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missoNet  

Missingness in Multi-Task Regression with Network Estimation
View on CRAN: Click here


Download and install missoNet package within the R console
Install from CRAN:
install.packages("missoNet")

Install from Github:
library("remotes")
install_github("cran/missoNet")

Install by package version:
library("remotes")
install_version("missoNet", "1.2.0")



Attach the package and use:
library("missoNet")
Maintained by
Yixiao Zeng
[Scholar Profile | Author Map]
First Published: 2022-10-10
Latest Update: 2023-07-19
Description:
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.
How to cite:
Yixiao Zeng (2022). missoNet: Missingness in Multi-Task Regression with Network Estimation. R package version 1.2.0, https://cran.r-project.org/web/packages/missoNet. Accessed 29 Apr. 2025.
Previous versions and publish date:
1.0.0 (2022-10-10 07:50), 1.1.0 (2023-05-11 10:40)
Other packages that cited missoNet R package
View missoNet citation profile
Other R packages that missoNet depends, imports, suggests or enhances
Complete documentation for missoNet
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