Predicts categorical or continuous outcomes while concentrating on a number of key points. These are Cross-validation, Accuracy, Regression and Rule of Ten or "one in ten rule" (CARRoT), and, in addition to it R-squared statistics, prior knowledge on the dataset etc. It performs the cross-validation specified number of times by partitioning the input into training and test set and fitting linear/multinomial/binary regression models to the training set. All regression models satisfying chosen constraints are fitted and the ones with the best predictive power are given as an output. Best predictive power is understood as highest accuracy in case of binary/multinomial outcomes, smallest absolute and relative errors in case of continuous outcomes. For binary case there is also an option of finding a regression model which gives the highest AUROC (Area Under Receiver Operating Curve) value. The option of parallel toolbox is also available. Methods are described in Peduzzi et al. (1996) <doi:10.1016/S0895-4356(96)00236-3> , Rhemtulla et al. (2012) <doi:10.1037/a0029315>, Riley et al. (2018) <doi:10.1002/sim.7993>, Riley et al. (2019) <doi:10.1002/sim.7992>.

Implements Competitive Adaptive Reweighted Sampling (CARS) 
    algorithm for variable selection from high-dimensional dataset using Partial 
    Least Squares (PLS) regression models. CARS algorithm iteratively applies the 
    Monte Carlo sub-sampling and exponential variable elimination techniques to 
    identify/select the most informative variables/features subjected to minimal 
    cross-validated RMSE score. The implementation of CARS algorithm is inspired 
    from the work of Li et al. (2009) <doi:10.1016/j.aca.2009.06.046>. 
    This algorithm is widely applied in near-infrared (NIR), mid-infrared (MIR), 
    hyperspectral chemometrics areas, etc.

Contains functions to estimate the Correlation-Adjusted Regression Survival (CARS) Scores. The method is described in Welchowski, T. and Zuber, V. and Schmid, M., (2018), Correlation-Adjusted Regression Survival Scores for High-Dimensional Variable Selection, <arXiv:1802.08178>.