Package: netcmc 1.0.2
George Gerogiannis
netcmc: Spatio-Network Generalised Linear Mixed Models for Areal Unit and Network Data
Implements a class of univariate and multivariate spatio-network generalised linear mixed models for areal unit and network data, with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation. The response variable can be binomial, Gaussian, or Poisson. Spatial autocorrelation is modelled by a set of random effects that are assigned a conditional autoregressive (CAR) prior distribution following the Leroux model (Leroux et al. (2000) <doi:10.1007/978-1-4612-1284-3_4>). Network structures are modelled by a set of random effects that reflect a multiple membership structure (Browne et al. (2001) <doi:10.1177/1471082X0100100202>).
Authors:
netcmc_1.0.2.tar.gz
netcmc_1.0.2.tar.gz(r-4.5-noble)netcmc_1.0.2.tar.gz(r-4.4-noble)
netcmc_1.0.2.tgz(r-4.4-emscripten)netcmc_1.0.2.tgz(r-4.3-emscripten)
netcmc.pdf |netcmc.html✨
netcmc/json (API)
# Install 'netcmc' in R: |
install.packages('netcmc', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 years agofrom:ca5c9511ce. Checks:OK: 1 NOTE: 1. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 27 2024 |
R-4.5-linux-x86_64 | NOTE | Nov 27 2024 |
Exports:getAdjacencyMatrixgetMembershipMatrixgetTotalAltersByStatusmultiNetmultiNetLerouxmultiNetRanduniuniNetuniNetLerouxuniNetRand
Dependencies:clicodacolorspacefansifarverggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixMatrixModelsmcmcMCMCpackmgcvmunsellmvtnormnlmepillarpkgconfigquantregR6RColorBrewerRcppRcppArmadilloRcppProgressrlangscalesSparseMsurvivaltibbleutf8vctrsviridisLitewithr