Second, we used Lagrange v. 20130526 [ 80] to test the complex models of directional biogeographic expansion and inferred the geographic origin of the clade based on a dispersal-extinction-cladogenesis (DEC) model employing a maximum likelihood framework for testing models of geographic range evolution.
While computational protein design completely from scratch (in silico) is not presently seen as reasonable, probably (as we stress later) because we cannot yet use it to predict dynamics effectively, significant progress continues to be made in a number of areas, including 'fold to function', combinatorial design, and a maximum likelihood framework for protein design.
The model incorporates the mathematical aspects of ontogenetic growth and allometry into a maximum likelihood framework for quantitative trait locus (QTL) mapping.
In this paper we develop a maximum likelihood framework for inferring the best tree topology under (general) Markov processes.
We employed a Maximum Likelihood framework for hypothesis testing [ 16] that provided insights into the evolutionary history of these three taxa.
The cF mixture model was implemented in a maximum likelihood framework for phylogenetic inference, by modifying the source code of RAxML-VI-HPC version 2.2.3 [ 26] to produce the 'Q matrix mixture RAxML' or QmmRAxML for short.
All of the regressions were fitted using the Generalized Linear Latent and Mixed Models (GLLAMM) procedure in Stata, which provides a maximum likelihood framework for models with unobserved components (latent variables), including multilevel models (Rabe-Hesketh and Skrondal 2008).
We evaluated all trees in a likelihood framework by computing likelihood scores for all trees using the LG matrix in PHYML v3.0 [ 105] with the following command (phyml -i 01-Input.phy -c 4 -m LG -a e -o lr -f e -u 01-Input.tre -v e -d aa -b 0 -s NNI).
Finally, inclusion of the literature data within a supermatrix context also presents analytical problems in that molecular sequence data are arguably best analyzed within a likelihood framework whereas maximum parsimony is better suited for non-molecular data (despite the existence of models of evolution for such data; for example, [ 85, 86]).
This was also supported by the program ProtTest version 2.0 [ 39], which determines the best-fit substitution model for amino acid data under a likelihood framework.
