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IQ-TREE - Efficient phylogenomic software by maximum likelihood

  •    C++

The IQ-TREE software was created as the successor of IQPNNI and TREE-PUZZLE (thus the name IQ-TREE). IQ-TREE was motivated by the rapid accumulation of phylogenomic data, leading to a need for efficient phylogenomic software that can handle a large amount of data and provide more complex models of sequence evolution. To this end, IQ-TREE can utilize multicore computers and distributed parallel computing to speed up the analysis. IQ-TREE automatically performs checkpointing to resume an interrupted analysis. As input IQ-TREE accepts all common sequence alignment formats including PHYLIP, FASTA, Nexus, Clustal and MSF. As output IQ-TREE will write a self-readable report file (name suffix .iqtree), a NEWICK tree file (.treefile) which can be visualized by tree viewer programs such as FigTree, Dendroscope or iTOL.

Miscellaneous-R-Code - Code that might be useful to others for learning/demonstration purposes.

  •    R

This is a place for miscellaneous R and other code I've put together for clients, co-workers or myself for learning and demonstration purposes. The attempt is made to put together some well-commented and/or conceptually clear code from scratch, though most functionality is readily available in any number of well-developed R packages. Typically, examples are provided using such packages for comparison of results. I would say most of these are geared toward intermediate to advanced folks that want to dig a little deeper into the models and underlying algorithms. More recently, if it gets more involved, I usually just create a document of some kind rather than a standard *.R file, so you might check out the docs repo as well.

sem - :white_medium_small_square: <- :white_circle: Structural Equation Modeling from a broader context

  •    R

The first few chapters also serve as the basis of a workshop, and include a brief introduction to R that will be enough for one to follow along with the tools used (e.g. psych, lavaan, and mediation packages). The actual document can be found at https://m-clark.github.io/sem.

PyBGMM - Bayesian inference for Gaussian mixture model with some novel algorithms

  •    Python

Bayesian inference for Gaussian mixture model to reduce over-clustering via the powered Chinese restaurant process (pCRP). We use collapsed Gibbs sampling for posterior inference.

gaussian-mixture-model - Unsupervised machine learning with multivariate Gaussian mixture model which supports both offline data and real-time data stream

  •    Javascript

Unsupervised machine learning with multivariate Gaussian mixture model which supports both offline data and real-time data stream. For browser use, include dist/gmm.js file in your project. It will create a global variable GMM.

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