customizedTraining
1.3Customized Training for Lasso and Elastic-Net Regularized Generalized Linear Models
Overview
Customized training is a simple technique for transductive learning, when the test covariates are known at the time of training. The method identifies a subset of the training set to serve as the training set for each of a few identified subsets in the training set. This package implements customized training for the glmnet() and cv.glmnet() functions.
Install
Health
- OK2026-08-0513 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-08-0112 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- References docs
- 8%
Downloads
Dependencies
Nothing depends on this yet.
Code & Tests
Datasets
People & History
4 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- 1.3Latest
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- RR 4.0.0 released · 2020-04-24
- RR 3.6.0 released · 2019-04-26
- 1.22019-01-29 · diff ↗
- RR 3.5.0 released · 2018-04-23
- RR 3.4.0 released · 2017-04-21
- 1.12016-09-14 · diff ↗
- RR 3.3.0 released · 2016-05-03
- 1.02016-02-12
- RR 3.2.0 released · 2015-04-16
Package metadata
- First published
- 2016-02-12
- Total releases
- 4 / 10 yrs
- License
- GPL-2 OSI
- Bundled data
- 25 KB / 1 file
- Download size
- 40 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
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Run in R for the authors' preferred citation:
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