A fast C code implementation of maximum likelihood calculations for a multi-peak OU process with independent alpha, sigma, and theta parameters for each peak on phylogenetic trees (an extension of the ouch model). Includes R package wrapper as interface to C code. Likelihood calculation presented in http://www.carlboettiger.info/files/phylo-covariance.pdf, later the basis for: Jeremy M. Beaulieu, Dwueng-Chwuan Jhwueng, Carl Boettiger and Brian O’Meara, (2012). Modeling Stabilizing Selection: Expanding the Ornstein-Uhlenbeck Model of Adaptive Evolution, Evolution 66 (8) 2369-2383. doi:10.1111/j.1558-5646.2012.01619.x
Test the regimes data structure
Modify to handle a single regime
Implement a transition density calculation where transitions occur at nodes alone <<<
Pagel & Meade, Huelsenbeck papers -- what about transitions at nodes?
BM not really subset of OU, as alpha = 0 not acceptible OU calculation.
Modify the OU model to handle appropriately small alpha with Taylor Expansion or as BM
Implement and test maximum likelihood searches over parameters against existing methods
Start implementing a basic MCMC solver!
nonlinear transition densities?
Test the stationary distribution?
This should probably be done mostly at the R level, enforcing the strict formats and conventions of the input (i.e. if not doing regimes, all states should be listed at 0. States must be numbered incrementally from 0 to n-1, where n is the number of regimes present.)
- Recursive Joint Prob method using BM, compare to analyticals
- Bayesian OU vs BM in RJMCMC
- Use Pagel's BayesTraits run to "paint tree" with uncertainty, then repeat OUCH analysis with uncertainty in branch color