Type: Package
Package: missoNet
Title: Joint Sparse Regression & Network Learning with Missing Data
Version: 1.5.1
Date: 2025-09-01
Authors@R: c(
    person("Yixiao", "Zeng", , "yixiao.zeng@mail.mcgill.ca", role = c("aut", "cre", "cph")),
    person("Celia", "Greenwood", , "celia.greenwood@mcgill.ca", role = c("ths", "aut"))
  )
Maintainer: Yixiao Zeng <yixiao.zeng@mail.mcgill.ca>
Description: Simultaneously estimates sparse regression coefficients and
    response network structure in multivariate models with missing data.
    Unlike traditional approaches requiring imputation, handles
    missingness natively through unbiased estimating equations (MCAR/MAR
    compatible). Employs dual L1 regularization with automated selection
    via cross-validation or information criteria. Includes parallel
    computation, warm starts, adaptive grids, publication-ready
    visualizations, and prediction methods.  Ideal for genomics,
    neuroimaging, and multi-trait studies with incomplete high-dimensional
    outcomes. See Zeng et al. (2025) <doi:10.48550/arXiv.2507.05990>.
License: GPL-2
URL: https://github.com/yixiao-zeng/missoNet,
        https://arxiv.org/abs/2507.05990
BugReports: https://github.com/yixiao-zeng/missoNet/issues
Depends: R (>= 3.6.0)
Imports: circlize (>= 0.4.15), ComplexHeatmap, glassoFast (>= 1.0.1),
        graphics, grid, mvtnorm (>= 1.2.3), pbapply (>= 1.7.2), Rcpp
        (>= 1.0.9), scatterplot3d (>= 0.3.44), stats, utils
Suggests: ggplot2, glasso, gridExtra, igraph, knitr, parallel,
        RColorBrewer, reshape2, rmarkdown
LinkingTo: Rcpp, RcppArmadillo
VignetteBuilder: knitr
biocViews:
ByteCompile: true
Encoding: UTF-8
NeedsCompilation: yes
RoxygenNote: 7.3.2
Packaged: 2025-09-02 20:23:05 UTC; yixiao
Author: Yixiao Zeng [aut, cre, cph],
  Celia Greenwood [ths, aut]
Repository: CRAN
Date/Publication: 2025-09-02 20:50:07 UTC
Built: R 4.6.0; x86_64-w64-mingw32; 2026-04-03 01:47:57 UTC; windows
Archs: x64
