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

Documentation:

Reference manual:

rDecode.pdf

Downloads:

Package source:	rDecode_0.1.0.tar.gz
Windows binaries:	r-prerel: rDecode_0.1.0.zip, r-release: rDecode_0.1.0.zip, r-oldrel: rDecode_0.1.0.zip
macOS binaries:	r-prerel (arm64): rDecode_0.1.0.tgz, r-release (arm64): rDecode_0.1.0.tgz, r-oldrel (arm64): rDecode_0.1.0.tgz, r-prerel (x86_64): rDecode_0.1.0.tgz, r-release (x86_64): rDecode_0.1.0.tgz

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