Algorithms for solving a self-calibrated l1-regularized quadratic programming problem without parameter tuning. The algorithm, called DECODE, can handle high-dimensional data without cross-validation. It is found useful in high dimensional portfolio selection (see Pun (2018) <https://ssrn.com/abstract=3179569>) and large precision matrix estimation and sparse linear discriminant analysis (see Pun and Hadimaja (2019) <https://ssrn.com/abstract=3422590>).
Version: | 0.1.0 |
Depends: | R (≥ 2.10) |
Imports: | stats |
Published: | 2019-12-18 |
Author: | Chi Seng Pun, Matthew Zakharia Hadimaja |
Maintainer: | Chi Seng Pun <cspun at ntu.edu.sg> |
License: | GPL-2 |
NeedsCompilation: | no |
CRAN checks: | rDecode results |
Reference manual: | rDecode.pdf |
Package source: | rDecode_0.1.0.tar.gz |
Windows binaries: | r-devel: rDecode_0.1.0.zip, r-release: rDecode_0.1.0.zip, r-oldrel: rDecode_0.1.0.zip |
macOS binaries: | r-release: rDecode_0.1.0.tgz, r-oldrel: rDecode_0.1.0.tgz |
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