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R6causal
View on CRAN: Click
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Download and install R6causal package within the R console
Install from CRAN:
install.packages("R6causal")
Install from Github:
library("remotes")
install_github("cran/R6causal")
Install by package version:
library("remotes")
install_version("R6causal", "0.8.3")
Attach the package and use:
library("R6causal")
Maintained by
Juha Karvanen
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All associated links for this package
First Published: 2021-08-06
Latest Update: 2023-12-19
Description:
The implemented R6 class 'SCM' aims to simplify working with structural causal models. The missing data mechanism can be defined as a part of the structural model. The class contains methods for 1) defining a structural causal model via functions, text or conditional probability tables, 2) printing basic information on the model, 3) plotting the graph for the model using packages 'igraph' or 'qgraph', 4) simulating data from the model, 5) applying an intervention, 6) checking the identifiability of a query using the R packages 'causaleffect' and 'dosearch', 7) defining the missing data mechanism, 8) simulating incomplete data from the model according to the specified missing data mechanism and 9) checking the identifiability in a missing data problem using the R package 'dosearch'. In addition, there are functions for running experiments and doing counterfactual inference using simulation.
How to cite:
Juha Karvanen (2021). R6causal: R6 Class for Structural Causal Models. R package version 0.8.3, https://cran.r-project.org/web/packages/R6causal. Accessed 21 Dec. 2024.
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Complete documentation for R6causal
Functions, R codes and Examples using
the R6causal R package
Some associated functions: ParallelWorld . R6causal . SCM . backdoor . backdoor_md . counterfactual . frontdoor . generate_condprob . run_experiment . trapdoor .
Some associated R codes: R6causal.R . Full R6causal package functions and examples
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