Seek the significant cutoff value for a continuous variable, which will 
    be transformed into a classification, for linear regression, 
    logistic regression, logrank analysis and cox regression. First of all, 
    all combinations will be gotten by combn() function. Then n.per argument, 
    abbreviated of total number percentage, will be used to remove the combination 
    of smaller data group. In logistic, Cox regression and logrank analysis, 
    we will also use p.per argument, patient percentage, to filter the lower 
    proportion of patients in each group. Finally, p value in regression 
    results will be used to get the significant combinations and output 
    relevant parameters. In this package, there is no limit to the number of 
    cutoff points, which can be 1, 2, 3 or more. Still, we provide 2 methods, 
    typical Bonferroni and Duglas G (1994) <doi: 10.1093/jnci/86.11.829>, to 
    adjust the p value, Missing values will be deleted by na.omit() function 
    before analysis.

Estimate cutpoints that optimize a specified metric in binary classification tasks
    and validate performance using bootstrapping. Some methods for more robust cutpoint
    estimation are supported, e.g. a parametric method assuming normal distributions,
    bootstrapped cutpoints, and smoothing of the metric values per cutpoint using
    Generalized Additive Models. Various plotting functions are included. For an overview
    of the package see Thiele and Hirschfeld (2021) <doi:10.18637/jss.v098.i11>.

Use optimal equal-HR method to determine two optimal cutpoints of a continuous predictor that has a U-shaped relationship with survival outcomes based on Cox regression model. The optimal equal-HR method estimates two optimal cut-points that have approximately the same log hazard value based on Cox regression model and divides individuals into different groups according to their HR values.

Cross-validation methods of regression models that exploit features of various
    modeling functions to improve speed. Some of the methods implemented in the package are
    novel, as described in the package vignettes; for general introductions to cross-validation,
    see, for example, Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani 
    (2021, ISBN 978-1-0716-1417-4, Secs. 5.1, 5.3), "An Introduction to Statistical Learning with 
    Applications in R, Second Edition", and Trevor Hastie, Robert Tibshirani, 
    and Jerome Friedman (2009, ISBN 978-0-387-84857-0, Sec. 7.10), "The Elements of Statistical 
    Learning, Second Edition".