Free metropolis software for mac

ClineFit fits genotypic data to equilibrium cline models developed by Nick Barton. ClineFit uses a numerical maximum likelihood algorithm (an MCMC method aka a Metropolis-Hastings algorithm aka a biased random walk), and returns maximum-likelihood estimates and 2-unit support limits.
ClineFit supports diploid, haplodiploid or sex-linked genotypic data using codominant loci.
ClineFit takes data in my own format (very similar to Swoffords BIOSYS) or Arlequin format. ClineFit requires that the location of each population sample be a number, placed as the last piece of information in the populations name.
ClineFitt also requires that you identify, as part of the name of each locus, the alleles that will be most frequent on the right side of the cline.

Main features:
clines with 2, 4, 6 & 8 primary shape parameters, including:
- center and width
- 4 parameters describing introgression tails on either side of the cline
- 2 parameters describing frequencies of asymptotic polymorphisms on each side of the cline, if they are not fixed.
- single or multiple markers
- disequilibrium estimates, from which dispersal and selection estimates are obtained in 6- & 8-parameter models
- models that omit parameters of your choice, such as the introgression tail on one side of the cline
- models that omit such parameters for some traits but not others, under your control
- models that combine parameters traits in ways that you control. For example, you can estimate a single center for all traits, or a unique center for each trait, or any combination in between.

Crux is structured as a set of Python modules, which makes it possible to quickly develop Python scripts that perform unique, non-canned analyses.
The Python language is elegant and powerful, but it does not have any high performance implementations, which does make it a challenge to use for scientific computing.
Crux is actually implemented as a combination of Python, Cython and C code. Cython is a superset of Python, with various features that allow compiled results to substantially out-perform pure Python code.
The result is that Crux supports a wide variety of users:
- End users can use canned scripts, either those included with Crux, or scripts developed for them by computer-savvy collaborators.
- Computer-savvy users can develop custom scripts that utilize the components of Crux in unforseen ways.
- Power users can use Crux as a component of a custom software package, or extend Crux itself. The source code is available under a very permissive license, with the intent that it be used.

Main features:
- Multifurcating trees can be manipulated at a low level. Various standard operations are implemented, such as canonization (ordering the tree according to a ranking of the taxa), collapsing of zero-length branches, Robinson-Foulds distance computation, and so on.
- Parsers are included for various file formats, including Newick trees, FASTA sequences, and PHYLIP distance matrices.
- Pairwise distances between sequences can be computed based on percent identity, or using methods that correct for multiple hits (Jukes-Cantor, Kimura, and logDet).
- The neighbor joining (NJ) and relaxed neighbor joining (RNJ) implementations are among the fastest in existence, along with Clearcut.
- Tree log-likelihoods can be computed under a variety of models, including all specializations of GTR+I+? and mixture models. Tree likelihoods can be computed in parallel via pthreads. Additionally, Crux is capable of simulating character data under any model its likelihood engine is capable of. The huge range of simulation options makes a canned command line interface like that of Seq-Gen rather unweildy, so simulation is perhaps the most obvious compelling reason to develop Crux scripts.
- Bayesian Markov chain Monte Carlo (MCMC) methods (with Metropolis coupling and optional MPI support for parallel computation) can sample among non-nested models using reversible model jumps. Polytomous trees can be sampled, also via reversible jumps. In fact, every model parameter that Cruxs MCMC implementation estimates can be expunged via reversible jumps.

System requirements:
- Xcode (to build Crux from source)