https://bjarkehautop.r-universe.devRecent package updates in bjarkehautopR-universehttps://github.com/bjarkehautop.pnghttps://bjarkehautop.r-universe.devTue, 29 Sep 2026 06:58:12 GMTbjarke.kristensen@sund.ku.dk (Bjarke Hautop Kristensen)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.https://github.com/r-universe/disco-coders/actions/runs/36548952983Tue, 29 Sep 2026 06:58:12 GMTcausalDisco1.2.1.9000successhttps://disco-coders.r-universe.devhttps://github.com/disco-coders/causaldiscocausalDisco.qmdcausalDisco.htmlcausalDisco2026-03-23 09:12:172026-08-28 07:24:08bjarke.hautop@gmail.com (Bjarke Hautop)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>.https://github.com/r-universe/bjarkehautop/actions/runs/38374549987Tue, 16 Dec 2025 07:17:55 GMTbayesSSM0.7.1.9000successhttps://bjarkehautop.r-universe.devhttps://github.com/bjarkehautop/bayesssmbayesSSM.RmdbayesSSM.htmlbayesSSM2025-03-07 11:57:582025-11-07 16:15:11