missoNet: Missingness in Multi-Task Regression with Network Estimation

Efficient procedures for fitting conditional graphical lasso models that link a set of predictor variables to a set of response variables (or tasks), even when the response data may contain missing values. 'missoNet' simultaneously estimates the predictor coefficients for all tasks by leveraging information from one another, in order to provide more accurate predictions in comparison to modeling them individually. Additionally, 'missoNet' estimates the response network structure influenced by conditioning predictor variables using a L1-regularized conditional Gaussian graphical model. Unlike most penalized multi-task regression methods (e.g., MRCE), 'missoNet' is capable of obtaining estimates even when the response data is corrupted by missing values. The method automatically enjoys the theoretical and computational benefits of convexity, and returns solutions that are comparable to the estimates obtained without missingness.

Version: 1.2.0
Imports: circlize (≥ 0.4.14), ComplexHeatmap, glasso (≥ 1.11), mvtnorm (≥ 1.1.3), pbapply (≥ 1.5.0), Rcpp (≥, scatterplot3d (≥ 0.3.41)
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown
Published: 2023-07-19
DOI: 10.32614/CRAN.package.missoNet
Author: Yixiao Zeng [aut, cre, cph], Celia Greenwood [ths, aut], Archer Yang [ths, aut]
Maintainer: Yixiao Zeng <yixiao.zeng at mail.mcgill.ca>
BugReports: https://github.com/yixiao-zeng/missoNet/issues
License: GPL-2
URL: https://github.com/yixiao-zeng/missoNet
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: missoNet results


Reference manual: missoNet.pdf
Vignettes: An Introduction to missoNet


Package source: missoNet_1.2.0.tar.gz
Windows binaries: r-devel: missoNet_1.2.0.zip, r-release: missoNet_1.2.0.zip, r-oldrel: missoNet_1.2.0.zip
macOS binaries: r-release (arm64): missoNet_1.2.0.tgz, r-oldrel (arm64): missoNet_1.2.0.tgz, r-release (x86_64): missoNet_1.2.0.tgz, r-oldrel (x86_64): missoNet_1.2.0.tgz
Old sources: missoNet archive


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