Implements the One Rule (OneR) Machine Learning classification algorithm (Holte, R.C. (1993) <doi:10.1023/A:1022631118932>) with enhancements for sophisticated handling of numeric data and missing values together with extensive diagnostic functions. It is useful as a baseline for machine learning models and the rules are often helpful heuristics.

The log-rank test is performed to assess the survival outcomes between two group. 
             When there is no proper control group or obtaining such data is cumbersome, one sample
             log-rank test can be applied. This package performs one sample log-rank test as described in Finkelstein et al. (2003)<doi:10.1093/jnci/djt227> and variation of the 
             test for small sample sizes which is detailed in FD Liddell (1984)<doi:10.1136/jech.38.1.85> paper. Visualization function in the package
             generates Kaplan-Meier Curve comparing survival curve of the general population against that of the population of interest.

Useful functions for one-sample (individual level data)
    Mendelian randomization and instrumental variable analyses. The
    package includes implementations of; the Sanderson and Windmeijer
    (2016) <doi:10.1016/j.jeconom.2015.06.004> conditional F-statistic,
    the multiplicative structural mean model Hernán and Robins (2006)
    <doi:10.1097/01.ede.0000222409.00878.37>, and two-stage predictor
    substitution and two-stage residual inclusion estimators explained by
    Terza et al. (2008) <doi:10.1016/j.jhealeco.2007.09.009>.

This ONEST software implements the method of assessing the pathologist agreement in reading PD-L1 assays (Reisenbichler et al. (2020 <doi:10.1038/s41379-020-0544-x>)), to determine the minimum number of evaluators needed to estimate agreement involving a large number of raters. Input to the program should be binary(1/0) pathology data, where “0” may stand for negative and “1” for positive. Additional examples were given using the data from Rimm et al. (2017 <doi:10.1001/jamaoncol.2017.0013>).