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‎docs/Project.toml

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Compose = "a81c6b42-2e10-5240-aca2-a61377ecd94b"
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Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
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Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
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DocumenterVitepress = "4710194d-e776-4893-9690-8d956a29c365"
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FASTX = "c2308a5c-f048-11e8-3e8a-31650f418d12"
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Fontconfig = "186bb1d3-e1f7-5a2c-a377-96d770f13627"
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Literate = "98b081ad-f1c9-55d3-8b20-4c87d4299306"

‎docs/make.jl

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using MolecularEvolution
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using Documenter, Literate
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using Documenter, DocumenterVitepress, Literate
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using Phylo, Distributions
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using Plots
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using Compose, Cairo, Fontconfig
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authors = "Ben Murrell <[email protected]> and contributors",
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repo = "https://github.com/MurrellGroup/MolecularEvolution.jl/blob/{commit}{path}#{line}",
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sitename = "MolecularEvolution.jl",
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format = Documenter.HTML(;
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prettyurls = get(ENV, "CI", "false") == "true",
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canonical = "https://MurrellGroup.github.io/MolecularEvolution.jl",
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edit_link = "main",
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format = DocumenterVitepress.MarkdownVitepress(;
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repo = "https://github.com/MurrellGroup/MolecularEvolution.jl",
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#=prettyurls = get(ENV, "CI", "false") == "true",=#
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deploy_url = "https://MurrellGroup.github.io/MolecularEvolution.jl",
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devbranch = "main",
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devurl = "dev",
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assets = ["assets/favicon.ico"],
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#=for local hosting
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md_output_path = ".",
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build_vitepress = false
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),
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clean = false,
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=#
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),
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pages = [
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"Home" => "index.md",
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"framework.md",
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"Core Concepts" => [
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"intro.md",
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"framework.md",
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"models.md",
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],
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"Methods & Algorithms" => [
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"simulation.md",
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"optimization.md",
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"ancestors.md",
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],
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"Extensions & Utilities" => [
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"IO.md",
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"generated/viz.md",
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"generated/update.md",
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],
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"examples.md",
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"IO.md",
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"models.md",
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"simulation.md",
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"optimization.md",
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"ancestors.md",
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"generated/viz.md",
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"generated/update.md",
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"api.md"
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],
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)
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‎docs/src/api.md

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# Full API
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## Index
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```@index
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```
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## Docstrings
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```@autodocs
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Modules = [MolecularEvolution]
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```

