Introduction | Use case: iris data | Data set description | Using formula specification | Using model matrix specification | Centering to different values | Plotting the model | Using stepwise RBIC | Use case: lung data | Preprocessing | Using the sparseR function | Use case: heart data

The uihc_ed_arrivals data set | Modeling the data | Endogenous model (SRLPAC) | Exogenous model (SRLPACx) | Making predictions | k-step ahead predictions | Cumulative predictions | Conclusions

Lake Huron data set | What does predict do? | Forecasting

Lake Huron data set | EuStockMarkets | Seasonal examples | Nottem | UKDriverDeaths | sunspot

Introduction | Methods | The Lambert W x F transformation | The Box Cox transformation | The Yeo-Johnson transformation | The Ordered Quantile technique | Other included transformations | Other not-included transformations | Selecting the best technique | Examples | Basic Implementation | Visualizing out-of-sample estimates of normality | Improving speed of estimation | Leave-one-out CV | Visualizing transformations in ggplot2 with scales package | Application to autotrader data

Custom functions with bestNormalize | Example: cube-root | S3 methods | Optional: print method | Implementing with bestNormalize | Sanity check | Using custom normalization statistics