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S3 Infrastructure for Regular and Irregular Time Series (Z's Ordered Observations)
An S3 class with methods for totally ordered indexed observations. It is particularly aimed at irregular time series of numeric vectors/matrices and factors. zoo's key design goals are independence of a particular index/date/time class and consistency with ts and base R by providing methods to extend standard generics.
Color Space Manipulation
Carries out mapping between assorted color spaces including RGB, HSV, HLS, CIEXYZ, CIELUV, HCL (polar CIELUV), CIELAB and polar CIELAB. Qualitative, sequential, and diverging color palettes based on HCL colors are provided along with an interactive palette picker (with either a Tcl/Tk or a shiny GUI).
Extended Model Formulas
Infrastructure for extended formulas with multiple parts on the
right-hand side and/or multiple responses on the left-hand side
Testing Linear Regression Models
A collection of tests, data sets, and examples for diagnostic checking in linear regression models. Furthermore, some generic tools for inference in parametric models are provided.
Robust Covariance Matrix Estimators
Model-robust standard error estimators for cross-sectional, time series, clustered, panel, and longitudinal data.
Applied Econometrics with R
Functions, data sets, examples, demos, and vignettes for the book Christian Kleiber and Achim Zeileis (2008), Applied Econometrics with R, Springer-Verlag, New York. ISBN 978-0-387-77316-2. (See the vignette "AER" for a package overview.)
Testing, Monitoring, and Dating Structural Changes
Testing, monitoring and dating structural changes in (linear) regression models. strucchange features tests/methods from the generalized fluctuation test framework as well as from the F test (Chow test) framework. This includes methods to fit, plot and test fluctuation processes (e.g., CUSUM, MOSUM, recursive/moving estimates) and F statistics, respectively. It is possible to monitor incoming data online using fluctuation processes. Finally, the breakpoints in regression models with structural changes can be estimated together with confidence intervals. Emphasis is always given to methods for visualizing the data.
Measuring Inequality, Concentration, and Poverty
Inequality, concentration, and poverty measures. Lorenz curves (empirical and theoretical).
Beta regression for modeling beta-distributed dependent variables, e.g., rates and proportions. In addition to maximum likelihood regression (for both mean and precision of a beta-distributed response), bias-corrected and bias-reduced estimation as well as finite mixture models and recursive partitioning for beta regressions are provided.
Psychometric Modeling Infrastructure
Infrastructure for psychometric modeling such as data classes (for item response data and paired comparisons), basic model fitting functions (for Bradley-Terry, Rasch, partial credit, rating scale, multinomial processing tree models), extractor functions for different types of parameters (item, person, threshold, discrimination), unified inference and visualizations, and various datasets for illustration. Intended as a common lightweight and efficient toolbox for psychometric modeling and a common building block for fitting psychometric mixture models in package "psychomix" and trees based on psychometric models in package "psychotree".