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cff-version: 1.2.0
message: 'To cite package "AdapDiscom" in publications use:'
type: software
license: GPL-3.0-only
title: 'AdapDiscom: Adaptive Sparse Regression for Block Missing Multimodal Data'
version: 1.0.0
doi: 10.32614/CRAN.package.AdapDiscom
abstract: Provides adaptive direct sparse regression for high-dimensional multimodal
  data with heterogeneous missing patterns and measurement errors. 'AdapDISCOM' extends
  the 'DISCOM' framework with modality-specific adaptive weighting to handle varying
  data structures and error magnitudes across blocks. The method supports flexible
  block configurations (any K blocks) and includes robust variants for heavy-tailed
  distributions ('AdapDISCOM'-Huber) and fast implementations for large-scale applications
  (Fast-'AdapDISCOM'). Designed for realistic multimodal scenarios where different
  data sources exhibit distinct missing data patterns and contamination levels. Diakité
  et al. (2025) <https://doi.org/10.48550/arXiv.2508.00120>.
authors:
- family-names: Abdoul Oudouss
  given-names: Diakite
  email: abdouloudoussdiakite@gmail.com
- family-names: Amadou
  given-names: Barry
  email: amadoudiogo.barry@inrs.ca
repository: https://aodiakite.r-universe.dev
repository-code: https://github.com/AODiakite/AdapDiscom
commit: d973fd751608c731471bc909af31db982114d0dd
url: https://doi.org/10.48550/arXiv.2508.00120
date-released: '2025-08-18'
contact:
- family-names: Abdoul Oudouss
  given-names: Diakite
  email: abdouloudoussdiakite@gmail.com