causalDisco - Tools for Causal Discovery on Observational Data
Tools for causal structure learning from observational data, with emphasis on temporally ordered variables. The package implements the Temporal Peter–Clark (TPC) algorithm (Petersen, Osler & Ekstrøm, 2021; <doi:10.1093/aje/kwab087>), the Temporal Greedy Equivalence Search (TGES) algorithm (Larsen, Ekstrøm & Petersen, 2025; <doi:10.48550/arXiv.2502.06232>) and Temporal Fast Causal Inference (TFCI). It provides a unified framework for specifying background knowledge, which can be incorporated into the implemented algorithms from the R packages 'bnlearn' (Scutari, 2010; <doi:10.18637/jss.v035.i03>) and 'pcalg' (Kalish et al., 2012; <doi:10.18637/jss.v047.i11>), as well as the Java library 'Tetrad' (Scheines et al., 1998; <doi:10.1207/s15327906mbr3301_3>). The package further includes utilities for visualization, comparison, and evaluation of graph structures, facilitating performance evaluation and methodological studies.
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causal-discoverycausal-inferencegraphical-modelsstructure-learningquarto
7.57 score 24 stars 28 scripts 508 downloads
bayesSSM - Bayesian Methods for State Space Models
Implements methods for Bayesian analysis of State Space Models. Includes implementations of the Particle Marginal Metropolis-Hastings algorithm described in Andrieu et al. (2010) <doi:10.1111/j.1467-9868.2009.00736.x> and automatic tuning inspired by Pitt et al. (2012) <doi:10.1016/j.jeconom.2012.06.004> and J. Dahlin and T. B. Schön (2019) <doi:10.18637/jss.v088.c02>.
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markov-chain-monte-carlomarkov-modelparticle-filtersequential-monte-carlostate-spacetime-seriescpp
5.13 score 6 stars 15 scripts 397 downloads