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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>.
| Version: | 0.1.0 |
| Imports: | Rcpp (≥ 1.0.9), RcppParallel |
| LinkingTo: | Rcpp, RcppEigen, RcppParallel |
| Published: | 2024-01-31 |
| Author: | Yunan Wu [aut, cre, cph], Lan Wang [aut] |
| Maintainer: | Yunan Wu <yunan.wu at utdallas.edu> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| CRAN checks: | TFRE results |
| Reference manual: | TFRE.pdf |
| Package source: | TFRE_0.1.0.tar.gz |
| Windows binaries: | r-devel: TFRE_0.1.0.zip, r-release: TFRE_0.1.0.zip, r-oldrel: TFRE_0.1.0.zip |
| macOS binaries: | r-release (arm64): TFRE_0.1.0.tgz, r-oldrel (arm64): TFRE_0.1.0.tgz, r-release (x86_64): TFRE_0.1.0.tgz, r-oldrel (x86_64): TFRE_0.1.0.tgz |
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