ace.treedata: Reconstructing ancestral states of discrete and continuous...
In arborworkflows/aRbor: An R package with useful functions for Arbor workflows

This function returns ancestral state estimates using a variety of methods

ace.treedata(
  td,
  charType = "detect",
  aceType = "marginal",
  discreteModelType = "ER",
  na.rm = "bytrait"
)

`td`	An object of class 'treedata'
`charType`	specifies the type of character, either: "discrete" a character with a discrete number of states "continuous" a continuously varying character "detect" will attempt to determine the data type from the data itself
`aceType`	specifies the method used to reconstruct ancestral character states: "marginal" marginal ancestral state reconstructions, which reconstruct each node integrating over all possibilities at all other nodes in the tree; this is typically the method used in the literature to reconstruce ACEs "joint" joint ancestral reconstructions, which give the configuration of ancestral states that together maximize the likelihood of the data given model parameters "mcmc" reconstruct ancestral states using Bayesian MCMC. Note that the discrete version of this doesn't seem to work, and even if it did work it is not a full MCMC ancestral state method "stochastic" create stochastic character map
`discreteModelType`	One of ER, SYM, or ARD; see geiger's fitDiscrete for full description
`na.rm`	How to deal with missing data. "bytrait" Drop species out trait-by-trait "all" Drop out species that have NAs for any trait