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Copy file name to clipboardExpand all lines: README.Rmd
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Some technical details of the package are worth briefly noting. The estimation of marginal effects relies on numeric derivatives produced using `predict()` and [`numDeriv::grad()`](https://cran.r-project.org/package=numDeriv). While symbolic differentiation of some models (e.g., linear models) is possible using `D()` and `deriv()`, R's modelling language (the "formula" class) is sufficiently general to enable the construction of model formulae that contain terms that fall outside of R's symbolic differentiation rule table (e.g., `y ~ factor(x)` or `y ~ I(FUN(x))` for any arbitrary `FUN()`). By relying on numeric differentiation, `margins()` supports *any* model that can be expressed in R formula syntax. Even Stata's `margins` command is limited in its ability to handle variable transformations (e.g., including `x` and `log(x)` as predictors) and quadratic terms (e.g., `x^3`); these scenarios are easily expressed in an R formula and easily handled, correctly, by `margins()`.
[](http://www.repostatus.org/#wip)
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The development version of this package can be installed directly from GitHub using `devtools`:
[](http://www.repostatus.org/#wip)
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The development version of this package can be installed directly from GitHub using `devtools`:
Copy file name to clipboardExpand all lines: README.md
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Some technical details of the package are worth briefly noting. The estimation of marginal effects relies on numeric derivatives produced using `predict()` and [`numDeriv::grad()`](https://cran.r-project.org/package=numDeriv). While symbolic differentiation of some models (e.g., linear models) is possible using `D()` and `deriv()`, R's modelling language (the "formula" class) is sufficiently general to enable the construction of model formulae that contain terms that fall outside of R's symbolic differentiation rule table (e.g., `y ~ factor(x)` or `y ~ I(FUN(x))` for any arbitrary `FUN()`). By relying on numeric differentiation, `margins()` supports *any* model that can be expressed in R formula syntax. Even Stata's `margins` command is limited in its ability to handle variable transformations (e.g., including `x` and `log(x)` as predictors) and quadratic terms (e.g., `x^3`); these scenarios are easily expressed in an R formula and easily handled, correctly, by `margins()`.
[](http://www.repostatus.org/#wip)
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The development version of this package can be installed directly from GitHub using `devtools`:
[](http://www.repostatus.org/#wip)
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The development version of this package can be installed directly from GitHub using `devtools`:
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