Performs a qualitative analysis of one- and two-dimensional
   autonomous ordinary differential equation systems, using phase plane methods.
   Programs are available to identify and classify equilibrium points, plot the
   direction field, and plot trajectories for multiple initial conditions. In
   the one-dimensional case, a program is also available to plot the phase
   portrait. Whilst in the two-dimensional case, programs are additionally
   available to plot nullclines and stable/unstable manifolds of saddle points.
   Many example systems are provided for the user. For further details can be
   found in Grayling (2014) <doi:10.32614/RJ-2014-023>.

Functions to perform Bayesian inference on absorption time data for
             Phase-type distributions. The methods of Bladt et al (2003)
             <doi:10.1080/03461230110106435> and Aslett (2012)
             <https://www.louisaslett.com/PhD_Thesis.pdf> are provided.

General implementation of core function from phase-type
    theory. 'PhaseTypeR' can be used to model continuous and discrete
    phase-type distributions, both univariate and multivariate. The
    package includes functions for outputting the mean and (co)variance of
    phase-type distributions; their density, probability and quantile
    functions; functions for random draws; functions for
    reward-transformation; and functions for plotting the distributions as
    networks. For more information on these functions please refer to
    Bladt and Nielsen (2017, ISBN: 978-1-4939-8377-3) and Campillo Navarro
    (2019)
    <https://orbit.dtu.dk/en/publications/order-statistics-and-multivariate-discrete-phase-type-distributio>.

PHATE is a tool for visualizing high dimensional single-cell data
  with natural progressions or trajectories. PHATE uses a novel conceptual framework
  for learning and visualizing the manifold inherent to biological systems in which
  smooth transitions mark the progressions of cells from one state to another.
  To see how PHATE can be applied to single-cell RNA-seq datasets from hematopoietic
  stem cells, human embryonic stem cells, and bone marrow samples, check out our publication in Nature Biotechnology
  at <doi:10.1038/s41587-019-0336-3>.