Overview | Format | Details | Metainformation | Main text | Links | Working with task view files in R | Dealing with archived packages | Converting the legacy XML format to R/Markdown

Overview | MOB: Model-based recursive partitioning | A new implementation in R | Illustrations | Setting up a new mobster | Conclusion

Introduction | Beta regression | Implementation in R | Beta regression in practice | The basic model: Estimation, inference, diagnostics | Prater's gasoline yield data | Household food expenditures | Variable dispersion model | Selection of different link functions | Further replication exercises | Dyslexia and IQ predicting reading accuracy | Structural change testing in beta regressions | Summary | Acknowledgments | References

Introduction | A brief review of beta regression | Implementation in R | Extensions | Bias correction and reduction in beta regressions | Preamble | Generic framework | Bias correction and bias reduction for beta regressions | Implementation in betareg | Beta regression trees | Finite mixtures of beta regressions | Illustrative application | Bias correction and reduction | Beta regression tree | Latent class beta regression | Conclusions | Acknowledgments | Appendix: Bias correction/reduction for gasoline yield data | References

Things that can go wrong | False positives | False negatives | Examples | Z-test | Power for Z-test

Problem setup | Null hypothesis and test statistic | Calculating p-values | Using the binom.test() function

Problem setup | Assumption checking | Null hypothesis and test statistic | Calculating p-values | Using the t.test() function

Problem setup | Assumption checking | Null hypothesis and test statistic | Calculating p-values | Rejection regions | Power and sample size calculations | Formulas for power | Formulas for sample size calculations

Problem setup | Assumption checking | Null hypothesis and test statistic | Calculating p-values | One-sided Z-tests

Using the t.test() function

Problem setup | Working with the differences | Paired tests versus two sample tests | Paired Z-test | Paired T-test | Paired sign-test

The Poisson distribution | Goals in the 2018 FIFA World Cup | Basic fitted distribution | Poisson regression and probabilistic forecasting | Further details and extensions | Technical details: Maximum likelihood estimation of $\lambda$ | References

Problem setup | Assumption checking | Null hypothesis and test statistic | Calculating p-values

Introduction | The model | The data | Generalized fluctuation tests | F tests | Monitoring with the generalized fluctuation test | Conclusions | Implementation details for p values

Introduction | Data | Base model | Testing: Empirical fluctuation processes | Testing: $F$ statistics | Testing: Generalized empirical fluctuation processes | Dating breaks | Monitoring | References

Introduction | The linear regression model | Estimating the covariance matrix | Applications and illustrations | Summary | R code

Introduction | Model frame | Existing R infrastructure | Covariance matrix estimators | Illustrations | Discussion

Introduction | Overview | Methods | Software | Illustrations | Simulation | Summary | Simulation results for panel data with AR(1) correlations

Differential item functioning in the Rasch model | Data preparation | Model fitting, plotting and extraction of parameter values | Interpretation | Stability assessment | Acknowledgments | References

Analysis | Summary | References

Analysis | Summary | References

Introduction | Exercises | Combining exercises: The master LaTeX file | Application and customization | Discussion | Summary

Introduction | Using the exams package | Design | Extending the exams toolbox and writing new drivers | Summary and discussion | List of Sweave exercises in exams | Evaluation policies | Cloze exercises

Example 1 | Example 2 | Example 3 | Example 4 | Example 5 | Example 6 | Example 7 | Example 8 | Example 9 | Example 10 | Example 11 | Example 12 | Example 13 | Example 14 | Example 15 | Further comments

zoo works with any ordered index class | zoo is invisible | zoo is consistent with base R | zoo is infrastructure | zoo interfaces to all other time series packages | zoo includes extensive documentation | zoo long had no bug list since all bugs are fixed (almost) immediately | zoo includes formal regression testing

