Package: lqmm 1.5.8
lqmm: Linear Quantile Mixed Models
Functions to fit quantile regression models for hierarchical data (2-level nested designs) as described in Geraci and Bottai (2014, Statistics and Computing) <doi:10.1007/s11222-013-9381-9>. A vignette is given in Geraci (2014, Journal of Statistical Software) <doi:10.18637/jss.v057.i13> and included in the package documents. The packages also provides functions to fit quantile models for independent data and for count responses.
Authors:
lqmm_1.5.8.tar.gz
lqmm_1.5.8.tar.gz(r-4.5-noble)lqmm_1.5.8.tar.gz(r-4.4-noble)
lqmm_1.5.8.tgz(r-4.4-emscripten)lqmm_1.5.8.tgz(r-4.3-emscripten)
lqmm.pdf |lqmm.html✨
lqmm/json (API)
NEWS
# Install 'lqmm' in R: |
install.packages('lqmm', 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 3 years agofrom:1e67c5514e. Checks:OK: 2. Indexed: no.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 29 2024 |
R-4.5-linux-x86_64 | OK | Nov 29 2024 |
Exports:addnoiseallVarsRecasOneFormulabandwidth.rqbootboot.lqmboot.lqmmC_gradientShC_gradientSiC_ll_hcoef.lqmcoef.lqm.countscoef.lqmmcovHandlingcreateLaguerredalerrorHandlingextractAllextractBootextractBoot.boot.lqmmF.lqmgauss.quadgauss.quad.probgradientSiinvTfuninvvarALis.positive.definitelogLik.lqmlogLik.lqmmloglik.sloglik.tloglikilqmlqm.countslqm.fit.gslqmControllqmmlqmm.fit.dflqmm.fit.gslqmmControlmake.positive.definitemeanALmleALpalpermutationspredict.lqmpredict.lqm.countspredict.lqmmpredintpredint.lqmmprint.lqmprint.lqm.countsprint.lqmmprint.summary.lqmprint.summary.lqmmqalquadralranefranef.lqmmresiduals.lqmresiduals.lqm.countsresiduals.lqmmscore.alsummary.boot.lqmsummary.boot.lqmmsummary.lqmsummary.lqmmswitch_checkTfuntheta.z.dimvarALVarCorrVarCorr.lqmm
Dependencies:latticenlmeSparseGrid