A method for automatic detection of peaks in noisy periodic and quasi-periodic signals. This method, called automatic multiscale-based peak detection (AMPD), is based on the calculation and analysis of the local maxima scalogram, a matrix comprising the scale-dependent occurrences of local maxima.
     For further information see  <doi:10.3390/a5040588>.

Predicts antimicrobial peptides using random forests trained on the
    n-gram encoded peptides. The implemented algorithm can be accessed from
    both the command line and shiny-based GUI. The AmpGram model is too large 
    for CRAN and it has to be downloaded separately from the repository:
    <https://github.com/michbur/AmpGramModel>.

A toolkit to predict antimicrobial peptides from protein sequences on a genome-wide scale.
    It incorporates two support vector machine models ("precursor" and "mature") trained on publicly available antimicrobial peptide data using calculated
    physico-chemical and compositional sequence properties described in Meher et al. (2017) <doi:10.1038/srep42362>.
    In order to support genome-wide analyses, these models are designed to accept any type of protein as input
    and calculation of compositional properties has been optimised for high-throughput use. For best results it is important to select the model that accurately 
    represents your sequence type: for full length proteins, it is recommended to use the default "precursor" model. The alternative, "mature", model is best suited
    for mature peptide sequences that represent the final antimicrobial peptide sequence after post-translational processing. For details see Fingerhut et al. (2020) <doi:10.1093/bioinformatics/btaa653>.
    The 'ampir' package is also available via a Shiny based GUI at <https://ampir.marine-omics.net/>.

Three Shiny apps are provided that introduce Harvest Control Rules (HCR) for fisheries management.
    'Introduction to HCRs' provides a simple overview to how HCRs work. Users are able to select their own HCR and
    step through its performance, year by year. Biological variability and estimation uncertainty are introduced.
    'Measuring performance' builds on the previous app and introduces the idea of using performance indicators
    to measure HCR performance.
    'Comparing performance' allows multiple HCRs to be created and tested, and their performance compared so that the
    preferred HCR can be selected.