Quantile regression with fixed effects solves longitudinal data, considering the individual intercepts as fixed effects. The parametric set of this type of problem used to be huge. Thus penalized methods such as Lasso are currently applied. Adaptive Lasso presents oracle proprieties, which include Gaussianity and correct model selection. Bayesian information criteria (BIC) estimates the optimal tuning parameter lambda. Plot tools are also available.
Version: 1.2 Depends: R (≥ 4.4.0) Imports: Rcpp (≥ 1.0.5), MASS (≥ 7.3-49), stats LinkingTo: Rcpp, RcppArmadillo Published: 2025-07-03 DOI: 10.32614/CRAN.package.alqrfe Author: Ian Meneghel Danilevicz [aut, cre], Pascal Bondon [aut], Valderio A. Reisen [aut] Maintainer: Ian Meneghel Danilevicz <iandanilevicz at gmail.com> License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] NeedsCompilation: yes CRAN checks: alqrfe results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=alqrfe to link to this page.
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