Package: nbhdmodel 0.1.0.9000

nbhdmodel: Neighborhood Modeling and Analysis

Functionality for fitting neighborhood models of McCartan, Brown, and Imai <arxiv:2110.14014>. The core methodology is described in the paper and can be implemented with any tool that can fit generalized linear mixed models (GLMMs). However, some of the preprocessing necessary to set up the GLMM can be onerous. In addition to providing a specialized GLMM routine, this package provides several preprocessing functions that, while not completely general, should be useful for others performing these kinds of analyses.

Authors:Cory McCartan [aut, cre], Jacob Brown [aut], Kosuke Imai [aut]

nbhdmodel_0.1.0.9000.tar.gz
nbhdmodel_0.1.0.9000.zip(r-4.5)nbhdmodel_0.1.0.9000.zip(r-4.4)nbhdmodel_0.1.0.9000.zip(r-4.3)
nbhdmodel_0.1.0.9000.tgz(r-4.4-x86_64)nbhdmodel_0.1.0.9000.tgz(r-4.4-arm64)nbhdmodel_0.1.0.9000.tgz(r-4.3-x86_64)nbhdmodel_0.1.0.9000.tgz(r-4.3-arm64)
nbhdmodel_0.1.0.9000.tar.gz(r-4.5-noble)nbhdmodel_0.1.0.9000.tar.gz(r-4.4-noble)
nbhdmodel.pdf |nbhdmodel.html
nbhdmodel/json (API)

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

Peer review:

Bug tracker:https://github.com/corymccartan/nbhdmodel/issues

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

2.48 score 3 stars 9 exports 72 dependencies

Last updated 9 months agofrom:6e7d39e648. Checks:OK: 3 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 26 2024
R-4.5-win-x86_64OKOct 26 2024
R-4.5-linux-x86_64OKOct 26 2024
R-4.4-win-x86_64NOTEOct 26 2024
R-4.4-mac-x86_64NOTEOct 26 2024
R-4.4-mac-aarch64NOTEOct 26 2024
R-4.3-win-x86_64NOTEOct 26 2024
R-4.3-mac-x86_64NOTEOct 26 2024
R-4.3-mac-aarch64NOTEOct 26 2024

Exports:binned_residcalc_indiv_framefastAUCfixeflocal_areaneighborhood_modelpost_inclranefsimulate_neighborhood

Dependencies:abindbackportsBHbootcallrcheckmateclassclassIntclicolorspacecpp11DBIdescdistributionaldplyre1071fansifarverfastmatchgenericsggplot2gluegridExtragtableinlineisobandKernSmoothlabelinglatticelifecyclelme4loomagrittrMASSMatrixmatrixStatsmgcvminqamunsellnlmenloptrnumDerivpillarpkgbuildpkgconfigposteriorprocessxproxypsQuickJSRR6RColorBrewerRcppRcppEigenRcppParallelrlangrstans2scalessfStanHeadersstringistringrtensorAtibbletidyselectunitsutf8vctrsviridisLitewithrwk

Readme and manuals

Help Manual

Help pageTopics
Binned Residual Plotbinned_resid
Create a model data frame for an individual respondentcalc_indiv_frame
Get Posterior Mean of Effective Block Distanceeff_dist
Calculate AUCfastAUC
Filter block data to a radius of a FIPS codelocal_area
Functions for working with neighborhood fitsas.data.frame.nbhd_fit as.matrix.nbhd_fit coef.nbhd_fit fitted.nbhd_fit fixef.nbhd_fit nbhd_fit ranef.nbhd_fit residuals.nbhd_fit summary.nbhd_fit
Fit the Neighborhood Modelneighborhood_model
Plot coefficient estimatesplot.nbhd_fit
Get Posterior Block Inclusion Probabilitiespost_incl
Simulate a neighborhood for a respondentsimulate_neighborhood