mlame: mlame: Estimate average marginal effects for machine learning...
In umasds/mlame: Estimate Average Marginal Effects for Machine Learning Algorithms
Description
An easy way to estimate average marginal effects (AME), predicted
probabilities / values and similar quantities from different kinds of
machine learning algorithms. Bootstrap estimates of standard errors,
confidence intervals and p-values are also provided.
mlame functions
So far mlame provides two functions ame and ame.boot.
umasds/mlame documentation built on May 3, 2019, 2:24 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description
An easy way to estimate average marginal effects (AME), predicted probabilities / values and similar quantities from different kinds of machine learning algorithms. Bootstrap estimates of standard errors, confidence intervals and p-values are also provided.
mlame functions
So far mlame provides two functions ame and ame.boot.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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