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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>).
Getting started
Browse package contents
Package details |
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| Author | Chi Seng Pun, Matthew Zakharia Hadimaja |
| Maintainer | Chi Seng Pun <cspun@ntu.edu.sg> |
| License | GPL-2 |
| Version | 0.1.0 |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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