Fits multivariate (Brownian Motion, Early Burst, ACDC, Ornstein-Uhlenbeck and Shifts) models of continuous traits evolution on trees and time series.

Fits multivariate models of continuous traits evolution on trees

Fits multivariate (Brownian Motion, Early Burst, ACDC, Ornstein-Uhlenbeck and Shifts) models of continuous traits evolution on trees and time series. 'mvMORPH' also proposes high-dimensional multivariate comparative tools (linear models using Generalized Least Squares) based on penalized likelihood.  See
Clavel et al. (2015) <DOI:10.1111/2041-210X.12420> and Clavel et al. (2018) <DOI:10.1093/sysbio/syy045>.

Fits multivariate (Brownian Motion, Early Burst, ACDC, Ornstein-Uhlenbeck and Shifts) models of continuous traits evolution on trees and time series. 'mvMORPH' also proposes high-dimensional multivariate comparative tools (linear models using Generalized Least Squares and multivariate tests) based on penalized likelihood.  See
Clavel et al. (2015) <DOI:10.1111/2041-210X.12420> and Clavel et al. (2019) <DOI:10.1093/sysbio/syy045>.
R (>= 3.5.0), phytools, ape, corpcor, subplex
stats, spam, graphics, glassoFast, parallel, pbmcapply

Fits multivariate (Brownian Motion, Early Burst, ACDC, Ornstein-Uhlenbeck and Shifts) models of continuous traits evolution on trees and time series. 'mvMORPH' also proposes high-dimensional multivariate comparative tools (linear models using Generalized Least Squares and multivariate tests) based on penalized likelihood.  See
Clavel et al. (2015) <DOI:10.1111/2041-210X.12420>, Clavel et al. (2019) <DOI:10.1093/sysbio/syy045>, and Clavel & Morlon (2020) <DOI:10.1093/sysbio/syaa010>.

stats, utils, spam, graphics, glassoFast, parallel, pbmcapply

Multi-State Adaptive Dynamic Principal Component Analysis for
Multivariate Process Monitoring
c(person("Melissa", "Johnson", email = "melissa_johnson2@baylor.edu", role = c("aut")),
person("Gabriel", "Odom", email = "gabriel.odom@med.miami.edu", role = c("aut","cre")),
person("Ben", "Barnard", email = "ben_barnard@baylor.edu", role = c("aut")),
person("Karen", "Kazor", role = c("aut")),
person("Amanda", "Hering", email = "mandy_hering@baylor.edu", role = c("aut")))
Use multi-state splitting to apply Adaptive-Dynamic PCA (ADPCA) to
data generated from a continuous-time multivariate industrial or natural
process. Employ PCA-based dimension reduction to extract linear combinations
of relevant features, reducing computational burdens. For a description of
ADPCA, see <doi:10.1007/s00477-016-1246-2>, the 2016 paper from Kazor et al.
The multi-state application of ADPCA is from a manuscript under current
revision entitled "Multi-State Multivariate Statistical Process Control" by
Odom, Newhart, Cath, and Hering, and is  expected to appear in Q1 of 2018.
BMS, dplyr, lazyeval, plyr, rlang, utils, xts, zoo,
robustbase, graphics
Melissa Johnson [aut],
Gabriel Odom [aut, cre],
Ben Barnard [aut],
Karen Kazor [aut],
Amanda Hering [aut]