| Name : R-miic
| |
| Version : 2.0.3
| Vendor : obs://build_opensuse_org/devel:languages:R
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| Release : lp154.1.1
| Date : 2024-09-23 13:05:36
|
| Group : Development/Libraries/Other
| Source RPM : R-miic-2.0.3-lp154.1.1.src.rpm
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| Size : 0.85 MB
| |
| Packager : https://www_suse_com/
| |
| Summary : Learning Causal or Non-Causal Graphical Models Using Information Theory
|
Description :
Multivariate Information-based Inductive Causation, better known by its
acronym MIIC, is a causal discovery method, based on information theory
principles, which learns a large class of causal or non-causal
graphical models from purely observational data, while including the
effects of unobserved latent variables. Starting from a complete graph,
the method iteratively removes dispensable edges, by uncovering
significant information contributions from indirect paths, and assesses
edge-specific confidences from randomization of available data. The
remaining edges are then oriented based on the signature of causality
in observational data. The recent more interpretable MIIC extension
(iMIIC) further distinguishes genuine causes from putative and latent
causal effects, while scaling to very large datasets (hundreds of
thousands of samples). Since the version 2.0, MIIC also includes a
temporal mode (tMIIC) to learn temporal causal graphs from stationary
time series data. MIIC has been applied to a wide range of biological
and biomedical data, such as single cell gene expression data, genomic
alterations in tumors, live-cell time-lapse imaging data
(CausalXtract), as well as medical records of patients. MIIC brings
unique insights based on causal interpretation and could be used in a
broad range of other data science domains (technology, climatology,
economy, ...). For more information, you can refer to: Simon et al.,
eLife 2024, < doi:10.1101/2024.02.06.579177>, Ribeiro-Dantas et al.,
iScience 2024, < doi:10.1016/j.isci.2024.109736>, Cabeli et al., NeurIPS
2021, < https://why21.causalai.net/papers/WHY21_24.pdf>, Cabeli et al.,
Comput. Biol. 2020, < doi:10.1371/journal.pcbi.1007866>, Li et al.,
NeurIPS 2019,
< https://papers.nips.cc/paper/9573-constraint-based-causal-structure-learning-with-consistent-separating-sets>,
Verny et al., PLoS Comput. Biol. 2017,
< doi:10.1371/journal.pcbi.1005662>, Affeldt et al., UAI 2015,
< https://auai.org/uai2015/proceedings/papers/293.pdf>. Changes from the
previous 1.5.3 release on CRAN are available at
< https://github.com/miicTeam/miic_R_package/blob/master/NEWS.md>.
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RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/15.4/x86_64 |
