Package: eff2
Type: Package
Title: Efficient Least Squares for Total Causal Effects
Version: 1.0.2
Author: Richard Guo [aut, cre]
        (<https://orcid.org/0000-0002-2081-7398>)
Maintainer: Richard Guo <ricguo@uw.edu>
Authors@R: c(
    person("Richard", "Guo", , "ricguo@uw.edu", role = c("aut", "cre"), 
      comment = c(ORCID = "0000-0002-2081-7398"))
    )
Description: Estimate a total causal effect from observational data
        under linearity and causal sufficiency. The observational data
        is supposed to be generated from a linear structural equation
        model (SEM) with independent and additive noise. The underlying
        causal DAG associated the SEM is required to be known up to a
        maximally oriented partially directed graph (MPDAG), which is a
        general class of graphs consisting of both directed and
        undirected edges, including CPDAGs (i.e., essential graphs) and
        DAGs. Such graphs are usually obtained with structure learning
        algorithms with added background knowledge. The program is able
        to estimate every identified effect, including single and
        multiple treatment variables. Moreover, the resulting estimate
        has the minimal asymptotic covariance (and hence shortest
        confidence intervals) among all estimators that are based on
        the sample covariance.
License: MIT + file LICENSE
URL: https://github.com/richardkwo/eff2
BugReports: https://github.com/richardkwo/eff2/issues
Depends: R (>= 3.5.0)
Imports: pcalg (>= 2.6), RBGL, igraph
Suggests: knitr, rmarkdown, testthat, qgraph
VignetteBuilder: knitr
RoxygenNote: 7.2.3
Roxygen: list(markdown = TRUE)
Encoding: UTF-8
LazyData: true
Config/pak/sysreqs: libglpk-dev libxml2-dev
Repository: https://richardkwo.r-universe.dev
Date/Publication: 2026-04-14 13:30:00 UTC
RemoteUrl: https://github.com/richardkwo/eff2
RemoteRef: HEAD
RemoteSha: b1fca3294dc570e2950d239c2247c85e8ce55e9b
NeedsCompilation: no
Packaged: 2026-07-13 07:05:14 UTC; root