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)

References in zbMATH (referenced in 86 articles , 3 standard articles )

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  1. Polina Suter, Jack Kuipers, Giusi Moffa, Niko Beerenwinkel: Bayesian structure learning and sampling of Bayesian networks with the R package BiDAG (2021) arXiv
  2. Han Yu; Janhavi Moharil; Rachael Hageman Blair: BayesNetBP: An R Package for Probabilistic Reasoning in Bayesian Networks (2020) not zbMATH
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  4. Liu, Yue; Fang, Zhuangyan; He, Yangbo; Geng, Zhi; Liu, Chunchen: Local causal network learning for finding pairs of total and direct effects (2020)
  5. Mooij, Joris M.; Magliacane, Sara; Claassen, Tom: Joint causal inference from multiple contexts (2020)
  6. Peluso, Stefano; Consonni, Guido: Compatible priors for model selection of high-dimensional Gaussian DAGs (2020)
  7. Ramsey, Joseph D.; Malinsky, Daniel; Bui, Kevin V.: algcomparison: comparing the performance of graphical structure learning algorithms with TETRAD (2020)
  8. Vitale, Vincenzina; Musella, Flaminia; Vicard, Paola; Guizzi, Valentina: Modelling an energy market with Bayesian networks for non-normal data (2020)
  9. Witte, Janine; Henckel, Leonard; Maathuis, Marloes H.; Didelez, Vanessa: On efficient adjustment in causal graphs (2020)
  10. Bucur, Ioan Gabriel; Claassen, Tom; Heskes, Tom: Large-scale local causal inference of gene regulatory relationships (2019)
  11. Cui, Ruifei; Groot, Perry; Heskes, Tom: Learning causal structure from mixed data with missing values using Gaussian copula models (2019)
  12. Frot, Benjamin; Nandy, Preetam; Maathuis, Marloes H.: Robust causal structure learning with some hidden variables (2019)
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  14. Gu, Jiaying; Fu, Fei; Zhou, Qing: Penalized estimation of directed acyclic graphs from discrete data (2019)
  15. Musella, Flaminia; Vicard, Paola; Vitale, Vincenzina: Copula grow-shrink algorithm for structural learning (2019)
  16. van der Zander, Benito; Liśkiewicz, Maciej; Textor, Johannes: Separators and adjustment sets in causal graphs: complete criteria and an algorithmic framework (2019)
  17. Zhao, Jianjun; Ho, Shen-Shyang: Improving Bayesian network local structure learning via data-driven symmetry correction methods (2019)
  18. Champion, Magali; Picheny, Victor; Vignes, Matthieu: Inferring large graphs using (\ell_1)-penalized likelihood (2018)
  19. Md. Bahadur Badsha, Evan A Martin, Audrey Qiuyan Fu: MRPC: An R package for accurate inference of causal graphs (2018) arXiv
  20. Montero-Hernandez, Samuel; Orihuela-Espina, Felipe; Sucar, Luis Enrique; Pinti, Paola; Hamilton, Antonia; Burgess, Paul; Tachtsidis, Ilias: Estimating functional connectivity symmetry between oxy- and deoxy-haemoglobin: implications for fNIRS connectivity analysis (2018)

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