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alqrfe
View on CRAN: Click
here
Download and install alqrfe package within the R console
Install from CRAN:
install.packages("alqrfe")
Install from Github:
library("remotes")
install_github("cran/alqrfe")
Install by package version:
library("remotes")
install_version("alqrfe", "1.1")
Attach the package and use:
library("alqrfe")
Maintained by
Ian Meneghel Danilevicz
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-09-20
Latest Update: 2022-11-30
Description:
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.
How to cite:
Ian Meneghel Danilevicz (2022). alqrfe: Adaptive Lasso Quantile Regression with Fixed Effects. R package version 1.1, https://cran.r-project.org/web/packages/alqrfe. Accessed 21 Nov. 2024.
Previous versions and publish date:
1.0 (2022-09-20 01:36)
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Other R packages that alqrfe depends,
imports, suggests or enhances
Complete documentation for alqrfe
Functions, R codes and Examples using
the alqrfe R package
Some associated functions: alqrfe-package . bic_hat . clean_data . df_hat . f_den . f_tab . loss_alqr . loss_lqr . loss_qr . loss_qrfe . make_z . mqr . mqr_alpha . optim_alqr . optim_lqr . optim_qr . optim_qrfe . plot_alpha . plot_taus . print.ALQRFE . q_cov . qr . rho_koenker . sgf .
Some associated R codes: LQRFE_main_v2.R . RcppExports.R . Full alqrfe package functions and examples
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