Ontology-Informed Phylogenetic Comparative Analyses
Adding noise to MDS from one stochastic character map
Add pseudodata
Plot Picture
Color bar
Calculate KDE derivative over edges
Reading unsummarized simmap for one tree
Reading unsummarized simmap for a list of trees
Make edge profiles for plotting
Plot edge profiles and contMap
Estimate bandwidth
Estimate the normalized Markov KDE
Estimate the unnormalized Markov KDE
Get characters that are the descendants of a selected ontology term
Get edges IDs from root to a given node.
Multiple character state colors
Get state name for contMap plotting.
Get state information about a given path.
Wrapper for getting vector layer IDs for multiple terms
Get vector layer IDs for single term
Calculate KDE integral over edges
Join neighboring edges in edge profiles.
KDE for unnormalized Markov kernel vectorized.
KDE for unnormalized Markov kernel.
Convert list to edge matrix
Get loess smoothing for the unnormalized Markov KDE
Make color palette for image plotting
Make color palette for image plotting with relative scale
Make contMap KDE object
Get NHPP data for all edges (Markov KDE)
Get NHPP data for a given edge (Markov KDE)
Assign colors to picture ID layers
Make posterior distribution of NHPP
Plot morphospace from MDS
Merge state bins over branch
Merge state bins over a tree
Merge state bins over a tree list
Multidimensional scaling of character states from one stochastic chara...
Normalize loess smoothing
Stack multiple discrete stochastic character map lists
PARAMO
Path hamming
Hamming distances for a tree
Hamming distances for a list of trees
Phylogenetic Non-Homogeneous Poisson Process (pNHPP) method
Get analytical posterior
Get distributions of analytical posterior
Retrieve all characters under a given set of terms
Reading stochastic character maps file from ReVBayes
Stack two discrete stochastic character maps.
Stack two discrete stochastic character map lists; x and y are the lis...
Provides new tools for analyzing discrete trait data integrating bio-ontologies and phylogenetics. It expands on the previous work of Tarasov et al. (2019) <doi:10.1093/isd/ixz009>. The PARAMO pipeline allows to reconstruct ancestral phenomes treating groups of morphological traits as a single complex character. The pipeline incorporates knowledge from ontologies during the amalgamation of individual character stochastic maps. Here we expand the current PARAMO functionality by adding new statistical methods for inferring evolutionary phenome dynamics using non-homogeneous Poisson process (NHPP). The new functionalities include: (1) reconstruction of evolutionary rate shifts of phenomes across lineages and time; (2) reconstruction of morphospace dynamics through time; and (3) estimation of rates of phenome evolution at different levels of anatomical hierarchy (e.g., entire body or specific regions only). The package also includes user-friendly tools for visualizing evolutionary rates of different anatomical regions using vector images of the organisms of interest.