safe_predict.glm: Safe predictions from a generalized linear model
In alexpghayes/safepredict: Consistent prediction following tidymodels principles

Safe predictions from a generalized linear model

## S3 method for class 'glm'
safe_predict(object, new_data, type = c("link", "conf_int",
  "response", "class", "prob"), ..., std_error = FALSE, level = 0.95,
  threshold = 0.5)

`object`	A `glm` object returned from a call to `stats::glm()`.
`new_data`	TODO
`type`	What kind of predictions to return. Which predictions are available depends on the family of `object`. `"link"` and `"conf_int"` are available for all families. `"link"` produces numeric predictions on the linear predictor scale. `"conf_int"` produces numeric predictions on the response scale and corresponding confidence bounds. `"response"` results in a numeric prediction on the response scale and is available for families: `gaussian` `Gamma` `inverse.gaussian` `poisson` `quasipoisson` `quasi` `"class"` results in hard class predictions and is only available for `binomial` and `quasibinomial` families `"prob"` results in class predictions for each class and is only available for `binomial` and `quasibinomial` families Default is `"link"`.
`...`	Unused. `safe_predict()` checks that all arguments in `...` are evaluated via the `ellipsis` package. The idea is to prevent silent errors when arguments are mispelled. This feature is experimental and feedback is welcome.
`std_error`	Logical indicating whether or not calculate standard errors for the fit at each point. Not available for all models, and can be computationally expensive to compute. The standard error is always the standard error for the mean, and never the standard error for predictions. Standard errors are returned in a column called `.std_error`. Defaults to `FALSE`.
`level`	A number strictly between `0` and `1` to use as the confidence level when calculating confidence and prediction intervals. Setting `level = 0.90` correspondings to a 90 percent confidence interval. Ignored except when `type = "conf_int"` or `type = "pred_int"`. Defaults to `0.95`.
`threshold`	A number between `0` and `1` to use as a threshold for classification. When the class probability for the class corresponding to a positive event is greater than the threshold, the event will be classified as positive. Defaults to `0.5`.