1. I know that duplicate times are not allowed but my data has them. What do I do? | 2. When I try to specify a log axis to plot.zoo a warning is issued. What is wrong? | 3. How do I create right and a left vertical axes in plot.zoo? | 4. I have data frame with both numeric and factor columns. How do I convert that to a "zoo" object? | 5. Why does lag give slightly different results on a "zoo" and a "zooreg" series which are otherwise the same? | 6. How do I subtract the mean of each month from a "zoo" series? | 7. How do I create a monthly series but still keep track of the dates? | 8. How are axes added to a plot created using plot.zoo? | 9. Why is nothing plotted except axes when I plot an object with many NAs? | 10. Does zoo work with Rmetrics? | 11. What other packages use zoo? | 12. Why does ifelse not work as I expect? | 13. In a series which is regular except for a few missing times or for which we wish to align to a grid how is it filled or aligned? | What is the difference between as.Date in zoo and as.Date in the core of R? | 15. How can I speed up zoo?

Read a series from a text file | Query dates | Convert back into a plain matrix | Union and intersection | Visualization | Select (a few) observations | Handle missing data | Prices and returns | Query Yahoo! Finance | Summaries

Creation | Creation of regular series | Standard methods | Import/export | Coercion | Merging and binding | Mathematical operations | Extracting and replacing data and index | NA handling | Rolling analytics | Methods for regular series | Time classes for monthly and quarterly data

Introduction | The class "zoo" and its methods | Combining zoo with other packages | Summary and outlook | Reference card

Overview | Installation | Choosing HCL-based color palettes | Usage with base graphics | Usage with ggplot2 | Palette visualization and assessment

Introduction | Color palettes | Illustrations

Classes and methods | Coercing tree objects | Growing a simple classification tree | Predictions | Conclusion

Overview | Introduction | Recursive binary partitioning | Recursive partitioning by conditional inference | Examples | Illustrations and applications | Backward compatibility and novel functionality

Overview | Motivating example | Technical details | Summary

Overview | First examples | More elaborate examples | Building blocks | Demo files | Setup and customization | Standalone interaction forms

Introduction | Regression models | Censored regression (tobit) | Truncated regression | R implementation | Example | Summary | References

Overview | First motivation | Building blocks | Tutorial with a quiz | Tutorial with individual questions

Overview | Installation | Instrumental variables regression | Illustration: Returns to schooling

Introduction | Models and software | Application and illustrations | Summary | Technical details for hurdle models | Technical details for zero-inflated models | Methods for fitted zero-inflated and hurdle models | Replication of textbook results

Introduction | Fitting a mixed-model tree with constant fits | Detecting treatment-subgroup interactions in clustered data | Detecting subgroups with different growth trajectories | R code for generating artificial motivating dataset

Introduction | Review of 2SLS Estimation | Unusual-Data Diagnostics for 2SLS Regression | Unusual-Data Diagnostics in the ivreg Package | Unusual Data Diagnostics: An Example | Nonlinearity Diagnostics | Diagnosing Nonlinearity: An Example | Nonconstant Error Variance | Weighted 2SLS Regression | Collinearity Diagnostics | Robust Two-Stage Instrumental-Variables Regression | Concluding Remarks | References

Introduction | Motivating examples | Multiple parts | Multiple responses | Implementation | Working with classic "formula" objects | Constructing "Formula" objects | Extracting "formula" and "terms" objects | Computing model frames, matrices, and responses | Further methods | Usage in model fitting functions | Summary | References

1 Data | 2 stablelearner | 2.1 Growing a random forest in stablelearner | 2.2 Gaining insight into the forest | 3 Working with random forests fitted via other packages | References

Introduction | Circular regression models | Distribution trees for a von Mises distribution | Summary

Introduction | Globally and locally optimal decision trees | The evtree algorithm | Implementation and application in practice | Performance comparison | Choice of evtree parameters | Conclusions

Introduction | Implementation | Distributions | Poisson ("xpois") | Binomial ("xbinom") | Negative Binomial ("xnbinom") | Zero-Truncated Poisson ("xztpois") | Zero-Truncated Negative Binomial ("xztnbinom") | Hurdle Poisson ("xhpois") | Hurdle Negative Binomial ("xhnbinom")