stackedglm: Stacked Regression
In metamisc: Meta-Analysis of Diagnosis and Prognosis Research Studies

This function combines one or more existing prediction models into a so/called meta-model.

1	stackedglm(models, family = binomial, data)

`models`	a list containing the historical prediction models, which can be defined in several ways. For instance, historical regression models can be specified using a named vector containing the regression coefficients of the individual predictors (no need to include the intercept term). List items may also represent an object for which the function `predict()` exists.
`family`	a description of the error distribution and link function to be used in the meta-model. This can be a character string naming a family function, a family function or the result of a call to a family function. (See family for details of family functions.)
`data`	an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in `data`, the variables are taken from `environment(formula)`, typically the environment from which `stackedglm` is called.