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)


References in zbMATH (referenced in 78 articles , 3 standard articles )

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  1. Han Yu; Janhavi Moharil; Rachael Hageman Blair: BayesNetBP: An R Package for Probabilistic Reasoning in Bayesian Networks (2020) not zbMATH
  2. Vitale, Vincenzina; Musella, Flaminia; Vicard, Paola; Guizzi, Valentina: Modelling an energy market with Bayesian networks for non-normal data (2020)
  3. Bucur, Ioan Gabriel; Claassen, Tom; Heskes, Tom: Large-scale local causal inference of gene regulatory relationships (2019)
  4. Cui, Ruifei; Groot, Perry; Heskes, Tom: Learning causal structure from mixed data with missing values using Gaussian copula models (2019)
  5. Frot, Benjamin; Nandy, Preetam; Maathuis, Marloes H.: Robust causal structure learning with some hidden variables (2019)
  6. Gilles Kratzer, Fraser Iain Lewis, Arianna Comin, Marta Pittavino, Reinhard Furrer: Additive Bayesian Network Modelling with the R Package abn (2019) arXiv
  7. Gu, Jiaying; Fu, Fei; Zhou, Qing: Penalized estimation of directed acyclic graphs from discrete data (2019)
  8. Musella, Flaminia; Vicard, Paola; Vitale, Vincenzina: Copula grow-shrink algorithm for structural learning (2019)
  9. van der Zander, Benito; Liśkiewicz, Maciej; Textor, Johannes: Separators and adjustment sets in causal graphs: complete criteria and an algorithmic framework (2019)
  10. Zhao, Jianjun; Ho, Shen-Shyang: Improving Bayesian network local structure learning via data-driven symmetry correction methods (2019)
  11. Champion, Magali; Picheny, Victor; Vignes, Matthieu: Inferring large graphs using (\ell_1)-penalized likelihood (2018)
  12. Md. Bahadur Badsha, Evan A Martin, Audrey Qiuyan Fu: MRPC: An R package for accurate inference of causal graphs (2018) arXiv
  13. 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)
  14. Nandy, Preetam; Hauser, Alain; Maathuis, Marloes H.: High-dimensional consistency in score-based and hybrid structure learning (2018)
  15. Perković, Emilija; Textor, Johannes; Kalisch, Markus; Maathuis, Marloes H.: Complete graphical characterization and construction of adjustment sets in Markov equivalence classes of ancestral graphs (2018)
  16. Rothenhäusler, Dominik; Ernest, Jan; Bühlmann, Peter: Causal inference in partially linear structural equation models (2018)
  17. Runge, J.: Causal network reconstruction from time series: from theoretical assumptions to practical estimation (2018)
  18. Isozaki, Takashi; Kuroki, Manabu: Learning causal graphs with latent confounders in weak faithfulness violations (2017)
  19. Malinsky, Daniel; Spirtes, Peter: Estimating bounds on causal effects in high-dimensional and possibly confounded systems (2017)
  20. Park, Young Woong; Klabjan, Diego: Bayesian network learning via topological order (2017)

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