Allows users to produce diagnostic procedures and graphic tools for the evaluation of Small Area estimators.

Provides functionality for image processing and shape analysis in 
    the context of reconstructed medical images generated by deep learning-based
    methods or standard image processing algorithms and produced from different
    medical imaging types, such as X-ray, Computational Tomography (CT),
    Magnetic Resonance Imaging (MRI), and pathology imaging. Specifically,
    offers tools to segment regions of interest and to extract quantitative
    shape descriptors for applications in signal processing,
    statistical analysis and modeling, and machine learning.

The aim of the package is to provide some basic functions
        for doing statistics with one dimensional Fuzzy Data (in the
        form of polygonal fuzzy numbers). In particular, the package
        contains functions for the basic operations on the class of
        fuzzy numbers (sum, scalar product, mean, median, Hukuhara difference) 
        as well as for calculating (Bertoluzza) distance and sample variance. 
        Moreover a function to simulate fuzzy random variables and bootstrap tests 
        for the equality of means is included. Version 2.1 fixes some bugs
        of previous versions.

Provides tools to help tag and validate data according to user-specified rules. The 'safeframe' class adds variable level attributes to 'data.frame' columns. Once tagged, these variables can be seamlessly used in downstream analyses, making data pipelines clearer, more robust, and more reliable.

The goal of 'safejoin' is to guarantee that when performing joins extra rows are not added to your data. 'safejoin' provides a wrapper around 'dplyr::left_join' that will raise an error when extra rows are unexpectedly added to your data. This can be useful when working with data where you expect there to be a many to one relationship but you are not certain the relationship holds.

The goal of 'SAFEPG' is to predict climate-related extreme losses by fitting a frequency-severity model. It improves predictive performance by introducing a sign-aligned regularization term, which ensures consistent signs for the coefficients across the frequency and severity components. This enhancement not only increases model accuracy but also enhances its interpretability, making it more suitable for practical applications in risk assessment.