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Software application profile: tpc and micd—R packages for causal discovery with incomplete cohort data.
- Source :
-
International Journal of Epidemiology . Oct2024, Vol. 53 Issue 5, p1-5. 5p. - Publication Year :
- 2024
-
Abstract
- Motivation The Peter Clark (PC) algorithm is a popular causal discovery method to learn causal graphs in a data-driven way. Until recently, existing PC algorithm implementations in R had important limitations regarding missing values, temporal structure or mixed measurement scales (categorical/continuous), which are all common features of cohort data. The new R packages presented here, micd and tpc , fill these gaps. Implementation micd and tpc packages are R packages. General features The micd package provides add-on functionality for dealing with missing values to the existing pcalg R package, including methods for multiple imputations relying on the Missing At Random assumption. Also, micd allows for mixed measurement scales assuming conditional Gaussianity. The tpc package efficiently exploits temporal information in a way that results in a more informative output that is less prone to statistical errors. Availability The tpc and micd packages are freely available on the Comprehensive R Archive Network (CRAN). Their source code is also available on GitHub (https://github.com/bips-hb/micd ; https://github.com/bips-hb/tpc). [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 03005771
- Volume :
- 53
- Issue :
- 5
- Database :
- Academic Search Index
- Journal :
- International Journal of Epidemiology
- Publication Type :
- Academic Journal
- Accession number :
- 180217972
- Full Text :
- https://doi.org/10.1093/ije/dyae113