missoNet: Missingness in Multi-Task Regression with Network Estimation
Efficient procedures for fitting the conditional graphical
lasso models linking a set of predictor variables to a set of response
variables (or tasks), 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. Meanwhile, 'missoNet' is able to estimate the response
network structure influenced by conditioning predictor variables in a
L1-regularized conditional Gaussian graphical model. In contrast to
most penalized multi-task regression (conditional graphical lasso)
methods, 'missoNet' has the capability 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/close to the
estimates without any missing values. The package also includes
auxiliary functions for data simulation, goodness-of-fit evaluation,
regularization parameter tuning, and visualization of results, as well
as predictions in new data.
| Version: |
1.0.0 |
| Imports: |
circlize (≥ 0.4.14), ComplexHeatmap, glasso (≥ 1.11), glmnet (≥ 4.1.4), mvtnorm (≥ 1.1.3), pbapply (≥ 1.5.0), Rcpp (≥
1.0.8.3), scatterplot3d (≥ 0.3.41) |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Suggests: |
knitr, rmarkdown |
| Published: |
2022-10-10 |
| 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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