Stars
Natural Gradient Boosting for Probabilistic Prediction
LightweightMMM 🦇 is a lightweight Bayesian Marketing Mix Modeling (MMM) library that allows users to easily train MMMs and obtain channel attribution information.
Tools to help developers and data scientists in sports
Automated, smooth, N'th order derivatives of non-uniformly sampled time series data
A helper library to connect into Amazon SageMaker with AWS Systems Manager and SSH (Secure Shell)
Simple script to export current AWS SSO credentials or run a sub-process with them
🏬 modelstore is a Python library that allows you to version, export, and save a machine learning model to your filesystem or a cloud storage provider.
mRMR (minimum-Redundancy-Maximum-Relevance) for automatic feature selection at scale.
Data and images for the NFL's Big Data Bowl 2022 Student Competition
Fit interpretable models. Explain blackbox machine learning.
Collection of R/Python scripts for programmatically accessing, manipulating, and saving spatial data.
Satellite Hydrology Bits Analysis And Mapping (SHBAAM)
Some Python Implementations of the Kalman Filter
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filte…
Source code for the course project "RoNGBa: A Robustly Optimized Natural Gradient Boosting Training Approach with Leaf Number Clipping"
Statistical Rethinking course at MPI-EVA from Dec 2018 through Feb 2019
Slides, code and data for "Scalable methods for large spatial data" course, Spring 2017
This is the repository for the API backend of theseventhman.net
An end-to-end tutorial creating an R Shiny app that uses the reticulate package with Python 3
An extension of XGBoost to probabilistic modelling
vspinu / dygraphs
Forked from rstudio/dygraphsR interface to dygraphs
Easily send great-looking HTML email messages from R
Data Science in the tidyverse, a two-day workshop @ rstudio:conf(2018)