Package: alqrfe 1.1
alqrfe: Adaptive Lasso Quantile Regression with Fixed Effects
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.
Authors:
alqrfe_1.1.tar.gz
alqrfe_1.1.zip(r-4.5)alqrfe_1.1.zip(r-4.4)alqrfe_1.1.zip(r-4.3)
alqrfe_1.1.tgz(r-4.4-x86_64)alqrfe_1.1.tgz(r-4.4-arm64)alqrfe_1.1.tgz(r-4.3-x86_64)alqrfe_1.1.tgz(r-4.3-arm64)
alqrfe_1.1.tar.gz(r-4.5-noble)alqrfe_1.1.tar.gz(r-4.4-noble)
alqrfe_1.1.tgz(r-4.4-emscripten)alqrfe_1.1.tgz(r-4.3-emscripten)
alqrfe.pdf |alqrfe.html✨
alqrfe/json (API)
| # Install 'alqrfe' in R: |
| install.packages('alqrfe', repos = c('https://iandanilevicz.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 2 years agofrom:442be00a9b. Checks:OK: 1 NOTE: 8. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 19 2024 |
| R-4.5-win-x86_64 | NOTE | Nov 19 2024 |
| R-4.5-linux-x86_64 | NOTE | Nov 19 2024 |
| R-4.4-win-x86_64 | NOTE | Nov 19 2024 |
| R-4.4-mac-x86_64 | NOTE | Nov 19 2024 |
| R-4.4-mac-aarch64 | NOTE | Nov 19 2024 |
| R-4.3-win-x86_64 | NOTE | Nov 19 2024 |
| R-4.3-mac-x86_64 | NOTE | Nov 19 2024 |
| R-4.3-mac-aarch64 | NOTE | Nov 19 2024 |
Exports:bic_hatclean_datadf_hatf_denf_tabloss_alqrloss_lqrloss_qrloss_qrfemake_zmqrmqr_alphaoptim_alqroptim_lqroptim_qroptim_qrfeplot_alphaplot_tausprint.ALQRFEq_covqrrho_koenkersgf
Dependencies:MASSRcppRcppArmadillo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Adaptive Lasso Quantile Regression with Fixed Effects | alqrfe-package alqrfe |
| Bayesian Information Criteria | bic_hat |
| Clean missings | clean_data |
| degrees of fredom | df_hat |
| Kernel density | f_den |
| Tabular function | f_tab |
| Loss adaptive lasso quantile regression with fixed effects | loss_alqr |
| Loss lasso quantile regression with fixed effects | loss_lqr |
| Loss quantile regression | loss_qr |
| Loss quantile regression with fixed effects | loss_qrfe |
| Incident matrix Z | make_z |
| multiple penalized quantile regression | mqr |
| multiple penalized quantile regression - alpha | mqr_alpha |
| optim adaptive lasso quantile regression with fixed effects | optim_alqr |
| optim lasso quantile regression with fixed effects | optim_lqr |
| optim quantile regression | optim_qr |
| optim quantile regression with fixed effects | optim_qrfe |
| plot multiple penalized quantile regression - alpha | plot_alpha |
| plot multiple penalized quantile regression | plot_taus |
| Print an ALQRFE | print.ALQRFE |
| Covariance | q_cov |
| quantile regression | qr |
| Rho Koenker | rho_koenker |
| Identify significance | sgf |
