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+HyPhy: Hypothesis testing using Phylogenies
+
+HyPhy is an open-source software package for the analysis of genetic
+sequences (in particular the inference of natural selection) using
+techniques in phylogenetics, molecular evolution, and machine learning.
+It features a rich scripting language for limitless customization of
+analyses. Additionally, HyPhy features support for parallel computing
+environments (via message passing interface).
+
+HyPhy was designed to allow the specification and fitting of a broad
+class of continuous-time discrete-space Markov models of sequence
+evolution. To implement these models, HyPhy provides its own scripting
+language - HBL, or HyPhy Batch Language, which can be used to develop
+custom analyses or modify existing ones. Importantly, it is not
+necessary to learn (or even be aware of) HBL in order to use HyPhy, as
+most common models and analyses have been implemented for user
+convenience. Once a model is defined, it can be fitted to data (using a
+fixed topology tree), its parameters can be constrained in user-defined
+ways to test various hypotheses (e.g. is rate1 > rate2), and simulate
+data from. HyPhy primarily implements maximum likelihood methods, but
+it can also be used to perform some forms of Bayesian inference (e.g.
+FUBAR), fit Bayesian graphical models to data, run genetic algorithms to
+perform complex model selection.
+
+Features
+- Support for arbitrary sequence data, including nucleotide, amino-acid,
+  codon, binary, count (microsattelite) data, including multiple
+  partitions mixing differen data types.
+- Complex models of rate variation, including site-to-site, branch-to-
+  branch, hidden markov model (autocorrelated rates), between/within
+  partitions, and co-varion type models.
+- Fast numerical fitting routines, supporting parallel and distributed
+  execution.
+- A broad collection of pre-defined evolutionary models.
+- The ability to specify flexible constraints on model parameters and
+  estimate confidence intervals on MLEs.
+- Ancestral sequence reconstruction and sampling.
+- Simulate data from any model that can be defined and fitted in the
+  language.
+- Apply unique (for this domain) machine learning methods to discover
+  patterns in the data, e.g. genetic algorithms, stochastic context free
+  grammars, Bayesian graphical models.
+- Script analyses completely in HBL including flow control, I/O,
+  parallelization, etc.
+
+Registration
+you are highly advised to fill the registration form found at:
+https://veg.github.io/hyphy-site/register/
+
+Citing
+Sergei L. Kosakovsky Pond, Simon D. W. Frost and Spencer V. Muse (2005)
+HyPhy: hypothesis testing using phylogenies.
+Bioinformatics 21(5): 676-679