Implements the largeVis algorithm (see Tang, et al. (2016)  <https://arxiv.org/abs/1602.00370>) for visualizing very large high-dimensional datasets; very fast search for approximate nearest neighbors; outlier detection; optimized implementation of the HDBSCAN clustering algorithm; plotting functions for visualizing the above.
testthat, covr, knitr, rmarkdown, wesanderson, RColorBrewer,
dplyr, magrittr

Implements the largeVis algorithm (see Tang, et al. (2016)  <DOI:10.1145/2872427.2883041>) for visualizing very large high-dimensional datasets. Also very fast search for approximate nearest neighbors; outlier detection; and optimized implementations of the HDBSCAN*, DBSCAN and OPTICS clustering algorithms; plotting functions for visualizing the above.
ggforce, testthat, knitr, rmarkdown, png, dbscan
Orphaned and corrected on 2018-02-16 as check errors
were not corrected despite reminder.

Misue of BLAS_LIBS/LAPACK_LIBS.

Least Angle Regression, Lasso and Forward Stagewise
Trevor Hastie <hastie@stanford.edu> and Brad Efron
<brad@stat.stanford.edu>
Efficient procedures for fitting an entire lasso
sequence with the cost of a single least squares
fit. Least angle regression and infinitessimal forward
stagewise regression are related to the lasso, as
described in the paper below.

Efficient procedures for fitting an entire lasso sequence
with the cost of a single least squares fit. Least angle
regression and infinitessimal forward stagewise regression are
related to the lasso, as described in the paper below.

Efficient procedures for fitting an entire lasso sequence
with the cost of a single least squares fit. Least angle
regression and infinitesimal forward stagewise regression are
related to the lasso, as described in the paper below.

Efficient procedures for fitting an entire lasso
sequence with the cost of a single least squares
fit. Least angle regression and infinitesimal forward
stagewise regression are related to the lasso, as
described in the paper below.

Likelihood Analysis of Speciation/Extinction Rates from Phylogenies
laser implements maximum likelihood methods based on the birth-death
process to test whether diversification rates have changed over time.  The package
permits batch processing of phylogenies to generate null distributions of test statistics
and posterior distributions of parameter estimates.  Additional functions for manipulating
branching times from molecular phylogenies and for simulating branching times under constant-rate models
of diversification are provided.