Provides tools for analyzing differential abundance in proteomics experiments. Implements S3
    classes for data management and supports Generalized Linear Models (GLM; Nelder and Wedderburn (1972) <doi:10.2307/2344614>), Aligned Rank Transform (ART; Wobbrock et al. (2011) <doi:10.1145/1978942.1978963>), and pairwise test
    methods for statistical analysis. Includes visualization functions for
    Principal Component Analysis (PCA), volcano plots, and heatmaps.

Is designed to make easier printing summary statistics (for continues and factor level) tables in Latex, and plotting by factor.

Designed for prediction error estimation
        through resampling techniques, possibly accelerated by parallel
        execution on a compute cluster. Newly developed model fitting
        routines can be easily incorporated. Methods used in the package are detailed in
        Porzelius Ch., Binder H. and Schumacher M. (2009) <doi:10.1093/bioinformatics/btp062>
        and were used, for instance, in
        Porzelius Ch., Schumacher M. and Binder H. (2011) <doi:10.1007/s00180-011-0236-6>.

A versatile R visualization package that empowers 
    researchers with comprehensive visualization tools for seamlessly mapping peptides 
    to protein sequences, identifying distinct domains and regions of interest, 
    accentuating mutations, and highlighting post-translational modifications, 
    all while enabling comparisons across diverse experimental conditions. 
    Potential applications of 'PepMapViz' include the visualization of cross-software 
    mass spectrometry results at the peptide level for specific protein and domain 
    details in a linearized format and post-translational modification coverage 
    across different experimental conditions; unraveling insights into disease 
    mechanisms. It also enables visualization of Major histocompatibility 
    complex-presented peptide clusters in different antibody regions predicting 
    immunogenicity in antibody drug development.