Simulation and pricing routines for rare-event options using
    Adaptive Multilevel Splitting and standard Monte Carlo under
    Black-Scholes and Heston models. Core routines are implemented in C++
    via Rcpp and RcppArmadillo with lightweight R wrappers.

Manage and analyze animal movement data. The functionality of
    'amt' includes methods to calculate home ranges, track statistics
    (e.g. step lengths, speed, or turning angles), prepare data for
    fitting habitat selection analyses, and simulation of space-use from
    fitted step-selection functions.

Creates interactive Venn diagrams using the 'amCharts5' library for 
    'JavaScript'. They can be used directly from the R console, from 'RStudio', 
    in 'shiny' applications, and in 'rmarkdown' documents.

Predicts amyloid proteins 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.

A free software for a fast and easy analysis of 1:1 molecular interaction studies. 
   This package is suitable for a high-throughput data analysis. 
   Both the online app and the package are completely open source. 
   You provide a table of sensogram, tell 'anabel' which method to use,
   and it takes care of all fitting details.
   The first two releases of 'anabel' were created and implemented as in 
   (<doi:10.1177/1177932218821383>, <doi:10.1093/database/baz101>).

Is a collection of models to analyze genome scale codon
        data using a Bayesian framework. Provides visualization
        routines and checkpointing for model fittings. Currently
        published models to analyze gene data for selection on codon
        usage based on Ribosome Overhead Cost (ROC) are: ROC (Gilchrist
        et al. (2015) <doi:10.1093/gbe/evv087>), and ROC with phi
        (Wallace & Drummond (2013) <doi:10.1093/molbev/mst051>). In
        addition 'AnaCoDa' contains three currently unpublished models.
        The FONSE (First order approximation On NonSense Error) model
        analyzes gene data for selection on codon usage against of
        nonsense error rates. The PA (PAusing time) and PANSE (PAusing
        time + NonSense Error) models use ribosome footprinting data to
        analyze estimate ribosome pausing times with and without
        nonsense error rate from ribosome footprinting data.