Confidence or Prediction Intervals, Quantiles, and Probabilities for Statistical Models
Confidence Intervals for Generalized Linear Model Predictions
Confidence Intervals for Generalized Linear Mixed Model Predictions
Confidence Intervals for Linear Model Predictions
Confidence Intervals for Linear Mixed Model Predictions
Confidence Intervals for Negative Binomial Linear Model Predictions
Add Confidence Intervals for Fitted Values to Data Frames
Confidence Intervals for the Mean Survival Time of Accelerated Failure...
Prediction Intervals for Generalized Linear Models
Prediction Intervals for Generalized Linear Mixed Model Predictions
Prediction Intervals for Linear Model Predictions
Prediction Intervals for Linear Mixed Model Fitted Values
Prediction Intervals for Negative Binomial Linear Models
Add Prediction Intervals to Data Frames
Prediction Intervals for Accelerated Failure Time Models
Response Probabilities for Generalized Linear Models
Response Probabilities for Generalized Linear Mixed Model Predictions
Response Level Probabilities for Linear Models
Response Probabilities for Linear Mixed Models
Response Probabilities for Negative Binomial Models
Add Regression Probabilities to Data Frames
Confidence Intervals for the Survivor Function of Accelerated Failure ...
Quantiles for the Response of a Generalized Linear Model
Response Quantiles for Generalized Linear Mixed Model Predictions
Quantiles for the Response of a Linear Model
Quantiles for the Response of a Linear Mixed Model
Quantiles for the Response of a Negative Binomial Regression
Add Regression Quantiles to Data Frames
Confidence Intervals for Predicted Survival Time Quantiles of Accelera...
Functions to append confidence intervals, prediction intervals, and other quantities of interest to data frames. All appended quantities are for the response variable, after conditioning on the model and covariates. This package has a data frame first syntax that allows for easy piping. Currently supported models include (log-) linear, (log-) linear mixed, generalized linear models, generalized linear mixed models, and accelerated failure time models.