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cff-version: 1.2.0
message: 'To cite package "mantar" in publications use:'
type: software
license: GPL-3.0-or-later
title: 'mantar: Missingness Alleviation for Network Analysis'
version: 0.3.0
doi: 10.32614/CRAN.package.mantar
abstract: Provides functionality for estimating cross-sectional network structures
  representing partial correlations while accounting for missing data. Networks are
  estimated via neighborhood selection or regularization, with model selection guided
  by information criteria. Missing data can be handled primarily via multiple imputation
  or a maximum likelihood-based approach, as demonstrated by Nehler and Schultze (2025)
  <https://doi.org/10.1080/00273171.2025.2503833> and Nehler and Schultze (2026) <https://doi.org/10.1037/met0000828>.
  Deletion-based approaches are also available but play a secondary role.
authors:
- family-names: Nehler
  given-names: Kai Jannik
  email: nehler@psych.uni-frankfurt.de
  orcid: https://orcid.org/0000-0003-3764-761X
repository: https://kai-nehler.r-universe.dev
repository-code: https://github.com/kai-nehler/mantar
commit: 3b800d68ec2877bd1140471a04ff1b8c1447aca8
url: https://github.com/kai-nehler/mantar
date-released: '2026-05-26'
contact:
- family-names: Nehler
  given-names: Kai Jannik
  email: nehler@psych.uni-frankfurt.de
  orcid: https://orcid.org/0000-0003-3764-761X