Sarah Lutteropp

I can implement that thing in an hour. It is super straight-forward.

I implemented it (took about 1.5 hours to code). Turns out it works pretty well! :-)

Here I printed the pseudologlikelihood and pseudo-BIC after each accepted move, to show how well this estimation performs: [example-run.txt](https://github.com/lutteropp/NetRAX/files/6404931/example-run.txt) Cool thing is that computing this pseudologlikelihood doesn't take much more...

My idea is to use pseudologlikelihood during initial prefiltering phase, and then move the top 10% of prefiltered candidates to the normal candidate ranking which uses real loglikelihood for scoring...

The simulated dataset, the RAxML-NG best ML tree, the PhyLINC inferred network, and networks inferrred by several NetRAX variants [datasets_phylinc_exp_smaller.zip](https://github.com/lutteropp/NetRAX/files/6986406/datasets_phylinc_exp_smaller.zip)

PhyLINC result on the PhD laptop, with max_reticulations set to 2, start from RAxML-NG best ML tree: Total inference runtime: 38365.49 seconds. Inferred a network with 2 reticulations. Printed multiple...

I am also including PhyloDAG in this comparison. Here the data to run PhyloDAG on the dataset: [data_for_phylodag.zip](https://github.com/lutteropp/NetRAX/files/6986463/data_for_phylodag.zip)

The PhyloDAG inference already finished. It took 3.308089 mins, ran only single-threaded, and inferred this network, with 1 reticulation and loglikelihood -17771.85: 

We need to also compare NetRAX and PhyloDAG on a larger dataset. Let's say 30 taxa, 3 reticulations. I am using the dataset from experiment D (the scrambling one) for...

In this archive, we have: - 0_0.nex: The input file for PhyloDAG for the 10 taxa 1 reticulation dataset - D.nex: The input file for PhyloDAG for the 30 taxa...