Package: TFRE 0.1.0
TFRE: A Tuning-Free Robust and Efficient Approach to High-Dimensional Regression
Provide functions to estimate the coefficients in high-dimensional linear regressions via a tuning-free and robust approach. The method was published in Wang, L., Peng, B., Bradic, J., Li, R. and Wu, Y. (2020), "A Tuning-free Robust and Efficient Approach to High-dimensional Regression", Journal of the American Statistical Association, 115:532, 1700-1714(JASA’s discussion paper), <doi:10.1080/01621459.2020.1840989>. See also Wang, L., Peng, B., Bradic, J., Li, R. and Wu, Y. (2020), "Rejoinder to “A tuning-free robust and efficient approach to high-dimensional regression". Journal of the American Statistical Association, 115, 1726-1729, <doi:10.1080/01621459.2020.1843865>; Peng, B. and Wang, L. (2015), "An Iterative Coordinate Descent Algorithm for High-Dimensional Nonconvex Penalized Quantile Regression", Journal of Computational and Graphical Statistics, 24:3, 676-694, <doi:10.1080/10618600.2014.913516>; Clémençon, S., Colin, I., and Bellet, A. (2016), "Scaling-up empirical risk minimization: optimization of incomplete u-statistics", The Journal of Machine Learning Research, 17(1):2682–2717; Fan, J. and Li, R. (2001), "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties", Journal of the American Statistical Association, 96:456, 1348-1360, <doi:10.1198/016214501753382273>.
Authors:
TFRE_0.1.0.tar.gz
TFRE_0.1.0.zip(r-4.5)TFRE_0.1.0.zip(r-4.4)TFRE_0.1.0.zip(r-4.3)
TFRE_0.1.0.tgz(r-4.4-x86_64)TFRE_0.1.0.tgz(r-4.4-arm64)TFRE_0.1.0.tgz(r-4.3-x86_64)TFRE_0.1.0.tgz(r-4.3-arm64)
TFRE_0.1.0.tar.gz(r-4.5-noble)TFRE_0.1.0.tar.gz(r-4.4-noble)
TFRE_0.1.0.tgz(r-4.4-emscripten)TFRE_0.1.0.tgz(r-4.3-emscripten)
TFRE.pdf |TFRE.html✨
TFRE/json (API)
# Install 'TFRE' in R: |
install.packages('TFRE', repos = c('https://yunanwu123.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/yunanwu123/tfre/issues
Last updated 10 months agofrom:5dcee11988. Checks:OK: 1 NOTE: 8. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 02 2024 |
R-4.5-win-x86_64 | NOTE | Nov 02 2024 |
R-4.5-linux-x86_64 | NOTE | Nov 02 2024 |
R-4.4-win-x86_64 | NOTE | Nov 02 2024 |
R-4.4-mac-x86_64 | NOTE | Nov 02 2024 |
R-4.4-mac-aarch64 | NOTE | Nov 02 2024 |
R-4.3-win-x86_64 | NOTE | Nov 02 2024 |
R-4.3-mac-x86_64 | NOTE | Nov 02 2024 |
R-4.3-mac-aarch64 | NOTE | Nov 02 2024 |
Exports:coef.TFREest_lambdaplot.TFREpredict.TFRETFRE
Dependencies:RcppRcppEigenRcppParallel