Provides tools for post-process, evaluate and visualize results from 3d Meteorological and Air Quality models against point observations (i.e. surface stations) and grid (i.e. satellite) observations.

Evaluates the performance of binary classifiers.  Computes
    confusion measures (TP, TN, FP, FN), derived measures (TPR, FDR,
    accuracy, F1, DOR, ..), and area under the curve.  Outputs are well
    suited for nested dataframes.

Contains a set of clustering methods and evaluation metrics to select the best number of the clusters based on clustering stability. Two references describe the methodology: Fahimeh Nezhadmoghadam, and Jose Tamez-Pena (2021)<doi:10.1016/j.compbiomed.2021.104753>, and Fahimeh Nezhadmoghadam, et al.(2021)<doi:10.2174/1567205018666210831145825>.

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.

Evaluate diagnostic test performance using data from laboratory or diagnostic research. 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. The methodology used to compute the performance indicators follows the overview described by Habibzadeh (2025) <doi:10.11613/BM.2025.010101>. Thanks to 'shiny' package.