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ipyvue

Jupyter widgets base for Vue libraries

ipywidgets_bokeh

Allows embedding of Jupyter widgets in Bokeh applications.

iqss-beamer-theme

A Beamer theme featuring IQSS orange.

iqss.emacs

Yet Another .emacs.d

iris

A powerful, format-agnostic, and community-driven Python library for analysing and visualising Earth science data

iris-builds

:eyes: Builds for Iris

irlba

Fast truncated singular value decompositions

irregulartimeseriesautoencoderpaper

ishares

isindy

implicit-SINDy code example from paper "Inferring Biological Networks by Sparse Identification fo Nonlinear Dynamics" http://ieeexplore.ieee.org/abstract/document/7809160/

isle

islr-python

An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code

ismir2018tutorialnotebooks

Jupyter notebooks for Ismir-2018 tutorial titled "Computational approaches for analysis of non-Western music traditions" by Serra, Clayton and Bozkurt

itch

Nasdaq Order Book Reconstructor

itch-1

Create and Parse NASDAQ Itch Messages in Python

itch-order-book

Fast implementation of an ITCH order book

itchy-rust

NASDAQ ITCH parser in Rust

iterated-sums-signature-py

An (inefficient) implementation of the iterated-sums signature <https://arxiv.org/abs/1906.05823>.

iternash

Iterative Nash equilibrium finder for simulating continuous games in Python/Coconut.

iterreg

Iterative regularization solvers for non strongly convex penalties : L1, low rank, etc.

ito_diffusions

Library for stochastic process simulation

ivalice

Boosting and ensemble learning in Python.

iventure

An interactive, browser-based probabilistic programming environment.

jackrabbit

Fast Motif Finder

jags_by_example

A set of JAGS examples implementing Bayesian modelling

jags_examples

A large set of JAGS examples using R

jagsexamples

Examples of statistical models implemented using JAGS

jamsim

A Java interface for R Based Microsimulation

janusgraph

JanusGraph: an open-source, distributed graph database

jarvis

Jarvis is a toolbox built on top of TensorFlow2.0 that allows developers and researchers to easily build neural networks in TensorFlow, particularly CTR models for large-scale advertising and recommendation scenarios.