Tyler Ransom by tyleransom

Computer Code

Introductory slides that introduce Julia (outdated version here)
My Github repository, which introduces basic econometric models in Julia.
EM algorithm example: Code to generate data and estimate simple versions of the EM algorithm for estimation of models with discrete type-specific unobserved heterogeneity.

summarize.m: Mimics Stata's summarize command.
normalMLE.m: Estimates normal linear regression with heteroskedastic errors by maximum likelihood.
applyRestr.m: Allows for easy parameter restrictions in optimization problems (co-writen with Jared Ashworth).
clogit.m: Estimates conditional logit regression by maximum likelihood (co-writen with Jared Ashworth).
ologit.m: Estimates the ordered logit model by maximum likelihood.
EM algorithm example: Code to generate data and estimate simple versions of the EM algorithm for estimation of models with discrete type-specific unobserved heterogeneity.

Cape Town: a Beamer slide theme for LaTeX presentations.
Poster: a LaTeX poster example.
jole.bst: a BibTeX style file for formatting citations according to the style of the Journal of Labor Economics.
CV template: a template for producing a CV in LaTeX (PDF output)
Template for completing peer review: a template for producing materials for requests to serve as a peer reviewer
Minimal working example of how to include multiple bibliographies in a LaTeX document

Introduction to machine learning for social scientists: Slides from a short presentation that outlines what machine learning is and how social scientists can benefit from using its methods.
Practical tips for machine learning practitioners, via Pedro Domingos.
The Master Algorithm, written by Pedro Domingos.