Venkata Duvvuri's repositories
tableGAN
tableGAN is a synthetic data generation technique (Data Synthesis based on Generative Adversarial Networks paper) based on Generative Adversarial Network architecture (DCGAN).
learningPySpark
Code base for the Learning PySpark book (in preparation)
RBeast
R tools for posterior analyses using BEAST
fastbaps
A fast approximation to a Dirichlet Process Mixture model (DPM) for clustering genetic data
tcoffee
A collection of tools for Multiple Alignments of DNA, RNA, Protein Sequence
beast2
Bayesian Evolutionary Analysis by Sampling Trees
PhyDyn
PhyDyn: Epidemiological modelling in BEAST
pyvolve
Python library to simulate evolutionary sequence data
InfluenzaGeometry
Code and data for Dalziel et al. 2018 Science
beast-mcmc
Bayesian Evolutionary Analysis Sampling Trees
EpiModel-Gallery
Gallery of Network-Based Epidemic Model Templates for EpiModel
Structured-birth-death-model
Population structure using the multi-type birth-death model
NELSI
This is the repository for NELSI: Nucleotide Evolutionary rate Simulator
sismid
Pathogen evolution, selection and immunity
gbmunge
Munge GenBank files into FASTA and tab-separated metadata
machine-learning-project-walkthrough
An implementation of a complete machine learning solution in Python on a real-world dataset. This project is meant to demonstrate how all the steps of a machine learning pipeline come together to solve a problem!
datasharing
The Leek group guide to data sharing
epitope-prediction
R package implementing a simple method for CD8 T cell epitope prediction by MHC class I binding for HLA and other MHC-I molecules.
hack-university-machine-learning
Hack University course "Introduction to Machine Learning" sylabus, course material, python examples, resource links, and data sets
MASTER
A versitile simulation engine for stochastic population dynamics models.
2015-CeskyKrumlov-Phylodynamics
Český Krumlov 2015 - Phylodynamics
zombies
Code for the 2012 APHA Learning Institute "A Gentle Introduction to Mathematical Modeling: Lessons from the Living Dead"
dynamics-practical
Inferring spatiotemporal dynamics of the H1N1 influenza pandemic from sequence data