Joint Sparse Regression & Network Learning with Missing Data R package missoNet version 1.5.1 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>. Software 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 LinkingTo: Rcpp, RcppArmadillo Suggests: ggplot2, glasso, gridExtra, igraph, knitr, parallel, RColorBrewer, reshape2, rmarkdown Yixiao Zeng <yixiao.zeng@mail.mcgill.ca> Comprehensive R Archive Network (CRAN) Yixiao Zeng [aut, cre, cph], Celia Greenwood [ths, aut] GPL-2 2025-09-02 application/tgz https://CRAN.R-project.org/package=missoNet doi:10.32614/CRAN.package.missoNet