An implementation of reliability estimation methods described in the paper (Bosnic, Z., & Kononenko, I. (2008) <doi:10.1007/s10489-007-0084-9>), which allows you to test the reliability of a single predicted instance made
             by your model and prediction function. It also allows you to make a correlation test to estimate which reliability estimate is the most 
             accurate for your model.

Evaluate the predictive performance of an existing (i.e. previously 
    developed) prediction/ prognostic model given relevant information about the 
    existing prediction model (e.g. coefficients) and a new dataset. Provides a 
    range of model updating methods that help tailor the existing model to the 
    new dataset; see Su et al. (2018) <doi:10.1177/0962280215626466>. Techniques 
    to aggregate multiple existing prediction models on the new data are also 
    provided; see Debray et al. (2014) <doi:10.1002/sim.6080> and Martin et al. 
    (2018) <doi:10.1002/sim.7586>).

Global hypothesis tests combine information across multiple endpoints to test a single hypothesis. The prediction test is a recently proposed global hypothesis test with good performance for small sample sizes and many endpoints of interest. The test is also flexible in the types and combinations of expected results across the individual endpoints. This package provides functions for data processing and calculation of the prediction test.

Provides additional functions for evaluating predictive models, including plotting calibration curves and model-based Receiver Operating Characteristic (mROC) based on Sadatsafavi et al (2021) <arXiv:2003.00316>.

Makes the time series prediction easier by automatizing this process
  using four main functions: prep(), modl(), pred() and postp(). Features different
  preprocessing methods to homogenize variance and to remove trend and seasonality.
  Also has the potential to bring together different predictive models to make comparatives.
  Features ARIMA and Data Mining Regression models (using caret).