Package: HMTL 0.1.0

HMTL: Heterogeneous Multi-Task Feature Learning

The heterogeneous multi-task feature learning is a data integration method to conduct joint feature selection across multiple related data sets with different distributions. The algorithm can combine different types of learning tasks, including linear regression, Huber regression, adaptive Huber, and logistic regression. The modified version of Bayesian Information Criterion (BIC) is produced to measure the model performance. Package is based on Yuan Zhong, Wei Xu, and Xin Gao (2022) <https://www.fields.utoronto.ca/talk-media/1/53/65/slides.pdf>.

Authors:Yuan Zhong [aut, cre], Wei Xu [aut], Xin Gao [aut]

HMTL_0.1.0.tar.gz
HMTL_0.1.0.zip(r-4.7-any)HMTL_0.1.0.zip(r-4.6-any)HMTL_0.1.0.zip(r-4.5-any)
HMTL_0.1.0.tgz(r-4.6-any)HMTL_0.1.0.tgz(r-4.5-any)
HMTL_0.1.0.tar.gz(r-4.7-any)HMTL_0.1.0.tar.gz(r-4.6-any)
HMTL_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
HMTL/json (API)

# Install 'HMTL' in R:
install.packages('HMTL', repos = c('https://hermitz9.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 275 downloads 5 exports 4 dependencies

Last updated from:f00b438e84. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK113
source / vignettesOK217
linux-releaseOK135
macos-releaseOK177
macos-oldrelOK148
windows-develOK94
windows-releaseOK89
windows-oldrelOK77
wasm-releaseOK85

Exports:MTL_classMTL_heteroMTL_regplot_HMTLSelection_HMTL

Dependencies:latticeMatrixpROCRcpp