Convenient wrapper functions for the analysis of
    matrix-assisted laser desorption/ionization-time-of-flight (MALDI-TOF)
    spectra data in order to select only representative spectra (also
    called cherry-pick). The package covers the preprocessing and
    dereplication steps (based on Strejcek, Smrhova, Junkova and Uhlik
    (2018) <doi:10.3389/fmicb.2018.01294>) needed to cluster MALDI-TOF
    spectra before the final cherry-picking step. It enables the easy
    exclusion of spectra and/or clusters to accommodate complex
    cherry-picking strategies. Alternatively, cherry-picking using
    taxonomic identification MALDI-TOF data is made easy with functions to
    import inconsistently formatted reports.

A complete analysis pipeline for matrix-assisted laser
        desorption/ionization-time-of-flight (MALDI-TOF) and other
        two-dimensional mass spectrometry data. In addition to commonly
        used plotting and processing methods it includes distinctive
        features, namely baseline subtraction methods such as
        morphological filters (TopHat) or the statistics-sensitive
        non-linear iterative peak-clipping algorithm (SNIP), peak
        alignment using warping functions, handling of replicated
        measurements as well as allowing spectra with different
        resolutions.

Functions for reading (tab, csv, Bruker fid, Ciphergen
        XML, mzXML, mzML, imzML, Analyze 7.5, CDF, mMass MSD) and
        writing (tab, csv, mMass MSD, mzML, imzML) different file
        formats of mass spectrometry data into/from 'MALDIquant'
        objects.

Provides methods for quality control and robust pre-processing and analysis of MALDI mass spectrometry data (Palarea-Albaladejo et al. (2018) <doi:10.1093/bioinformatics/btx628>).

An R interface for the Java Machine Learning for Language Toolkit (mallet)
  <http://mallet.cs.umass.edu/> to estimate probabilistic topic models, such
  as Latent Dirichlet Allocation. We can use the R package to read textual 
  data into mallet from R objects, run the Java implementation of mallet 
  directly in R, and extract results as R objects. The Mallet toolkit 
  has many functions, this wrapper focuses on the topic modeling sub-package 
  written by David Mimno. The package uses the rJava package to connect to a 
  JVM.