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covid-20

covid-21

Julia code to evaluate SIR models

covid19-agent-based-model

This repository contains the Python implementation of the agent-based model used to model the spread of COVID-19.

covid19-analyses

Example data analysis using covid-19 data

covid19-healthsystemcapacity

Open geospatial work to support health systems' capacity (providers, supplies, ventilators, beds, meds) to effectively care for rapidly growing COVID19 patient needs

covid19-japan-reff

covid19-scenario-modeling-hub

COVID-19 Scenario Modeling Hub

covid19-stata-tutorials

Files used in the online articles

covid19_agentbasedsimulation

COVID-ABS: An Agent-Based Model of COVID-19 Epidemic to Simulate Health and Economic Effects of Social Distancing Interventions

covid_bayesian_mcmc

Bayesian Markov Chain Monte Carlo Forecast for COVID-19

covidmodel

covidshinymodel

This is LEMMA v0.2. Our development model is LEMMA v0.3:

cpp-523-fall-2019

Course shell for CPP 523 Foundations of Program Evaluation I for Fall 2019.

cpp-529-spr-2020

Course shell for CPP 529 Data Practicum on Community Analytics for Spring 2020.

cqs-ml-stat-r

crashcoursecausalinference

Introduction to Causal Inference in Epidemilogy a Crash Course in Spanish

crd150.github.io

creating-maps-in-r

Introductory tutorial on graphical display of geographical information in R.

crimebythenumbers

This book introduces the programming language R and is meant for undergrads or graduate students studying criminology. R is a programming language that is well-suited to the type of work frequently done in criminology - taking messy data and turning it into useful information. While R is a useful tool for many fields of study, this book focuses on the skills criminologists should know and uses crime data for the example data sets.Notes for using R in criminology research

crimemapping_textbook_bookdown

Workbook for crime mapping course

critical-data-book

Code repository for the critical data book

cross-sectional-or-microeconometrics-hw-examples

This repository contains the supporting files for some HW assignments completed for a graduate course in Cross Sectional or Microeconometrics. The course followed Cameron & Trivedi's

crossfit

Implementation of Double/Debiased Machine Learning approach

crossreg

Crosswalk between Stata, R, and Python. Demonstrates/Implements OLS regression calculations in each environment.

crowdsourcing-class.github.io

Crowdsourcing and Human Computation (UPenn NETS 213)

crunchingnumbers

Matlab, Python, and R codes for crunchingnumbers(a)live

cs-e5710-bayesian-data-analysis

cs-for-psych

Course materials for PSYC 132: Introduction to Programming for Psychological Scientists

cs145-notebooks-2016

Public materials for the Fall 2016 offering of CS145

cs146---modern-computational-statistics