# STATS 506, Fall 2023

Computational Methods and Tools in Statistics

## Course Material

### Syllabus

### Assignments

Preliminary Assignment - Git/Github

Problem Set 1 due Sep 12

Problem Set 1 Solutions

Problem Set 2 due Sep 26

Problem Set 2 Solutions

Problem Set 3 due Oct 10

Problem Set 3 Solutions

Problem Set 4 due Oct 24

Problem Set 4 Solutions

Problem Set 5 due Nov 21

Problem Set 5 Solutions

Problem Set 6 due Dec 5

Problem Set 6 Solutions

### Midterm & Final Project

### Lecture Notes

01 - Introduction to R

02 - Quarto and RMarkdown

03 - Version Control and Git

04 - Vectorization and Monte Carlo

05 - Debugging Function in R

06 - Fitting Models in R

07 - Stata

08 - SQL

09 - Regular Expressions

10 - Other Statistical Software

11 - The Tidyverse

12 - R Visualization - Base R and **ggplot2**

13 - R Visualization - **plotly** (Version with no plots)

14 - SAS Fundamentals (sas_oda.zip, sas_oda2.zip - only needed if you are *not* using SAS OnDemand)

15 - R’s OOP

16 - **data.table**

17 - Parallel Processing

18 - Futures

19 - High Performance Computing

20 - Unix Skills

21 - R Packages, **polypack** repo

### Scripts from Lecture

These are the scripts I work on during class. I make no promises that these are complete or useful.

### Case Studies

Case Study 1: Stata (9/21/23)

Files: 01-import.do, 02-merge.do, 03-variables.do, 04-bivariate.do, 05-analysis.do

Case Study 2: MS Daily Data (10/24/23)

Files: msdaily-tidyverse.R, msdaily-baseR.R

Case Study 3: `InfinitySparseMatrix`

implementation (11/9/23)

- InfinitySparseMatrix.R

- Matching slides - Some slides with more information on matching and **optmatch** for more context on InfinitySparseMatrices..

### Miscellanous

Developing a Consistent R Style

Response Surface script

`mc.cores`

argument to `mclapply`