Type: Package
Package: missoNet
Title: Missingness in Multi-Task Regression with Network Estimation
Version: 1.0.0
Date: 2022-10-06
Authors@R: c(
    person("Yixiao", "Zeng", email = "yixiao.zeng@mail.mcgill.ca", role = c("aut", "cre", "cph")),
    person("Celia", "Greenwood", email = "celia.greenwood@mcgill.ca", role = c("ths", "aut")),
    person("Archer", "Yang", email = "archer.yang@mcgill.ca", role = c("ths", "aut")))
Maintainer: Yixiao Zeng <yixiao.zeng@mail.mcgill.ca>
Description: 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.
License: GPL-2
URL: https://github.com/yixiao-zeng/missoNet
BugReports: https://github.com/yixiao-zeng/missoNet/issues
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)
Suggests: knitr, rmarkdown
LinkingTo: Rcpp, RcppArmadillo
VignetteBuilder: knitr
biocViews:
Encoding: UTF-8
RoxygenNote: 7.2.1
NeedsCompilation: yes
Packaged: 2022-10-06 23:08:03 UTC; yixiao
Author: Yixiao Zeng [aut, cre, cph],
  Celia Greenwood [ths, aut],
  Archer Yang [ths, aut]
Repository: CRAN
Date/Publication: 2022-10-10 05:50:05 UTC
Built: R 4.1.3; x86_64-w64-mingw32; 2023-04-17 16:26:26 UTC; windows
Archs: i386, x64
