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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 (arm64): rDecode_0.1.0.tgz, r-oldrel (arm64): rDecode_0.1.0.tgz, r-release (x86_64): rDecode_0.1.0.tgz, r-oldrel (x86_64): rDecode_0.1.0.tgz |
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