gam

command line management for Google Workspace

Open-source visual effects designed in Godot, from our VFX Secrets course.

Automated line item generator for Prebid.js and Google Ad Manager

Love2D silly fangame of Baba Is You

Boosting algorithms for fitting generalized linear, additive and interaction models to potentially high-dimensional data. The current relase version can be found on CRAN (http://cran.r-project.org/package=mboost).

A document introducing generalized additive models.📈

👓 Functions related to R visualizations

A simple Windows app, using WPF, C# and GAM to make Chromebook management a little easier.

GAM User Offboarding Script

Functions for using mgcv for mixed models. 📈

R Scripts and supporting data for creating metamodels, as published in Ecosphere, and using them to generate global estimates of potato late blight risk based on climate data as published in Global Change Biology.

Front End Python program for GAMADV-XTD3 by Ross Scroggs.

The parsnip backend for GAM Models.

Spline regression and additive models (Simple introduction with C++). We provide an example of C++ implementation of additive regression splines through Rcpp and RcppArmadillo.

unfold+mixed models = unmixed | alpha version!

Google Apps Manager in a Docker container

This repo explains how LuckyDay configured their G-Suite domain to allow access to multiple AWS accounts using SAML federation. Also included is a small python tool that allows you to manage the access levels for multiple users easily via a single command.

C library which implements reading resources used in Fallout™.

Resources and Guides for our Google Workspace

Code library for common machine learning algorithms

GAM-based Spatial Surplus Production Models

Script realizado en Bash para facilitar la creación de cuentas educativas en G-Suite for Education usando la información de la plataforma Séneca (Andalucía).

Gammit

Powershell functions and example scripts to sync from Infinite Campus to Google Classroom

GAM-based model that predicts FIP based on expected whiff rate, command and expected contact from Statcast data

Thesis Chatper 3: This repository compares Spatial Interpolation methods to predict Seoul's air pollution

GAM with Super Learner smoother

Boosting models for fitting generalized additive models for location, shape and scale (GAMLSS) to potentially high dimensional data. The current relase version can be found on CRAN (https://cran.r-project.org/package=gamboostLSS).