Package: miic 2.0.3
Franck Simon
miic: Learning Causal or Non-Causal Graphical Models Using Information Theory
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>.
Authors:
miic_2.0.3.tar.gz
miic_2.0.3.tar.gz(r-4.5-noble)miic_2.0.3.tar.gz(r-4.4-noble)
miic_2.0.3.tgz(r-4.4-emscripten)miic_2.0.3.tgz(r-4.3-emscripten)
miic.pdf |miic.html✨
miic/json (API)
# Install 'miic' in R: |
install.packages('miic', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/miicteam/miic_r_package/issues
- cosmicCancer - Genomic and ploidy alterations in breast tumors
- cosmicCancer_stateOrder - Genomic and ploidy alterations in breast tumors
- covidCases - Covid cases
- hematoData - Early blood development: single cell binary gene expression data
Last updated 3 months agofrom:577d2b135e. Checks:OK: 2. Indexed: no.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 17 2024 |
R-4.5-linux-x86_64 | OK | Nov 17 2024 |
Exports:computeMutualInfocomputeThreePointInfodiscretizeMDLdiscretizeMutualestimateTemporalDynamicexportmiicwriteCytoscapeNetworkwriteCytoscapeStyle
Dependencies:clicolorspacefarvergluelabelinglifecycleMASSmunsellppcorR6RColorBrewerRcpprlangscalesviridisLite
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Compute (conditional) mutual information | computeMutualInfo |
Compute (conditional) three-point information | computeThreePointInfo |
Genomic and ploidy alterations in breast tumors | cosmicCancer |
Genomic and ploidy alterations in breast tumors | cosmicCancer_stateOrder |
Covid cases | covidCases |
Discretize a real valued distribution | discretizeMDL |
Iterative dynamic programming for (conditional) mutual information through optimized discretization. | discretizeMutual |
Estimation of the temporal causal discovery parameters | estimateTemporalDynamic |
Export miic result for plotting (with igraph) | export |
Early blood development: single cell binary gene expression data | hematoData |
MIIC, causal network learning algorithm including latent variables | miic |
Basic plot function of a miic network inference result | plot.miic |
Basic plot function of a temporal miic (tmiic) network inference result | plot.tmiic |
GraphML converting function for miic graph | writeCytoscapeNetwork |
Style writing function for the miic network | writeCytoscapeStyle |