Provides various statistical methods for evaluating
    Individualized Treatment Rules under randomized data. The provided
    metrics include Population Average Value (PAV), Population Average
    Prescription Effect (PAPE), Area Under Prescription Effect Curve
    (AUPEC). It also provides the tools to analyze Individualized
    Treatment Rules under budget constraints. Detailed reference in Imai
    and Li (2019) <arXiv:1905.05389>.

The purpose of this package is to generate trees and validate unverified code. Trees are made by parsing a statement into a verification tree data structure. This will make it easy to port the statement into another language. Safe statement evaluations are done by executing the verification trees.

Evaluates diagnostic test performance using data from laboratory or 
    diagnostic research. It includes functions to compute common performance 
    indicators along with their confidence intervals, and offers an interactive 
    'shiny' application for comprehensive analysis including ROC curve visualization
    and related metrics. It supports both binary and continuous test variables. 
    It allows users to compute key performance indicators and visualize Receiver
    Operating Characteristic (ROC) curves, determine optimal cut-off thresholds,
    display confusion matrix, and export publication-ready plot. It aims to facilitate
    the application of statistical methods in diagnostic test evaluation by healthcare
    professionals. Methodological details and references for the computation of 
    performance indicators are provided in the package vignette.

Parsing and evaluation tools that make it easy to recreate
    the command line behaviour of R.

Implements various quality evaluation statistics to assess the
    value of plant germplasm core collections using qualitative and 
    quantitative phenotypic trait data according to Odong et al. (2015) 
    <doi:10.1007/s00122-012-1971-y>.

Provides Some of the most important evaluation measures for evaluating a model. Just by giving the real and predicted class, measures such as accuracy, sensitivity, specificity, ppv, npv, fmeasure, mcc and ... will be returned.