Partition-Assisted Clustering and Multiple Alignments of
Networks
Implements partition-assisted clustering and multiple alignments of networks. It 1) utilizes partition-assisted clustering to find robust and accurate clusters and 2) discovers coherent relationships of clusters across multiple samples. It is particularly useful for analyzing single-cell data set. Please see Li et al. (2017) <doi:10.1371/journal.pcbi.1005875> for detail method description.
Rcpp (>= 0.12.2),igraph,parmigene,infotheo,dplyr, Rtsne,
ggplot2, ggrepel

Implements Partition-Assisted Clustering and Multiple Alignments of Networks. It 1) utilizes partition-assisted clustering to find robust and accurate clusters and 2) discovers coherent relationships of clusters across multiple samples. It is particularly useful for analyzing single-cell data set.

A function for clustering online datasets. The number
of cells is data-driven which need not to be chosen in advance by the user.
The method is introduced and fully described in Le Li, Benjamin Guedj and Sebastien Loustau (2016), "PAC-Bayesian Online Clustering" (arXiv preprint: <https://arxiv.org/abs/1602.00522>).

Kehui Chen <khchen@wald.ucdavis.edu> Matt Yang
<matyang@ucdavis.edu>
The core program of this package is Functional Principal
Component Analysis (FPCA), a key technique for functional data
analysis, for sparsely or densely sampled random trajectories
and time courses, via the Principal Analysis by Conditional
Estimation (PACE) algorithm.

Penalized and Constrained Lasso Optimization
c(person("Courtney", "Paulson", email = "cpaulson@rhsmith.umd.edu", role = c("aut", "cre")),person("Gareth","James",role="ctb"),person("Paat","Rusmevichientong",role="ctb"))
An implementation of both the equality and inequality constrained lasso
functions for the algorithm described in "Penalized and Constrained Optimization"
by James, Paulson, and Rusmevichientong (Journal of the American Statistical Association, 2019;
see <http://www-bcf.usc.edu/~gareth/research/PAC.pdf> for a full-text version of the paper).
The algorithm here is designed to allow users to define linear constraints (either equality
or inequality constraints) and use a penalized regression approach to solve the constrained
problem. The functions here are used specifically for constraints with the lasso formulation,
but the method described in the PaC paper can be used for a variety of scenarios. In addition
to the simple examples included here with the corresponding functions, complete code to
entirely reproduce the results of the paper is available online through the Journal of the
American Statistical Association.
Courtney Paulson [aut, cre],
Gareth James [ctb],
Paat Rusmevichientong [ctb]