pcalg
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 51 articles , 2 standard articles )
Showing results 1 to 20 of 51.
Sorted by year (- Malinsky, Daniel; Spirtes, Peter: Estimating bounds on causal effects in high-dimensional and possibly confounded systems (2017)
- Pircalabelu, Eugen; Claeskens, Gerda; Gijbels, Irène: Copula directed acyclic graphs (2017)
- Aghdam, Rosa; Alijanpour, Mohsen; Azadi, Mehrdad; Ebrahimi, Ali; Eslahchi, Changiz; Rezvan, Abolfazl: Inferring gene regulatory networks by PCA-CMI using Hill climbing algorithm based on MIT score and SORDER method (2016)
- Goudie, Robert J.B.; Mukherjee, Sach: A Gibbs sampler for learning DAGs (2016)
- Ha, Min Jin; Sun, Wei; Xie, Jichun: $\mathsfPenPC$: a two-step approach to estimate the skeletons of high-dimensional directed acyclic graphs (2016)
- Lan, Wei; Ding, Yue; Fang, Zheng; Fang, Kuangnan: Testing covariates in high dimension linear regression with latent factors (2016)
- Lan, Wei; Zhong, Ping-Shou; Li, Runze; Wang, Hansheng; Tsai, Chih-Ling: Testing a single regression coefficient in high dimensional linear models (2016)
- Oates, Chris.J.; Smith, Jim Q.; Mukherjee, Sach: Estimating causal structure using conditional DAG models (2016)
- Peña, Jose M.; Gómez-Olmedo, Manuel: Learning marginal AMP chain graphs under faithfulness revisited (2016)
- Zhang, Jiji; Spirtes, Peter: The three faces of faithfulness (2016)
- Aragam, Bryon; Zhou, Qing: Concave penalized estimation of sparse Gaussian Bayesian networks (2015)
- Bontempi, Gianluca; Flauder, Maxime: From dependency to causality: a machine learning approach (2015)
- Ernest, Jan; Bühlmann, Peter: Marginal integration for nonparametric causal inference (2015)
- Kuipers, Jack; Moffa, Giusi: Uniform random generation of large acyclic digraphs (2015)
- Maathuis, Marloes H.; Colombo, Diego: A generalized back-door criterion (2015)
- Mohammadi, A.; Wit, E.C.: BDgraph: An R Package for Bayesian Structure Learning in Graphical Models (2015) arXiv
- Pircalabelu, Eugen; Claeskens, Gerda; Waldorp, Lourens: A focused information criterion for graphical models (2015)
- Scutari, Marco; Denis, Jean-Baptiste: Bayesian networks. With examples in R (2015)
- Slawski, Martin; Hein, Matthias: Estimation of positive definite $M$-matrices and structure learning for attractive Gaussian Markov random fields (2015)
- Statnikov, Alexander; Ma, Sisi; Henaff, Mikael; Lytkin, Nikita; Efstathiadis, Efstratios; Peskin, Eric R.; Aliferis, Constantin F.: Ultra-scalable and efficient methods for hybrid observational and experimental local causal pathway discovery (2015)