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Lixiong Yang's Projects

awesome icon awesome

Awesome resources on Bioinformatics, data science, machine learning, programming language (Python, Golang, R, Perl) and miscellaneous stuff.

bayest-caviar icon bayest-caviar

An adaptive MCMC algorithm for Bayesian Threshold CAViaR model

c2plasso icon c2plasso

R package for the structural varying-coefficient regression and the pliable lasso

causalforest icon causalforest

DEPRECATED. See new generalized random forest package for up-to-date implementation.

causaltree icon causaltree

Working repository for Causal Tree and extensions

classo icon classo

A package implements Classifier-Lasso

clusterr icon clusterr

Gaussian mixture models, k-means, mini-batch-kmeans and k-medoids clustering

did icon did

Difference in Differences with Multiple Periods

doublemachinelearning icon doublemachinelearning

R codes for double machine learning proposed by Chernozhukov et al. (2018, Econometrics Journal)

ecomod_spml icon ecomod_spml

Machine learning for spatial data: This repository contains the R-scripts for the analysis described in the paper "Importance of spatial predictor variable selection in machine learning applications - Moving from data reproduction to spatial prediction" submitted to Ecological Modelling

errata-of-econometrics icon errata-of-econometrics

勘误表。《计量经济学-基于stata应用》,清华大学出版社,2024,01.杨利雄

financialsr icon financialsr

Extract and tidy SEC corporate financial data for analysis

gmm-lasso icon gmm-lasso

The scripts for simulation results in "Estimation of Sparse Structural Parameters with Many Endogenous Variables" (2016), Econometric Reviews.

leetcode icon leetcode

推荐刷题网站:https://www.lintcode.com/?utm_source=tf-github-lucifer2022 LeetCode Solutions: A Record of My Problem Solving Journey.( leetcode题解,记录自己的leetcode解题之路。)

midasml icon midasml

midasml package is dedicated to run predictive high-dimensional mixed data sampling models

midasr icon midasr

R package for mixed frequency time series data analysis.

ml_spatial icon ml_spatial

Case studies for testing the risk of overfitting and the need for variable selection in spatial (-temporal) predictive modelling

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