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 (≥ 1.0.8.3), 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 |
Documentation:
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