Package: SARAR 0.1.0

SARAR: Bayesian Estimation of Spatial Autoregressive Models with Flexible Error Distributions

Provides Bayesian estimation and inference for spatial autoregressive models with spatial dependence in both the response variable and the error terms. The models accommodate Gaussian, Student-t, Laplace, skew-normal, and skew-Student-t error distributions. Posterior inference is performed using Markov chain Monte Carlo methods. The package also provides posterior summaries, convergence diagnostics, residual analysis, and model comparison criteria, including the Deviance Information Criterion (DIC) and the Watanabe-Akaike Information Criterion (WAIC). See LeSage and Pace (2009, ISBN:9781420064247), "Introduction to Spatial Econometrics".

Authors:Luis Hernando Vanegas [aut, cre], Luz Marina Rondón [aut]

SARAR_0.1.0.tar.gz
SARAR_0.1.0.zip(r-4.7-any)SARAR_0.1.0.zip(r-4.6-any)SARAR_0.1.0.zip(r-4.5-any)
SARAR_0.1.0.tgz(r-4.6-any)SARAR_0.1.0.tgz(r-4.5-any)
SARAR_0.1.0.tar.gz(r-4.7-any)SARAR_0.1.0.tar.gz(r-4.6-any)
SARAR_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
SARAR/json (API)

# Install 'SARAR' in R:
install.packages('SARAR', repos = c('https://lhvanegasp.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/lhvanegasp/sarar/issues

Datasets:
  • boston - Boston Census Tracts
  • W1 - Boston Contiguity Spatial Weight Matrix
  • W2 - Boston Four-Nearest-Neighbor Spatial Weight Matrix

On CRAN:

Conda:

1.00 score 5 exports 20 dependencies

Last updated from:914f3c3788. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK143
source / vignettesOK150
linux-releaseOK121
macos-releaseOK122
macos-oldrelOK85
windows-develOK172
windows-releaseOK207
windows-oldrelOK167
wasm-releaseOK101

Exports:DICeffectiveSize_SPREGgeweke_diagSPREGspatialRegWAIC

Dependencies:bootclassclassIntcodaDBIdeldire1071FormulaKernSmoothlatticeproxyRcpps2sfspspDataspdepstatmodunitswk