PatientLevelPrediction 3.0.0

• Added large-scale prediction function to train Cartesian product of T,O,TAR, models, covariate settings and population settings
• Added deep learning and ensemble models
• Updated journal paper creation function
• Updated vignettes
• Updated Readme
• Fix bugs

PatientLevelPrediction Update

Added network study package function
Added documentation

New Feature Extraction

The package was updated for the new feature extraction package

PatientLevelPrediction OHDSI Tutorial

The version that will be used at the 2017 OHDSI tutorial on PatientLevelPrediction.

This version includes:

-Standardized covariate construction using the CDM
-Ability to add custom covariates
-7 classifiers (ada boost, decision tree, gradient boosting machine, knn, lasso logistic regression, naive bayes, random forest)
-Ability to add custom classifier
-Internal validation measures (calibration, discrimination, visualizations)
-Interactive shiny visualization of internal validation performance
-Simple functions to extract similar data (the same covariates) and apply the model on new datasets

Added support for custom covariate builders

Added support for custom covariate builders. A new covariate builder has been included for generating covariates typically used in HDPS(tm) studies.

First stable release

This is the first stable release that it intended for testing and development purposes.