The aim of this package is to offer new methodology for unit-level 
    small area models under transformations and limited population auxiliary 
    information. In addition to this new methodology, the widely used nested 
    error regression model without transformations (see "An Error-Components 
    Model for Prediction of County Crop Areas Using Survey and Satellite Data" 
    by Battese, Harter and Fuller (1988) <doi:10.1080/01621459.1988.10478561>) 
    and its well-known uncertainty estimate (see "The estimation of the mean 
    squared error of small-area estimators" by Prasad and Rao (1990) 
    <doi:10.1080/01621459.1995.10476570>) are provided. In this package, the 
    log transformation and the data-driven log-shift transformation are 
    provided. If a transformation is selected, an appropriate method is chosen 
    depending on the respective input of the population data: Individual 
    population data (see "Empirical best prediction under a nested error model 
    with log transformation" by Molina and Martín (2018) 
    <doi:10.1214/17-aos1608>) but also aggregated population data (see 
    "Estimating regional income indicators under transformations and access to 
    limited population auxiliary information" by Würz, Schmid and Tzavidis 
    <unpublished>) can be entered. Especially under limited data access, new 
    methodologies are provided in saeTrafo. Several options are available to 
    assess the used model and to judge, present and export its results. For a 
    detailed description of the package and the methods used see the 
    corresponding vignette.

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.