Package: BANAM 0.2.1

Joris Mulder

BANAM: Bayesian Analysis of the Network Autocorrelation Model

The network autocorrelation model (NAM) can be used for studying the degree of social influence regarding an outcome variable based on one or more known networks. The degree of social influence is quantified via the network autocorrelation parameters. In case of a single network, the Bayesian methods of Dittrich, Leenders, and Mulder (2017) <doi:10.1016/j.socnet.2016.09.002> and Dittrich, Leenders, and Mulder (2019) <doi:10.1177/0049124117729712> are implemented using a normal, flat, or independence Jeffreys prior for the network autocorrelation. In the case of multiple networks, the Bayesian methods of Dittrich, Leenders, and Mulder (2020) <doi:10.1177/0081175020913899> are implemented using a multivariate normal prior for the network autocorrelation parameters. Flat priors are implemented for estimating the coefficients. For Bayesian testing of equality and order-constrained hypotheses, the default Bayes factor of Gu, Mulder, and Hoijtink, (2018) <doi:10.1111/bmsp.12110> is used with the posterior mean and posterior covariance matrix of the NAM parameters based on flat priors as input.

Authors:Joris Mulder [aut, cre], Dino Dittrich [aut, ctb], Roger Leenders [aut, ctb]

BANAM_0.2.1.tar.gz
BANAM_0.2.1.tar.gz(r-4.5-noble)BANAM_0.2.1.tar.gz(r-4.4-noble)
BANAM_0.2.1.tgz(r-4.4-emscripten)BANAM_0.2.1.tgz(r-4.3-emscripten)
BANAM.pdf |BANAM.html✨
BANAM/json (API)

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

Peer review:

Datasets:
  • W_votes - Weight matrix for counties in Alabama, US
  • X_votes - Covariate data frame for the Alabama voter turnout data
  • y_votes - Logarithmized voter turnout in Alabama, US

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1 exports 0.09 score 77 dependencies 1 scripts 231 downloads

Last updated 3 months agofrom:1163b7415c. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 20 2024
R-4.5-linuxOKAug 20 2024

Exports:banam

Dependencies:bainBergmBFpackbootcachemclicodaDEoptimRergmevaluateextraDistrfansifastmapgluegmmGPArotationgslhighrknitrlatticelavaanlifecyclelme4lpSolveAPImagrittrMASSMatrixmatrixcalcMatrixModelsmcmcMCMCpackmemoisemgcvminqamnormtmvtnormnetworknlmenloptrnumDerivpbivnormpillarpkgconfigpracmapsychpurrrQRMquadprogquantregrARPACKrbibutilsRcppRcppEigenRdpackRglpkrlangrlerobustbaseRSpectrasandwichslamsnaSparseMstatnet.commonstringistringrsurvivaltibbletimeDatetimeSeriestmvtnormtrustutf8vctrsxfunyamlzoo