R-universe - mk314199 (mk314199)

pcalg - Methods for Graphical Models and Causal Inference

Functions for causal structure learning and causal inference using graphical models. The main algorithms for causal structure learning are PC (for observational data without hidden variables), FCI and RFCI (for observational data with hidden variables), and GIES (for a mix of data from observational studies (i.e. observational data) and data from experiments involving interventions (i.e. interventional data) without hidden variables). For causal inference the IDA algorithm, the Generalized Backdoor Criterion (GBC), the Generalized Adjustment Criterion (GAC) and some related functions are implemented. Functions for incorporating background knowledge are provided.

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openblascpp

8.51 score 17 dependents 784 scripts 4.0k downloads

unifDAG - Uniform Sampling of Directed Acyclic Graphs

Uniform sampling of Directed Acyclic Graphs (DAG) using exact enumeration by relating each DAG to a sequence of outpoints (nodes with no incoming edges) and then to a composition of integers as suggested by Kuipers, J. and Moffa, G. (2015) <doi:10.1007/s11222-013-9428-y>.

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2.00 score 5 scripts 206 downloads