Q1: Avian Flu Infection Simulation [30 points] Avian flu is a headache for the livestock industry around the world. In this question, we will simulate the virus infection and recovery for the chickens in a farm. We use a one-dimensional array, called int farm[SIZE] , represent a planer area in the farm (i.e., like an […]
VC2022 predefined macro RAND MAX, you may get a random value in the range [0, RA Read More »
5557 Final Exam (20 marks) (3:30pm – 6:00pm, April 23, 2024) • Use Eclipse or another IDE. • Name your project and class FinalYourFirstName. • You can use the data structure classes from the textbook/slides/class examples or the Oracle API; • You should submit ALL your code (xxx.java files only) and the screenshots to Blackboard.
6-4 Regression Discontinuity Design – Solutions Regression Discontinuity ## Loading required package: pacman April 09, 2024 # install packages # ———- if (!require(“pacman”)) install.packages(“pacman”) pacman::p_load(# Tidyverse packages including dplyr and ggplot2 tidyverse, rdd, # regression discontinuity design library tidymodels, # machine learning workflow (R’s version of Python’s sklearn) here) # run here to set working
CA 13 (Berkeley’s congressional district) not because they enjoy an incumbency a Read More »
6-3 Matching Methods – Solutions March 12, 2024 # libraries xfun::pkg_attach2(c(“tidyverse”, # load all tidyverse packages “here”, # set file path “MatchIt”, # for matching “optmatch”, # for matching “cobalt”)) # for matching assessment # chunk options —————————————————————- knitr::opts_chunk$set( warning = FALSE # prevent scientific notation # ———- options(scipen = 999) # prevents warning from
Introduction SuperLearner and LTMLE # Install packages if (!require(“pacman”)) install.packages(“pacman”) pacman::p_load(# Tidyverse packages including dplyr and ggplot2 tidyverse, set.seed(44) SuperLearner, tidymodels, For our final lab, we will be looking at the SuperLearner library, as well as the Targeted Maximum Likelihood Estimation (TMLE) framework, with an extension to longitudinal data structures. This lab brings together a
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6-7: Sensitivity Analysis and Bounds – Solutions April 18, 2024 Sensitivity Analysis Rationale So far in our explorations of observational studies we have considered various methods of accounting for measured confounders W as well as some cases in which we can account for unmeasured confounders U (e.g. if we have measured a suitable instrumental variable).
## Loading required package: pacman 6-6 DiD and Synthetic Control April 03, 2024 he ‘rlang’ pac # Install packages if (!require(“pacman”)) install.packages(“pacman”) # We are using a package (augsynth) that is not on CRAN, R packages on CRAN have to pass # some formal tests. Always proceed with caution if a packages is not on
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R Refresher: Notebooks, Notation and Visualization UC Berkeley Social 273M: Computational Social Science, Part B Spring 2021 Learning Objectives 2 Basic R Commands 2 Importing and Manipulating Data 5 A note on data.table vs data.frame and dplyr 9 Generating Random Numbers 9 ggplot 11 R Markdown 14 TheHeader…………………………………………. 14 Basics……………………………………………. 14 Making PDFs using R
6-5 Instrumental Variables – Solutions March 19, 2024 Causal Graph (DAG) Review Let’s briefly review what we know about directed acyclic graphs (DAGs) or causal graphs. In this lab, we will introduce the daggity and ggdag libraries for drawing DAGs in R. Firstly, it is imperative that we keep in mind that DAGs are essentially
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COM4521/COM6521 Parallel Computing with Graphical Processing Units (GPUs) COM4521/COM6521 Parallel Computing with Graphical Processing Units (GPUs) Assignment (80% of module mark) Deadline: 5pm Friday 17th May (Week 12) Starting Code: Download Here Document Changes Any corrections or changes to this document will be noted here and an update will be sent out via the course’s
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