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ait580

Course materials for AIT580 at GMU IST.

asnr

Animal Social Networks Repository

autodiff

automatic differentiation made easier for C++

baruuum.github.io

A beautiful Jekyll theme for academics

bda_course_aalto

Bayesian Data Analysis course at Aalto

cmdstanr

CmdStanR: the R interface to CmdStan

coarse-discourse

A large corpus of discourse annotations and relations on ~10K forum threads.

cppnumericalsolvers

a lightweight C++17 library of numerical optimization methods for nonlinear functions (Including L-BFGS-B for TensorFlow)

data.table

R's data.table package extends data.frame:

dissertate

Beautiful LaTeX dissertation templates.

ergm

Fit, Simulate and Diagnose Exponential-Family Models for Networks

fastglm

Fast glm fitting via RcppEigen

git-practice

Introduction to Git process

git_test

my first repository

helpful_stan_functions

igplot

Simple wrapper around `base::plot` to create network graphs from `igraph` objects and some other functionalities to plot networks

intro_bayes

intro_to_stats_2019

ipdgame

Iterated Prisoner's Dilemma Game Tournament for SOC3490 (Micro-Macro Processes)

knitr_case_studies

Inference case studies in knitr

leidenalg

Implementation of the Leiden algorithm for various quality functions to be used with igraph in Python.

linkrank_modularity

R code to calculate LinkRank modularity (Kim et al. 2012)

math

The Stan Math Library is a C++, reverse-mode automatic differentiation library designed to be usable, extensive and extensible, efficient, scalable, stable, portable, and redistributable in order to facilitate the construction and utilization of algorithms that utilize derivatives.

multithreading_stan

A short test of using within-chain multithreading in Stan

notebooks

Jupyter notebooks for the Natural Language Processing with Transformers book

openai-cookbook

Examples and guides for using the OpenAI API

perspectiveapi

Perspective is an API that uses machine learning models to score the perceived impact a comment might have on a conversation. For support, see: https://support.perspectiveapi.com/

prml

PRML algorithms implemented in Python

python-for-r-users-workshop

Material for the Python for R Users workshop

random_files