Pinned Repositories
asdfree
analyze survey data for free
awesome-research
:seedling: a curated list of tools to help you with your research/life; I built a front end around this repo, please click on the link
Chinese-STD-GB-T-7714-related-csl
GB/T 7714相关的csl以及Zotero使用技巧及教程。
ClashR_for_windows_chinese
ClashR_for_windows_chinese(ClashR中文版，支持windows）（非原创，From：https://github.com/BoyceLig）
ColliderApp
Visualization Collider Effect: ShinyApp
covid_age
COVerAGE-DB: COVID-19 cases, deaths, and tests by age and sex
demographic-methods
Materials for teaching demographic methods
ECON379
Program Evaluation for International Development
ginglam
blog
hexo-theme-next
🎉 Elegant and powerful theme for Hexo.
GingLam's Repositories
GingLam/asdfree
analyze survey data for free
GingLam/awesome-research
:seedling: a curated list of tools to help you with your research/life; I built a front end around this repo, please click on the link
GingLam/Chinese-STD-GB-T-7714-related-csl
GB/T 7714相关的csl以及Zotero使用技巧及教程。
GingLam/ClashR_for_windows_chinese
ClashR_for_windows_chinese(ClashR中文版，支持windows）（非原创，From：https://github.com/BoyceLig）
GingLam/ColliderApp
Visualization Collider Effect: ShinyApp
GingLam/covid_age
COVerAGE-DB: COVID-19 cases, deaths, and tests by age and sex
GingLam/demographic-methods
Materials for teaching demographic methods
GingLam/ECON379
Program Evaluation for International Development
GingLam/ginglam
blog
GingLam/hexo-theme-next
🎉 Elegant and powerful theme for Hexo.
GingLam/MathStatsCode
Codes for my mathematical statistics course
GingLam/pub_bias
Replication package for Winship and Zhuo (2017)
GingLam/publishment
GingLam/Replication-material-for-Ludwig-Bruederl-ASR2018
Replication files, Data and Stata Do files for replication of research article: Ludwig, V., & Brüderl, J. (2018). Is There a Male Marital Wage Premium? New Evidence from the United States. American Sociological Review, 83(4), 744–770. https://doi.org/10.1177/0003122418784909
GingLam/soc4all
Social Survey and Research Methods
GingLam/socviz
Support files for a data visualization course and book
GingLam/stat4soc
teaching website
GingLam/stats-illustrations
R & stats illustrations by @allison_horst
GingLam/Storage
graph
GingLam/summer-school
南京大学2019年“计算社会科学”论文工作坊
GingLam/teaching
GingLam/Titanic-Machine-Learning-from-Disaster
Start here if... You're new to data science and machine learning, or looking for a simple intro to the Kaggle prediction competitions. Competition Description The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships. One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class. In this challenge, we ask you to complete the analysis of what sorts of people were likely to survive. In particular, we ask you to apply the tools of machine learning to predict which passengers survived the tragedy. Practice Skills Binary classification Python and R basics
GingLam/website
academic website