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-4.7-x86_64)nbhdmodel_0.1.0.9000.zip(r-4.7-arm64)nbhdmodel_0.1.0.9000.zip(r-4.6-x86_64)nbhdmodel_0.1.0.9000.zip(r-4.6-arm64)nbhdmodel_0.1.0.9000.zip(r-4.5-x86_64)
nbhdmodel_0.1.0.9000.tgz(r-4.6-x86_64)nbhdmodel_0.1.0.9000.tgz(r-4.6-arm64)nbhdmodel_0.1.0.9000.tgz(r-4.5-x86_64)nbhdmodel_0.1.0.9000.tgz(r-4.5-arm64)
nbhdmodel_0.1.0.9000.tar.gz(r-4.7-arm64)nbhdmodel_0.1.0.9000.tar.gz(r-4.7-x86_64)nbhdmodel_0.1.0.9000.tar.gz(r-4.6-arm64)nbhdmodel_0.1.0.9000.tar.gz(r-4.6-x86_64)
nbhdmodel_0.1.0.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
nbhdmodel/json (API)

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

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

Uses libs:
  • onetbb– Parallelism library for C++
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

onetbbcpp

2.18 score 3 stars 9 exports 73 dependencies

Last updated from:6e7d39e648. Checks:15 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK222
linux-devel-x86_64OK262
source / vignettesOK288
linux-release-arm64OK211
linux-release-x86_64OK245
macos-release-arm64OK158
macos-release-x86_64OK360
macos-oldrel-arm64OK204
macos-oldrel-x86_64OK509
windows-devel-arm64OK300
windows-devel-x86_64OK290
windows-release-arm64OK292
windows-release-x86_64OK248
windows-oldrel-x86_64OK270
wasm-releaseOK225

Exports:binned_residcalc_indiv_framefastAUCfixeflocal_areaneighborhood_modelpost_inclranefsimulate_neighborhood

Dependencies:abindbackportsBHbootcallrcheckmateclassclassIntclicpp11DBIdescdistributionaldplyre1071farverfastmatchgenericsggplot2gluegridExtragtableinlineisobandKernSmoothlabelinglatticelifecyclelme4loomagrittrMASSMatrixmatrixStatsminqanlmenloptrnumDerivotelpillarpkgbuildpkgconfigposteriorprocessxproxypsQuickJSRR6rbibutilsRColorBrewerRcppRcppEigenRcppParallelRdpackreformulasrlangrstans2S7scalessfStanHeadersstringistringrtensorAtibbletidyselectunitsutf8vctrsviridisLitewithrwk

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