mguindani / Lectures
This repository contains slides from some of the classes I have taught in my career. The repository will be updated from time to time. If you are interested in the .tex files and other material (e.g., lab material) for your own course preparation, feel free to reach out directly to me.
☆33Updated 2 months ago
Alternatives and similar repositories for Lectures
Users that are interested in Lectures are comparing it to the libraries listed below
Sorting:
- 🎯 Targeted Learning in R: A Causal Data Science Handbook☆59Updated 6 months ago
- Lecture Notes on Statistical Inference☆76Updated 6 months ago
- Trees are all you need☆114Updated 11 months ago
- Friedman's H-statistics☆30Updated last week
- Supplemental R Examples on Applied Conformal Prediction. These examples follow Practical Guide to Applied Conformal Prediction in Python,…☆42Updated last year
- Compute SHAP values for your tree-based models using the TreeSHAP algorithm☆87Updated 9 months ago
- mixgb: multiple imputation through XGBoost☆23Updated this week
- Survival analysis workshop using R☆51Updated 5 years ago
- 📦 R package for Supplemental Materials for the Bayes Rules! Book☆72Updated 3 years ago
- Stochastic tree ensembles (BART / XBART) for supervised learning and causal inference☆39Updated last week
- ☆14Updated 4 months ago
- A high-performance R for supervised and unsupervised machine learning evaluation metrics witten in 'C++'.☆23Updated last week
- miceRanger: Fast Imputation with Random Forests in R☆68Updated 2 years ago
- Building a Survival Model in Stan.☆57Updated last year
- Introduction to the mosts common estimators and computation in causal inference for epidemiologists: A tutorial☆38Updated 4 years ago
- An R package for nonparametric covariance matrix estimation in high dimensions☆13Updated last year
- Nested cross-validation for accurate confidence intervals for prediction error.☆41Updated 2 years ago
- ☆14Updated last month
- Slides to accompany Forecasting: Principles and Practice, 3rd edition☆73Updated last year
- Interpretability methods to analyze the behavior and individual predictions of modern neural networks in R.☆30Updated last month
- That's weird: Anomaly detection using R☆42Updated 5 months ago
- Conformal Time Series Forecasting Using State of Art Machine Learning Algorithms☆25Updated 2 years ago
- intro to ML☆28Updated 3 months ago
- Testing hypotheses through statistical models opens a universe of new possibilities. Learn how to improve your daily work with this appro…☆82Updated last year
- Biostatistics resources : reading list, RMarkdown notebooks, and more!☆72Updated last week
- Surrogate Assisted Feature Extraction in R☆28Updated 2 years ago
- This package is for visualising variable importance and variable interaction.☆23Updated 9 months ago
- 🤠 📿 The Highly Adaptive Lasso☆50Updated 5 months ago
- Interpretable and model-robust causal inference for heterogeneous treatment effects using generalized linear working models with targeted…☆24Updated 3 years ago
- ☆43Updated 2 years ago