‎docs/src/index.md

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```@meta
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CurrentModule = MolecularEvolution
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```
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# MolecularEvolution
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Documentation for [MolecularEvolution](https://github.com/MurrellGroup/MolecularEvolution.jl).
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### A Julia package for the flexible development of phylogenetic models.
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MolecularEvolution.jl exploits Julia's multiple dispatch, implementing a fully generic suite of likelihood calculations, branchlength optimization, topology optimization, and ancestral inference. Users can construct trees using already-defined data types and models. But users can define probability distributions over their own data types, and specify the behavior of these under their own model types, and can mix and match different models on the same phylogeny.
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If the behavior you need is not already available in `MolecularEvolution.jl`:
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- If you have a new data type:
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- A `Partition` type that represents the uncertainty over your state.
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- `combine!()` that merges evidence from two `Partition`s.
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- If you have a new model:
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- A `BranchModel` type that stores your model parameters.
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- `forward!()` that evolves state distributions over branches, in the root-to-tip direction.
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- `backward!()` that reverse-evolves state distributions over branches, in the tip-to-root direction.
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And then sampling, likelihood calculations, branch-length optimization, ancestral reconstruction, etc should be available for your new data or model.
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### Design principles
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In order of importance, we aim for the following:
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- Flexibility and generality
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- Where possible, we avoid design decisions that limit the development of new models, or make it harder to develop new models.
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- We do not sacrifice flexibility for performance.
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- Scalability
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- Analyses implemented using `MolecularEvolution.jl` should scale to large, real-world datasets.
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- Performance
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- While the above take precedence over speed, it should be possible to optimize your `Partition`, `combine!()`, `BranchModel`, `forward!()` and `backward!()` functions to obtain competative runtimes.
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### Authors:
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Venkatesh Kumar and Ben Murrell, with additional contributions by Sanjay Mohan, Alec Pankow, Hassan Sadiq, and Kenta Sato.
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### Quick example: Likelihood calculations under phylogenetic Brownian motion:
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```julia
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using MolecularEvolution, Plots
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#First simulate a tree, using a coalescent process
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tree = sim_tree(n=200)
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internal_message_init!(tree, GaussianPartition())
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#Simulate brownian motion over the tree
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bm_model = BrownianMotion(0.0,1.0)
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sample_down!(tree, bm_model)
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#And plot the log likelihood as a function of the parameter value
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ll(x) = log_likelihood!(tree,BrownianMotion(0.0,x))
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plot(0.7:0.001:1.6,ll, xlabel = "variance per unit time", ylabel = "log likelihood")
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```@raw html
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---
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# https://vitepress.dev/reference/default-theme-home-page
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layout: home
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hero:
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name: "MolecularEvolution.jl"
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text: "Phylogenetic Modeling Framework"
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tagline: "A Julia package for flexible development of phylogenetic models"
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image:
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src: '/logo.png'
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actions:
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- theme: brand
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text: Intro
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link: /intro
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- theme: alt
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text: API reference
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link: /api
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- theme: alt
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text: View on Github
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link: https://github.com/MurrellGroup/MolecularEvolution.jl
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features:
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- title: Flexible Model Development
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details: Create custom evolutionary models with your own data types and probability distributions while leveraging the existing infrastructure for tree operations and likelihood calculations.
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link: /framework
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- title: Rich Model Library
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details: Includes standard models like JC69, K80, GTR for nucleotides, JTT and WAG for amino acids, MG94 and GY94 for codons, plus Brownian motion for continuous traits.
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link: /models
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- title: Powerful Tree Operations
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details: Optimize branch lengths, infer ancestral states, perform tree rearrangements (NNI), and sample topologies with MCMC.
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link: /optimization
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- title: Simulation Tools
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details: Generate phylogenies using both coalescent and birth-death processes, and simulate sequence evolution on those trees.
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link: /simulation
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---
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```
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![](figures/quick_example.svg)
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```@index
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```
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```@autodocs
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Modules = [MolecularEvolution]
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```@meta
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CurrentModule = MolecularEvolution
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```

‎docs/src/intro.md

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# Intro
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MolecularEvolution.jl exploits Julia's multiple dispatch, implementing a fully generic suite of likelihood calculations, branchlength optimization, topology optimization, and ancestral inference. Users can construct trees using already-defined data types and models. But users can define probability distributions over their own data types, and specify the behavior of these under their own model types, and can mix and match different models on the same phylogeny.
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5+
If the behavior you need is not already available in `MolecularEvolution.jl`:
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- If you have a new data type:
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- A `Partition` type that represents the uncertainty over your state.
8+
- `combine!()` that merges evidence from two `Partition`s.
9+
- If you have a new model:
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- A `BranchModel` type that stores your model parameters.
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- `forward!()` that evolves state distributions over branches, in the root-to-tip direction.
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- `backward!()` that reverse-evolves state distributions over branches, in the tip-to-root direction.
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And then sampling, likelihood calculations, branch-length optimization, ancestral reconstruction, etc should be available for your new data or model.
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### Design principles
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In order of importance, we aim for the following:
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- Flexibility and generality
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- Where possible, we avoid design decisions that limit the development of new models, or make it harder to develop new models.
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- We do not sacrifice flexibility for performance.
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- Scalability
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- Analyses implemented using `MolecularEvolution.jl` should scale to large, real-world datasets.
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- Performance
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- While the above take precedence over speed, it should be possible to optimize your `Partition`, `combine!()`, `BranchModel`, `forward!()` and `backward!()` functions to obtain competative runtimes.
25+
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### Authors:
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Venkatesh Kumar and Ben Murrell, with additional contributions by Sanjay Mohan, Alec Pankow, Hassan Sadiq, and Kenta Sato.
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### Quick example: Likelihood calculations under phylogenetic Brownian motion:
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```julia
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using MolecularEvolution, Plots
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#First simulate a tree, using a coalescent process
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tree = sim_tree(n=200)
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internal_message_init!(tree, GaussianPartition())
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#Simulate brownian motion over the tree
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bm_model = BrownianMotion(0.0,1.0)
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sample_down!(tree, bm_model)
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#And plot the log likelihood as a function of the parameter value
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ll(x) = log_likelihood!(tree,BrownianMotion(0.0,x))
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plot(0.7:0.001:1.6,ll, xlabel = "variance per unit time", ylabel = "log likelihood")
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```
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![](figures/quick_example.svg)

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