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causalgenerics is the shared generics package for the r-causal ecosystem. It provides a near-zero-dependency home for the S3 generics that packages such as propensity, halfmoon, positively, and balancing register methods on. Owning the generic definitions in one place means that attaching several r-causal packages at once produces no masking: each package contributes methods to a common generic instead of redefining the function.
The package is modeled on the generics package, which provides commonly used S3 generics for the same reason: so that packages can share a definition instead of each defining their own.
You can install the released version of causalgenerics from CRAN with:
install.packages("causalgenerics")You can install the development version from GitHub with:
# install.packages("pak")
pak::pak("r-causal/causalgenerics")Most users will get causalgenerics as a dependency of another r-causal package rather than installing it directly.
causalgenerics owns the following generics:
ipw(): bring-your-own-model inverse probability
weighted estimation of causal effects from a weighting model and a
weighted outcome model.ess(): the effective sample size of a set of weights or
a fitted model.is_causal_wt(), estimand(), and
estimand<-(): accessors for the metadata carried by
causal weight vectors.The generics are intentionally minimal. Method-specific arguments are
passed through ..., and the concrete weight classes live in
the packages that own them.
Packages in the r-causal ecosystem import causalgenerics and register their methods against these generics. Because the generic is defined once, a user can attach any combination of those packages without one masking another.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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