Implements our Bayesian phase I repeated measurement design that accounts for multidimensional toxicity endpoints from multiple treatment cycles. The package also provides a novel design to account for both multidimensional toxicity endpoints and early-stage efficacy endpoints in the phase I design. For both designs, functions are provided to recommend the next dosage selection based on the data collected in the available patient cohorts and to simulate trial characteristics given design parameters. Yin, Jun, et al. (2017) <doi:10.1002/sim.7134>.

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>.