Package: cbq 0.2.0.4

Xiao Lu

cbq: Conditional Binary Quantile Models

Estimates conditional binary quantile models developed by Lu (2020) <doi:10.1017/pan.2019.29>. The estimation procedure is implemented based on Markov chain Monte Carlo methods.

Authors:Xiao Lu [aut, cre]

cbq_0.2.0.4.tar.gz
cbq_0.2.0.4.tar.gz(r-4.7-arm64)cbq_0.2.0.4.tar.gz(r-4.7-x86_64)cbq_0.2.0.4.tar.gz(r-4.6-arm64)cbq_0.2.0.4.tar.gz(r-4.6-x86_64)
manual.pdf |manual.html✨
card.svg |card.png
cbq/json (API)

# Install 'cbq' in R:
install.packages('cbq', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

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

cpp

1.70 score 2 scripts 307 downloads 10 exports 48 dependencies

Last updated from:50ac7dbf33. Checks:5 OK, 1 FAIL. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK844
linux-devel-x86_64OK861
source / vignettesOK760
linux-release-arm64OK861
linux-release-x86_64OK869
wasm-releaseFAIL156

Exports:cbqdaldpaldplot_coef.cbqplot_trace.cbqprint_coef.cbqprint_mcmc.cbqprint_text.cbqqaldrald

Dependencies:abindbackportsBHcallrcheckmateclicpp11descdistributionalfarverFormulagenericsggplot2gluegridExtragtableinlineisobandlabelinglifecycleloomagrittrmatrixStatsnumDerivpillarpkgbuildpkgconfigposteriorprocessxpsQuickJSRR6RColorBrewerRcppRcppEigenRcppParallelrlangrstanrstantoolsS7scalesStanHeaderstensorAtibbleutf8vctrsviridisLitewithr