Learning Microeconometrics with R

Focuses on the assumptions underlying the algorithms rather than their statistical properties
Presents cutting-edge analysis of factor models and finite mixture models.
Uses a hands-on approach to examine the assumptions made by the models and when the models fail to estimate accurately
Utilizes interesting real-world data sets that can be used to analyze important microeconomic problems
Introduces R programming concepts throughout the book.
Includes appendices that discuss many of the concepts introduced in the book, as well as measures of uncertainty in microeconometrics.