Advanced Linear Modeling in R

Linear modeling is the one of the most flexible methods available in inferential statistics, but this flexibility comes at a learning cost. This intermediate workshop in R provides an introduction in and best practices for linear modeling. These include: 

1. Customizing contrasts for factor variables
2. Robusticity against violations of glm assumptions
3. Stepwise regression
4. Heterogeneous model comparison
5. Logistic, Poisson, and other non-Gaussian models

 Requirements:

1. R version >= 3.2.0 installed
2. The following packages installed: 

• `MASS`
• `robust`
• `lmodel2`

  3.  Basic familiarity with `lm` is assumed