Instead of using the usual calculations for the variance components of our model, we use a method that is robust to heteroscedasticity and serial correlation. The usual estimate for the variance-covariance matrix is as follows:
- Title Page
- Copyright and Credits
- About Packt
- Contributors
- Preface
- Getting Started with R and Statistics
- Introduction
- Technical requirements
- Maximum likelihood estimation
- Calculating densities, quantiles, and CDFs
- Creating barplots using ggplot
- Generating random numbers from multiple distributions
- Complex data processing with dplyr
- 3D visualization with the plot3d package
- Formatting tabular data with the formattable package
- Simple random sampling
- Creating diagrams via the DiagrammeR package
- C++ in R via the Rcpp package
- Interactive plots with the ggplot GUI package
- Animations with the gganimate package
- Using R6 classes
- Modeling sequences with the TraMineR package
- Clustering sequences with the TraMineR package
- Displaying geographical data with the leaflet package
- Univariate and Multivariate Tests for Equality of Means
- Linear Regression
- Introduction
- Computing ordinary least squares estimates 
- Reporting results with the sjPlot package 
- Finding correlation between the features 
- Testing hypothesis 
- Testing homoscedasticity 
- Implementing sandwich estimators 
- Variable selection 
- Ridge regression 
- Working with LASSO 
- Leverage, residuals, and influence 
- Bayesian Regression
- Nonparametric Methods
- Robust Methods
- Time Series Analysis
- Introduction
- The general ARIMA model 
- Seasonality and SARIMAX models 
- Choosing the best model with the forecast package 
- Vector autoregressions (VARs)  
- Facebook's automatic Prophet forecasting  
- Modeling count temporal data 
- Imputing missing values in time series  
- Anomaly detection 
- Spectral decomposition of time series 
- Mixed Effects Models
- Predictive Models Using the Caret Package
- Bayesian Networks and Hidden Markov Models
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