Performs Bayesian nonparametric multiscale density estimation and multiscale testing of group differences with multiscale Bernstein polynomials (msBP) mixtures as in Canale and Dunson (2016).

Provides sample data sets that are used in statistics and data science courses at the Münster School of Business. The datasets refer to different business topics but also other domains, e.g. sports, traffic, etc.

Functions to calculate hazard and survival function of Multi-Stage Clonal Expansion Models used in cancer epidemiology. For the Two-Stage Clonal Expansion Model an exact solution is implemented assuming piecewise constant parameters. Numerical solutions are provided for its extensions.

Create native charts for 'Microsoft PowerPoint' and 'Microsoft Word' documents. 
 These can then be edited and annotated. Functions are provided to let users create charts, modify 
 and format their content. The chart's underlying data is automatically saved within the 
 'Word' document or 'PowerPoint' presentation. It extends package 'officer' that does 
 not contain any feature for 'Microsoft' native charts production.

Functions to classify mass spectra in known categories, and to determine discriminant mass-over-charge values. It includes easy-to-use functions for pre-processing mass spectra, functions to determine discriminant mass-over-charge values (m/z) from a library of mass spectra corresponding to different categories, and functions to predict the category (species, phenotypes, etc.) associated to a mass spectrum from a list of selected mass-over-charge values. Three vignettes illustrating how to use the functions of this package from real data sets are also available online to help users: <https://agodmer.github.io/MSclassifR_examples/Vignettes/Vignettemsclassifr_Ecrobiav3.html>, <https://agodmer.github.io/MSclassifR_examples/Vignettes/Vignettemsclassifr_Klebsiellav3.html> and <https://agodmer.github.io/MSclassifR_examples/Vignettes/Vignettemsclassifr_DAv3.html>.

Model based clustering using 
    the multivariate multiple Scaled t (MST) and multivariate multiple 
    scaled contaminated normal (MSCN) distributions. The MST is an 
    extension of the multivariate Student-t distribution to include 
    flexible tail behaviors, Forbes, F. & Wraith, D. (2014) <doi:10.1007/s11222-013-9414-4>. The MSCN represents a  heavy-tailed
    generalization of the multivariate normal (MN) distribution to
    model elliptical contoured scatters in the presence of mild outliers
    (also referred to as "bad" points) and automatically detect bad points, Punzo, A. & Tortora, C. (2021) <doi:10.1177/1471082X19890935>.