pcalg

R package pcalg: Estimation of CPDAG/PAG and causal inference using the IDA algorithm , Standard and robust estimation of the equivalence class of a Directed Acyclic Graph (DAG) via the PC-Algorithm. The equivalence class is represented by its (unique) Completete Partially Directed Acyclic Graph (CPDAG). Furthermore, a PAG instead of a CPDAG can be estimated if latent variables and/or selection variables are assumed to be present. FCI and RFCI are available for estimating PAGs. Functions for causal inference using the IDA algorithm (based on do-calculus of Judea Pearl) are provided for CPDAGs. (Source: http://cran.r-project.org/web/packages)

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References in zbMATH (referenced in 90 articles , 3 standard articles )

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  1. Rodríguez-López, Verónica; Sucar, Luis Enrique: Knowledge transfer for causal discovery (2022)
  2. Scutari, Marco; Denis, Jean-Baptiste: Bayesian networks. With examples in R (2022)
  3. Gnecco, Nicola; Meinshausen, Nicolai; Peters, Jonas; Engelke, Sebastian: Causal discovery in heavy-tailed models (2021)
  4. Polina Suter, Jack Kuipers, Giusi Moffa, Niko Beerenwinkel: Bayesian structure learning and sampling of Bayesian networks with the R package BiDAG (2021) arXiv
  5. Santtu Tikka, Antti Hyttinen, Juha Karvanen: Incomplete Data Sources: A General Search-Based Approach (2021) not zbMATH
  6. Watson, David S.; Wright, Marvin N.: Testing conditional independence in supervised learning algorithms (2021)
  7. Han Yu; Janhavi Moharil; Rachael Hageman Blair: BayesNetBP: An R Package for Probabilistic Reasoning in Bayesian Networks (2020) not zbMATH
  8. Kalainathan, Diviyan; Goudet, Olivier; Dutta, Ritik: Causal discovery toolbox: uncovering causal relationships in Python (2020)
  9. Liu, Yue; Fang, Zhuangyan; He, Yangbo; Geng, Zhi; Liu, Chunchen: Local causal network learning for finding pairs of total and direct effects (2020)
  10. Mooij, Joris M.; Magliacane, Sara; Claassen, Tom: Joint causal inference from multiple contexts (2020)
  11. Peluso, Stefano; Consonni, Guido: Compatible priors for model selection of high-dimensional Gaussian DAGs (2020)
  12. Ramsey, Joseph D.; Malinsky, Daniel; Bui, Kevin V.: algcomparison: comparing the performance of graphical structure learning algorithms with TETRAD (2020)
  13. Vitale, Vincenzina; Musella, Flaminia; Vicard, Paola; Guizzi, Valentina: Modelling an energy market with Bayesian networks for non-normal data (2020)
  14. Witte, Janine; Henckel, Leonard; Maathuis, Marloes H.; Didelez, Vanessa: On efficient adjustment in causal graphs (2020)
  15. Bucur, Ioan Gabriel; Claassen, Tom; Heskes, Tom: Large-scale local causal inference of gene regulatory relationships (2019)
  16. Cui, Ruifei; Groot, Perry; Heskes, Tom: Learning causal structure from mixed data with missing values using Gaussian copula models (2019)
  17. Frot, Benjamin; Nandy, Preetam; Maathuis, Marloes H.: Robust causal structure learning with some hidden variables (2019)
  18. Gilles Kratzer, Fraser Iain Lewis, Arianna Comin, Marta Pittavino, Reinhard Furrer: Additive Bayesian Network Modelling with the R Package abn (2019) arXiv
  19. Gu, Jiaying; Fu, Fei; Zhou, Qing: Penalized estimation of directed acyclic graphs from discrete data (2019)
  20. Musella, Flaminia; Vicard, Paola; Vitale, Vincenzina: Copula grow-shrink algorithm for structural learning (2019)

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