rDecode: Descent-Based Calibrated Optimal Direct Estimation

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-devel-gcc8: rDecode_0.1.0.zip, r-release: rDecode_0.1.0.zip, r-oldrel: rDecode_0.1.0.zip
OS X binaries: r-release: rDecode_0.1.0.tgz, r-oldrel: rDecode_0.1.0.tgz


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