1: Libraries and functions for reading data

• Unit covers reading data from excel and other file formats using a number of libraries.
• Day 1 Supplemental materials

2: Data Programming

• Unit covers dplyr, reshape2, etc..
• Day 2 Supplemental materials
• Resource: RStudio Data Wrangling cheatsheet

3. ggPlot2

• Unit covers qplot and ggplot functions.
• Supplemental materials

4. Principal Components analysis and Eigen decomposition and SVD

• Unit covers PCA, SVD, eigen decomposition, and projection into subspaces.
• Supplemental materials

5. Psychometrics

• Unit covers psychometrics, including test-retest reliability, ICC, Cronbach's alpha, and Cohen's Kappa.
• Supplemental materials

6. Power analysis

• Unit covers power analysis functions in R.
• Supplemental materials

7. Testing Multiple Dependent Variables, Hotellings T2, the MANOVA Procedure, and Canonical Correlation Analysis (CCA)

• Unit covers MANOVA and methods for testing multiple DVs.
• Supplemental materials

8. Generalized Linear Regression

• Unit covers Generalized linear regression, ML estimation, weighted regression,.
• Supplemental materials

9. Logistic regression

• Unit covers logistic regression, over/under-dispersion, and probit regression.
• Supplemental materials

9b. Multinomial regression model

• Unit a short example on predicting multiple different classes using a variation on the logistic model.
• Supplemental materials

10-11. Item Response Theory

• Unit covers using Item Response theory and Rasch Model to do psychometrics.
• Supplemental materials

Tutorials on Shiny

• Basic materials covering Shiny
• Supplemental materials

12. Classification with LDA and QDA

• Unit covers using LDA and QDA for simple classifier models.
• Supplemental materials

13. Classification with trees and random forests

• Unit covers using decision/partition trees, random forests, and KNN classification methods.
• Supplemental materials

13b. K-nearest neighbor

• Unit covers KNN classification methods.
• Supplemental materials

14. Naive Bayes Classification

• Unit covers using Naive Bayes classification.
• Supplemental materials

14b. Support Vector Machines

• Unit covers support vector machines.
• Supplemental materials

15a. Artificial Neural Networks

• Unit covers simple artificial neural networks.
• Supplemental materials

16. Multi-dimensional Scaling

• Unit covers metric and non-metric MDS.
• Supplemental materials

18. Clustering Methods

• Unit covers hierarchical and finite clustering methods.
• Supplemental materials

19. Model-based Clustering Methods

• Unit covers Model-based clustering and finite mixture modeling using mclust and flexmix.
• Supplemental materials

20. Mixtures of regression models

• Unit covers mixtures of regresions with flexmix.
• Supplemental materials

21. Exploratory Factor Analysis

• Unit covers EFA using factanal and psych::fa, including rotations. li>
• Supplemental materials

22. Confirmatory Factor Analysis

• Unit using lavaan to do confirmatory factor analysis and latent variable models.
• Supplemental materials

23. Correspondence Analysis and Multiple Correspondence Analysis

• Unit using mass::corresp and ca::ca ca::mjca to do correspondence analysis.
• Supplemental materials