* Several substantial changes to the CLI and modules with according [documentation updates](https://nolan-h-hamilton.github.io/ROCCO/)
* Additional features for scaling and filtering samples' count data prior to scoring
* Multiple options to measure central tendency and dispersion during scoring:
* Central tendency: median(still default), trimmed mean via `scipy.stats.tmean`, general `np.quantile`, traditional mean, etc.
* Dispersion: median absolute deviation (still default), trimmed standard deviation via `scipy.stats.tstd`, iqr, etc.
* Post-scoring parametric-sigmoid transformation (`rocco.parsig()`, `--use_parsig`)
* Can be useful to promote integrality of solutions from the relaxation directly
* Split modules for obtaining read count tracks from BAM files (`readtracks.py`)
* ROCCO now relies exclusively on [OR-Tools](https://github.com/google/or-tools) to solve the relaxed optimization problem
* glop -- simplex method, ensures vertex solutions
* pdlp (default) -- first-order PDHG method scalable to massive problems. Minimal accuracy degradation in the context of ROCCO.