Defines functions to describe regression models using only
    pre-computed summary statistics (i.e. means, variances, and covariances)
    in place of individual participant data.
    Possible models include linear models for linear combinations, products, 
    and logical combinations of phenotypes.
    Implements methods presented in 
    Wolf et al. (2021) <doi:10.3389/fgene.2021.745901>
    Wolf et al. (2020) <doi:10.1142/9789811215636_0063> and 
    Gasdaska et al. (2019) <doi:10.1142/9789813279827_0036>.

The Prize-Collecting Steiner Tree problem asks to find a subgraph
    connecting a given set of vertices with the most expensive nodes and least
    expensive edges. Since it is proven to be NP-hard, exact and efficient algorithm
    does not exist. This package provides convenient functionality for obtaining an
    approximate solution to this problem using loopy belief propagation algorithm.

Functions and example data to teach and
  increase the reproducibility of the methods and code underlying 
  the Propensity to Cycle Tool (PCT), a research project and web application 
  hosted at <https://www.pct.bike/>. 
  For an academic paper on the methods,
  see Lovelace et al (2017) <doi:10.5198/jtlu.2016.862>.

Provides a comprehensive suite of tools for analyzing omics data. It includes
          functionalities for alpha diversity analysis, beta diversity analysis,
          differential abundance analysis, community assembly analysis, visualization of
          phylogenetic tree, and functional enrichment analysis. With a progressive
          approach, the package offers a range of analysis methods to explore and understand
          the complex communities. It is designed to support researchers and practitioners
          in conducting in-depth and professional omics data analysis.

Classes and methods for modelling and simulation of
    periodically correlated (PC) and periodically integrated time
    series.  Compute theoretical periodic autocovariances and related
    properties of PC autoregressive moving average models. Some original
    methods including Boshnakov & Iqelan (2009)
    <doi:10.1111/j.1467-9892.2009.00617.x>, Boshnakov (1996)
    <doi:10.1111/j.1467-9892.1996.tb00281.x>.