gragusa Goto Github PK

674

In progress: lecture notes for time-series econometrics

academic-doom-emacs-config

My doom emacs configuration files

adabag

Applies multiclass AdaBoost.M1, AdaBoost-SAMME and Bagging

advmetrics

analysis-of-nasa-turbofan-degradation-data

Exploratory Data Analysis of the engine simulation data in dataset 6, subset FD001, from https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/

apple-quartz-filters

applied-ml

📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.

ase

Applied Statistics and Econometrics

awesome-alfred-workflows

A curated list of awesome alfred workflows

awesome-quarto

A curated list of Quarto talks, tools, examples & articles! Contributions welcome!

azure-quickstart-templates

Azure Quickstart Templates

bayesiantools.jl

Julia package designed to handle product distributions

bcui-emacs

Automatically exported from code.google.com/p/bcui-emacs

bookdown

Authoring Books with R Markdown

brizzled

Source for my Octopress-generated blog

build-emacs-for-macos

Somewhat hacky script to automate building of Emac.app on macOS.

businessanalyticscourse

This is a repository to organize the teaching material for a new course at Monash University

clusters.jl

comptools

Github repository of Computational Tools for Macroeconometrics

coursera-r-programming

Notes from the R programming Coursera course

coursera-statistics-one--r

Statistics One is designed to be a comprehensive yet friendly introduction to fundamental concepts in statistics. Comprehensive means that this course provides a solid foundation for students planning to pursue more advanced courses in statistics. Friendly means exactly that. The course assumes very little background knowledge in statistics and introduces new concepts with several fun and easy to understand examples. This course is, quite literally, for everyone. If you think you can't learn statistics, this course is for you. If you had a statistics course before but feel like you need a refresher, this course is for you. Even if you are a relatively advanced researcher or analyst, this course provides a foundation and a context that helps to put one’s work into perspective. Statistics One also provides an introduction to the R programming language. All the examples and assignments will involve writing code in R and interpreting R output. R software is free! What this means is you can download R, take this course, and start programming in R after just a few lectures. That said, this course is not a comprehensive guide to R or to programming in general.

courses

Course materials for the Data Science Specialization: https://www.coursera.org/specialization/jhudatascience/1

covariancematrices.jl

Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation for Julia.

coverage.jl

Take Julia test coverage results and bundle them up in JSONs

covid-19

COVID-19 Italia - Monitoraggio situazione

csminwel

Chris Sims' optimization routine

csminwel.jl

Optimization in Julia

custom-numbered-blocks

quarto extension for user defined, automatically numbered div blocks / latex environment

decisiontree.jl

Julia implementation of Decision Tree (CART) and Random Forest algorithms

dependentbootstrap.jl

Statistical bootstrap procedures for time-series data