Provides functionality for quality control processing and statistical analysis of mass spectrometry (MS) omics data, in particular proteomic (either at the peptide or the protein level), lipidomic, and metabolomic data, as well as RNA-seq based count data and nuclear magnetic resonance (NMR) data. This includes data transformation, specification of groups that are to be compared against each other, filtering of features and/or samples, data normalization, data summarization (correlation, PCA), and statistical comparisons between defined groups.  Implements methods described in:  Webb-Robertson et al. (2014) <doi:10.1074/mcp.M113.030932>.  Webb-Robertson et al. (2011) <doi:10.1002/pmic.201100078>.  Matzke et al. (2011) <doi:10.1093/bioinformatics/btr479>.  Matzke et al. (2013) <doi:10.1002/pmic.201200269>.  Polpitiya et al. (2008) <doi:10.1093/bioinformatics/btn217>.  Webb-Robertson et al. (2010) <doi:10.1021/pr1005247>.

Monte Carlo based model choice for applied phylogenetics of
    continuous traits. Method described in  Carl Boettiger, Graham Coop,
    Peter Ralph (2012) Is your phylogeny informative? Measuring
    the power of comparative methods, Evolution 66 (7)
    2240-51. <doi:10.1111/j.1558-5646.2011.01574.x>.

Fit calibrations curves for clinical prediction models and calculate several associated 
  metrics (Eavg, E50, E90, Emax). Ideally predicted probabilities from a prediction model 
  should align with observed probabilities. Calibration curves relate predicted probabilities 
  (or a transformation thereof) to observed outcomes via a flexible non-linear smoothing function. 
  'pmcalibration' allows users to choose between several smoothers (regression splines, generalized 
  additive models/GAMs, lowess, loess). Both binary and time-to-event outcomes are supported. 
  See Van Calster et al. (2016) <doi:10.1016/j.jclinepi.2015.12.005>; 
  Austin and Steyerberg (2019) <doi:10.1002/sim.8281>; 
  Austin et al. (2020) <doi:10.1002/sim.8570>.

Aims to utilize model-based clustering (unsupervised)
        for high dimensional and ultra large data, especially in a distributed
        manner. The code employs 'pbdMPI' to perform a
        expectation-gathering-maximization algorithm
        for finite mixture Gaussian
        models. The unstructured dispersion matrices are assumed in the
        Gaussian models. The implementation is default in the single program
        multiple data programming model. The code can be executed
        through 'pbdMPI' and MPI' implementations such as 'OpenMPI'
        and 'MPICH'.
        See the High Performance Statistical Computing website
	<https://snoweye.github.io/hpsc/>
	for more information, documents and examples.