Package: missoNet 1.5.1

Yixiao Zeng

missoNet: Joint Sparse Regression & Network Learning with Missing Data

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>.

Authors:Yixiao Zeng [aut, cre, cph], Celia Greenwood [ths, aut]

missoNet_1.5.1.tar.gz
missoNet_1.5.1.zip(r-4.7)missoNet_1.5.1.zip(r-4.6)missoNet_1.5.1.zip(r-4.5)
missoNet_1.5.1.tgz(r-4.6-x86_64)missoNet_1.5.1.tgz(r-4.6-arm64)missoNet_1.5.1.tgz(r-4.5-x86_64)missoNet_1.5.1.tgz(r-4.5-arm64)
missoNet_1.5.1.tar.gz(r-4.7-arm64)missoNet_1.5.1.tar.gz(r-4.7-x86_64)missoNet_1.5.1.tar.gz(r-4.6-arm64)missoNet_1.5.1.tar.gz(r-4.6-x86_64)
missoNet_1.5.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
missoNet/json (API)

# Install 'missoNet' in R:
install.packages('missoNet', repos = c('https://yixiao-zeng.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/yixiao-zeng/missonet/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

On CRAN:

Conda:

conditional-graphical-lassomissing-datamulti-task-regressionopenblascppopenmp

4.59 score 2 stars 13 scripts 338 downloads 3 exports 28 dependencies

Last updated from:4a4a114d57. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK192
linux-devel-x86_64OK181
source / vignettesOK334
linux-release-arm64OK229
linux-release-x86_64OK198
macos-release-arm64OK232
macos-release-x86_64OK304
macos-oldrel-arm64OK168
macos-oldrel-x86_64OK562
windows-develOK186
windows-releaseOK219
windows-oldrelOK165
wasm-releaseOK180

Exports:cv.missoNetgenerateDatamissoNet

Dependencies:BiocGenericscirclizeclueclustercodetoolscolorspaceComplexHeatmapcrayondigestdoParallelforeachgenericsGetoptLongglassoFastGlobalOptionsIRangesiteratorsmatrixStatsmvtnormpbapplypngRColorBrewerRcppRcppArmadillorjsonS4Vectorsscatterplot3dshape

Case Study: Genomic Data Analysis with missoNet
Introduction | Scientific Context | Data Generation and Preparation | Simulating Realistic Genomic Data | Exploratory Data Analysis | Missing Data Patterns | Methylation Correlation Structure | Model Fitting | Initial Parameter Selection | Cross-Validation | Model Comparison | Results Interpretation | Identified meQTLs | CpG Network Analysis | SNP-CpG-Network Integration | Model Validation | Prediction Performance | Stability Assessment | Biological Interpretation | Gene Set Enrichment Analysis (Simulated) | Reporting and Export | Summary Report | Conclusions
Rendered frommissoNet-case-study.Rmdusingknitr::rmarkdown

Last update: 2025-09-02
Started: 2025-09-02

Cross-Validation and Advanced Features in missoNet
Introduction | Cross-Validation Framework | Basic Cross-Validation | Understanding the 1-SE Rule | Visualizing CV Results | CV Error Analysis | Parallel Processing | Setting Up Parallel CV | Advanced Parameter Tuning | Adaptive Grid Search | Custom Penalty Factors | Multi-Resolution Lambda Grids | Convergence Diagnostics | Monitoring Convergence | Handling Convergence Issues | Relaxed Network Estimation | De-biased Precision Matrix | Model Selection Strategies | Nested Cross-Validation | Best Practices Summary | Recommended Workflow | Performance Tips
Rendered frommissoNet-cross-validation.Rmdusingknitr::rmarkdown

Last update: 2025-09-02
Started: 2025-09-02

Getting Started with missoNet
Introduction | The Model | Installation | Quick Start | Generate Example Data | Basic Model Fitting | Visualization | Making Predictions | Understanding Missing Data Mechanisms | Model Selection Strategies | Comparing Selection Criteria | Custom Lambda Grids | Working with Real Data Patterns | Handling Variable Missing Rates | Incorporating Prior Knowledge | Tips for Optimal Performance | Standardization | Convergence Settings | Summary
Rendered frommissoNet-introduction.Rmdusingknitr::rmarkdown

Last update: 2025-09-02
Started: 2025-09-02