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:Marco Geraci

lqmm_1.5.8.tar.gz
lqmm_1.5.8.zip(r-4.7)lqmm_1.5.8.zip(r-4.6)lqmm_1.5.8.zip(r-4.5)
lqmm_1.5.8.tgz(r-4.6-x86_64)lqmm_1.5.8.tgz(r-4.6-arm64)lqmm_1.5.8.tgz(r-4.5-x86_64)lqmm_1.5.8.tgz(r-4.5-arm64)
lqmm_1.5.8.tar.gz(r-4.7-arm64)lqmm_1.5.8.tar.gz(r-4.7-x86_64)lqmm_1.5.8.tar.gz(r-4.6-arm64)lqmm_1.5.8.tar.gz(r-4.6-x86_64)
lqmm_1.5.8.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
lqmm/json (API)
NEWS

# Install 'lqmm' in R:
install.packages('lqmm', repos = c('https://marco-geraci.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • openblas– Optimized BLAS
Datasets:
  • labor - Labor Pain Data
  • Orthodont - Growth curve data on an orthdontic measurement

On CRAN:

Conda:

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

openblas

5.06 score 5 packages 131 scripts 5.8k downloads 10 mentions 74 exports 3 dependencies

Last updated from:1e67c5514e. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK102
linux-devel-x86_64OK113
source / vignettesOK155
linux-release-arm64OK110
linux-release-x86_64OK105
macos-release-arm64OK104
macos-release-x86_64OK152
macos-oldrel-arm64OK72
macos-oldrel-x86_64OK137
windows-develOK91
windows-releaseOK90
windows-oldrelOK92
wasm-releaseOK92

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

Readme and manuals

Help Manual

Help pageTopics
Linear Quantile Models and Linear Quantile Mixed Modelslqmm-package
Bootstrap functions for LQM and LQMMboot boot.lqm boot.lqmm
Extract LQM Coefficientscoef.lqm coef.lqm.counts
Extract LQMM Coefficientscoef.lqmm
Variance-Covariance MatrixcovHandling
The Asymmetric Laplace Distributiondal pal qal ral
Extract Fixed and Random Bootstrapped ParametersextractBoot extractBoot.boot.lqmm
Gaussian Quadraturegauss.quad
Gaussian Quadraturegauss.quad.prob
Test for Positive Definitenessis.positive.definite
Labor Pain Datalabor
Extract Log-LikelihoodlogLik.lqm
Extract Log-LikelihoodlogLik.lqmm
Fitting Linear Quantile Modelslqm
Quantile Regression for Countslqm.counts
Quantile Regression Fitting by Gradient Searchlqm.fit.gs
Control parameters for lqm estimationlqmControl
Fitting Linear Quantile Mixed Modelslqmm
Linear Quantile Mixed Models Fitting by Derivative-Free Optimizationlqmm.fit.df
Linear Quantile Mixed Models Fitting by Gradient Searchlqmm.fit.gs
Control parameters for lqmm estimationlqmmControl
Compute Nearest Positive Definite Matrixmake.positive.definite
Functions for Asymmetric Laplace Distribution ParametersinvvarAL meanAL varAL
Maximum Likelihood Estimation of Asymmetric Laplace DistributionmleAL
Growth curve data on an orthdontic measurementOrthodont
Predictions from LQM Objectspredict.lqm predict.lqm.counts
Predictions from an 'lqmm' Objectpredict.lqmm predint predint.lqmm
Print LQM Objectsprint.lqm print.lqm.counts
Print an 'lqmm' Objectprint.lqmm
Print an 'lqm' Summary Objectprint.summary.lqm
Print an 'lqmm' Summary Objectprint.summary.lqmm
Extract Random Effectsranef ranef.lqmm
Residuals from an LQM Objectsresiduals.lqm residuals.lqm.counts
Residuals from an 'lqmm' Objectresiduals.lqmm
Summary for a 'boot.lqm' Objectsummary.boot.lqm
Summary for a 'boot.lqmm' Objectsummary.boot.lqmm
Summary for an 'lqm' Objectsummary.lqm
Summary for an 'lqmm' Objectsummary.lqmm
Extract Variance-Covariance MatrixVarCorr VarCorr.lqmm