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)
Keywords for this software
References in zbMATH (referenced in 90 articles , 3 standard articles )
Showing results 1 to 20 of 90.
Sorted by year (- Rodríguez-López, Verónica; Sucar, Luis Enrique: Knowledge transfer for causal discovery (2022)
- Scutari, Marco; Denis, Jean-Baptiste: Bayesian networks. With examples in R (2022)
- Gnecco, Nicola; Meinshausen, Nicolai; Peters, Jonas; Engelke, Sebastian: Causal discovery in heavy-tailed models (2021)
- Polina Suter, Jack Kuipers, Giusi Moffa, Niko Beerenwinkel: Bayesian structure learning and sampling of Bayesian networks with the R package BiDAG (2021) arXiv
- Santtu Tikka, Antti Hyttinen, Juha Karvanen: Incomplete Data Sources: A General Search-Based Approach (2021) not zbMATH
- Watson, David S.; Wright, Marvin N.: Testing conditional independence in supervised learning algorithms (2021)
- Han Yu; Janhavi Moharil; Rachael Hageman Blair: BayesNetBP: An R Package for Probabilistic Reasoning in Bayesian Networks (2020) not zbMATH
- Kalainathan, Diviyan; Goudet, Olivier; Dutta, Ritik: Causal discovery toolbox: uncovering causal relationships in Python (2020)
- Liu, Yue; Fang, Zhuangyan; He, Yangbo; Geng, Zhi; Liu, Chunchen: Local causal network learning for finding pairs of total and direct effects (2020)
- Mooij, Joris M.; Magliacane, Sara; Claassen, Tom: Joint causal inference from multiple contexts (2020)
- Peluso, Stefano; Consonni, Guido: Compatible priors for model selection of high-dimensional Gaussian DAGs (2020)
- Ramsey, Joseph D.; Malinsky, Daniel; Bui, Kevin V.: algcomparison: comparing the performance of graphical structure learning algorithms with TETRAD (2020)
- Vitale, Vincenzina; Musella, Flaminia; Vicard, Paola; Guizzi, Valentina: Modelling an energy market with Bayesian networks for non-normal data (2020)
- Witte, Janine; Henckel, Leonard; Maathuis, Marloes H.; Didelez, Vanessa: On efficient adjustment in causal graphs (2020)
- Bucur, Ioan Gabriel; Claassen, Tom; Heskes, Tom: Large-scale local causal inference of gene regulatory relationships (2019)
- Cui, Ruifei; Groot, Perry; Heskes, Tom: Learning causal structure from mixed data with missing values using Gaussian copula models (2019)
- Frot, Benjamin; Nandy, Preetam; Maathuis, Marloes H.: Robust causal structure learning with some hidden variables (2019)
- Gilles Kratzer, Fraser Iain Lewis, Arianna Comin, Marta Pittavino, Reinhard Furrer: Additive Bayesian Network Modelling with the R Package abn (2019) arXiv
- Gu, Jiaying; Fu, Fei; Zhou, Qing: Penalized estimation of directed acyclic graphs from discrete data (2019)
- Musella, Flaminia; Vicard, Paola; Vitale, Vincenzina: Copula grow-shrink algorithm for structural learning (2019)