spBFA_1.2.tgz(r-4.4-arm64)spBFA_1.2.tgz(r-4.3-x86_64)spBFA_1.2.tgz(r-4.3-arm64)
spBFA_1.2.tar.gz(r-4.5-noble)spBFA_1.2.tar.gz(r-4.4-noble)
spBFA_1.2.tgz(r-4.4-emscripten)spBFA_1.2.tgz(r-4.3-emscripten)
spBFA.pdf |spBFA.html✨
spBFA/json (API)

Peer review:

Bug tracker:https://github.com/berchuck/spbfa/issues

On CRAN:

3 exports 3 stars 0.84 score 9 dependencies 3 scripts 210 downloads

Last updated 2 years agofrom:51c9ec571d. Checks:OK: 5 WARNING: 4. Indexed: yes.

Exports:bfa_spdiagnosticsis.spBFA

Dependencies:expmlatticeMatrixmsmmvtnormpgdrawRcppRcppArmadillosurvival