Package: estimateW 0.2.0

estimateW: Estimation of Spatial Weight Matrices

Bayesian estimation of spatial weight matrices in spatial econometric panel models. Allows for estimation of spatial autoregressive (SAR), spatial error (SEM), spatial Durbin (SDM), spatial error Durbin (SDEM) and spatially lagged explanatory variable (SLX) type specifications featuring an unknown spatial weight matrix. Methodological details are given in Krisztin and Piribauer (2022) <doi:10.1080/17421772.2022.2095426>.

Authors:Tamas Krisztin [aut, cre], Philipp Piribauer [aut]

estimateW_0.2.0.tar.gz
estimateW_0.2.0.zip(r-4.7-any)estimateW_0.2.0.zip(r-4.6-any)estimateW_0.2.0.zip(r-4.5-any)
estimateW_0.2.0.tgz(r-4.6-any)estimateW_0.2.0.tgz(r-4.5-any)
estimateW_0.2.0.tar.gz(r-4.7-any)estimateW_0.2.0.tar.gz(r-4.6-any)
estimateW_0.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
estimateW/json (API)

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

Bug tracker:https://github.com/tkrisztin/estimatew/issues

Datasets:
  • covid - Covid incidences data
  • nuts1growth - Regional growth data for European NUTS-1 regions

On CRAN:

Conda:

3.30 score 1 scripts 494 downloads 25 exports 5 dependencies

Last updated from:e9b2587d2b. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK147
source / vignettesOK220
linux-release-x86_64OK100
macos-release-arm64OK152
macos-oldrel-arm64OK197
windows-develOK72
windows-releaseOK70
windows-oldrelOK71
wasm-releaseOK158

Exports:bbinompdfbeta_priorsbeta_samplerbetapdflogdetAinvUpdatelogdetPaceBarrylogdetSplinenormalgammarho_priorsrho_samplersarsarwsdemsdemwsdmsdmwsemsemwsigma_priorssigma_samplersim_dgpslxslxwW_priorsW_sampler

Dependencies:latticeMatrixmatrixcalcplot.matrixR6

Readme and manuals

Help Manual

Help pageTopics
Probability density for a hierarchical prior setup for the elements of the adjacency matrix based on the beta binomial distributionbbinompdf
Set prior specifications for the slope parametersbeta_priors
An R6 class for sampling slope parametersbeta_sampler
The four-parameter Beta probability density functionbetapdf
Covid incidences datacovid
Efficient update of the log-determinant and the matrix inverselogdetAinvUpdate
Pace and Barry's log determinant approximationlogdetPaceBarry
Spline-based log determinant approximationlogdetSpline
A Markov Chain Monte Carlo (MCMC) sampler for a linear panel modelnormalgamma
Regional growth data for European NUTS-1 regionsnuts1growth
Graphical summary of the estimated adjacency matrix Omegaplot.estimateW
Graphical summary of a generated spatial weight matrixplot.sim_dgp
Specify prior for the spatial autoregressive parameter and sampling settingsrho_priors
An R6 class for sampling the spatial autoregressive parameter rhorho_sampler
A Markov Chain Monte Carlo (MCMC) sampler for the panel spatial autoregressive model (SAR) with exogenous spatial weight matrix.sar
A Markov Chain Monte Carlo (MCMC) sampler for the panel spatial autoregressive model (SAR) with unknown spatial weight matrixsarw
A Markov Chain Monte Carlo (MCMC) sampler for the panel spatial Durbin error model (SDEM) with exogenous spatial weight matrix.sdem
A Markov Chain Monte Carlo (MCMC) sampler for the panel spatial Durbin error model (SDEM) with unknown spatial weight matrixsdemw
A Markov Chain Monte Carlo (MCMC) sampler for the panel spatial Durbin model (SDM) with exogenous spatial weight matrix.sdm
A Markov Chain Monte Carlo (MCMC) sampler for the panel spatial Durbin model (SDM) with unknown spatial weight matrixsdmw
A Markov Chain Monte Carlo (MCMC) sampler for the panel spatial error model (SEM) with exogenous spatial weight matrix.sem
A Markov Chain Monte Carlo (MCMC) sampler for the panel spatial error model (SEM) with unknown spatial weight matrixsemw
Set prior specification for the error variance using an inverse Gamma distributionsigma_priors
An R6 class for sampling for sampling sigma^2sigma_sampler
Simulating from a data generating processsim_dgp
A Markov Chain Monte Carlo (MCMC) sampler for the panel spatial SLX model with exogenous spatial weight matrixslx
A Markov Chain Monte Carlo (MCMC) sampler for the panel spatial SLX model with unknown spatial weight matrixslxw
Set prior specifications for the spatial weight matrixW_priors
An R6 class for sampling the elements of WW_sampler