Packaged: 2022-09-02 09:02:51 UTC; michaelgrayling

Title: PHATE - Potential of Heat-Diffusion for Affinity-Based
Authors@R: c(person("Krishnan", "Srinivasan", email = "krishnan.srinivasan@yale.edu", role = c("aut")),
Description: 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 preprint on bioRxiv
Imports: methods, stats, graphics, reticulate (>= 1.7), ggplot2

Imports: methods, stats, graphics, reticulate (>= 1.4), ggplot2

Imports: methods, stats, graphics, reticulate (>= 1.8), ggplot2

stem cells, human embryonic stem cells, and bone marrow samples, check out our publication in Nature Biotechnology
Packaged: 2019-12-05 18:22:02 UTC; scottgigante

Imports: methods, stats, graphics, reticulate (>= 1.8), ggplot2,
Packaged: 2020-05-22 00:21:03 UTC; scottgigante

Description: A package for modelling anthropogenic deforestation

Description: phcfM is an R package for modelling anthropogenic
deforestation. It was initially developed to obtain REDD
baseline scenarios of deforestation for the "programme
holistique de conservation des forets a Madagascar" (from which
the package is named after). Parameter inference is done in a
hierarchical Bayesian framework. Markov chains Monte Carlo
(MCMC) are coded in C++ using the Scythe statistical library to

deforestation. It was initially developed to obtain REDD+

Title: Permutation Testing in High-Dimensional Linear Models
Description: Provides permutation methods for testing in high-dimensional linear models.
The tests are often robust against heteroscedasticity and non-normality
and usually perform well under anti-sparsity.

Description: The package for drawing pretty heatmaps in R. The ordinary
heatmap function in R has several drawbacks, when it comes to
producing publication quality heatmaps. It is hard to produce
pictures with consistent text, cell and overall sizes and
shapes. The function pheatmap tries to alleviate the problems
by offering more fine grained control over heatmap dimensions