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TreeSearch (version 0.4.3)

Phylogenetic Tree Search Using Custom Optimality Criteria

Description

Searches for phylogenetic trees that are optimal using a user-defined criterion. Handles inapplicable data using the algorithm of Brazeau, Guillerme and Smith (2019) . Implements Profile Parsimony (Faith and Trueman, 2001) , and Successive Approximations (Farris, 1969) .

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Install

install.packages('TreeSearch')

Monthly Downloads

702

Version

0.4.3

License

GPL (>= 3)

Issues

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Repository

https://github.com/ms609/TreeSearch

Homepage

https://ms609.github.io/TreeSearch

Maintainer

Martin R. Smith

Last Published

July 9th, 2020

Functions in TreeSearch (0.4.3)

Information content per step

Calculate parsimony score with inapplicable data

Amount of information in each character

CharacterLength

Character length

Implied weights parsimony Score

Report the character weightings associated with a Morphy object

Label nodes with jackknife support values

MorphyTreeLength

Calculate parsimony score with inapplicable data

MorphyErrorCheck

Check for error whilst modifying Morphy object

Information Content Steps

Logistic Points Extract points from a fitted model

Convert a number to binary

RandomTreeScore

Parsimony score of random postorder tree

Rearrange edges of a phylogenetic tree

MorphyBootstrap

Ratchet bootstrapper

Subtree Pruning and Rearrangement (SPR)

WithOneExtraStep

Number of trees with one extra step

Parsimony Ratchet

Nearest Neighbour Interchange (NNI)

Search for most parsimonious trees

Initialize a Morphy Object from a phyDat object

StopUnlessBifurcating

Check that all nodes in a tree are bifurcating.

Tree suboptimality

Extract character data from dataset

SuccessiveApproximations

Tree Search using Successive Approximations

RandomMorphyTree

Random postorder tree

inapplicable.citations

Thirty Datasets with Inapplicable data

UniqueExceptHits

Unique trees (ignoring 'hits' attribute)

PrepareDataProfile

Prepare data for Profile Parsimony

Profile Parsimony Score

SuccessiveWeights

Successive Weights

congreveLamsdellMatrices

100 simulated data matrices

mpl_attach_symbols

Attach a caller-specified list of symbols.

mpl_delete_rawdata

Deletes the caller-input data.

mpl_delete_Morphy

Destroys an instance of a Morphy object.

mpl_apply_tipdata

Commits parameters prior to nodal set calculations.

inapplicable.datasets

Thirty Datasets with Inapplicable data

mpl_second_up_recon

Finalises the ancestral state reconstructions for characters with inapplicable values.

mpl_set_num_internal_nodes

Sets the number of internal nodes in the dataset

mpl_attach_rawdata

Attach raw character state data (i.e. tip data).

mpl_get_numtaxa

Retrieve the number of taxa (rows) in the dataset.

Creates a new instance of a Morphy object

mpl_get_symbols

Retrieves the current list of symbols.

mpl_get_num_internal_nodes

Gets the number of internal nodal reconstruction sets being used by MorphyLib.

SetMorphyWeights

Set the character weightings associated with a Morphy object

Destroy a Morphy Object

inapplicable.phyData

Thirty Datasets with Inapplicable data

mpl_set_parsim_t

Sets a character's parsimony function type

SingleCharMorphy

Morphy object from single character

mpl_second_down_recon

Performs the second nodal reconstructions for characters with inapplicability.

mpl_init_Morphy

Sets up the dimensions of the dataset.

summary.morphyPtr

Details the attributes of a morphy object

mpl_set_charac_weight

Set the weight of a character in the dataset

mpl_translate_error

Converts a numeric error code to human-readable format

Tree bisection and reconnection (TBR)

Initial update of tip values following uppass reconstruction.

Tree topology for matrix simulation

mpl_update_lower_root

Updates the nodal sets for a lower ('dummy') root node

TBR Warning Print a warning and return given tree

mpl_first_down_recon

Reconstructs the first (downpass) nodal reconstructions

mpl_get_charac_weight

Retrieve the weight of a character in the dataset

mpl_get_num_charac

Retrieve the number of character (columns) in the dataset.

mpl_first_up_recon

Reconstructs the second (uppass) nodal reconstructions.