Name: Feng Li
Type: User
Company: Peking University - Guanghua School of Management
Bio: { computing, forecasting and learning with massive machines } Coding in R, Python, Julia in combo with Spark. My Lab @kl-lab.
Twitter: f3ngli
Location: Beijing, China
Blog: https://feng.li/
Feng Li's Projects
Feng Li's emacs configure
This library adds LatexMk support to AUCTeX.
Code for paper: Block Hankel Tensor ARIMA for Multiple Short Time Series Forecasting (AAAI-20)
Toolkit for the estimation of hierarchical Bayesian vector autoregressions. Implements hierarchical prior selection for conjugate priors in the fashion of Giannone, Lenza & Primiceri (2015). Allows for the computation of impulse responses and forecasts and provides functionality for assessing results.
Catastrophe loss prediction with NLP
Covariate-dependent copula models
Distributed ARIMA Models
分布式存储与计算
Teaching Materials for Distributed Statistical Computing (大数据分布式计算教学材料)
Distributed least squares approximation (dlsa) implemented with Apache Spark
Distributed least squares approximation (dlsa) implemented with R
Distributions and Gradients
Feng's dot files
Distributed Quantile Regression by Pilot Sampling and One-Step Updating
Distributed Statistical Modelling
Course material for Data Visualisation and Analytics
Feng Li's utility functions written in R
time series forecasting with image
Online version of the review paper “Forecasting: theory and practice”.
my git study for students
GRATIS: GeneRAting TIme Series with diverse and controllable characteristics
Basic Hadoop and Spark config files
HiveQL Jupyter Kernel
Feng Li's LaTeX Templetes for Funding Proposals (lattefun)
Efficient Bayesian Multivariate Surface Regression
Feng Li's variation of the "mvtnorm" package for multivariate normal and t distributions. Original homepage: http://mvtnorm.R-forge.R-project.org
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Emacs mode for navigating Python documentation through pydoc.