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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.)
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.
Extended Model Formulas
Infrastructure for extended formulas with multiple parts on the
right-hand side and/or multiple responses on the left-hand side
Robust Covariance Matrix Estimators
Object-oriented software for model-robust covariance matrix estimators. Starting out from the basic
robust Eicker-Huber-White sandwich covariance methods include: heteroscedasticity-consistent (HC)
covariances for cross-section data; heteroscedasticity- and autocorrelation-consistent (HAC)
covariances for time series data (such as Andrews' kernel HAC, Newey-West, and WEAVE estimators);
clustered covariances (one-way and multi-way); panel and panel-corrected covariances;
outer-product-of-gradients covariances; and (clustered) bootstrap covariances. All methods are
applicable to (generalized) linear model objects fitted by lm() and glm() but can also be adapted
to other classes through S3 methods. Details can be found in Zeileis et al. (2020)
A Toolbox for Manipulating and Assessing Colors and Palettes
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 corresponding ggplot2 color scales.
Color palette choice is aided by an interactive app (with either a Tcl/Tk
or a shiny graphical user interface) and shiny apps with an HCL color picker and a
color vision deficiency emulator. Plotting functions for displaying
and assessing palettes include color swatches, visualizations of the
HCL space, and trajectories in HCL and/or RGB spectrum. Color manipulation
functions include: desaturation, lightening/darkening, mixing, and
simulation of color vision deficiencies (deutanomaly, protanomaly, tritanomaly).
Details can be found on the project web page at < http://colorspace.R-Forge.R-project.org/>
and in the accompanying scientific paper: Zeileis et al. (2020, Journal of Statistical
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.
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.
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.
Measuring Inequality, Concentration, and Poverty
Inequality, concentration, and poverty measures. Lorenz curves (empirical and theoretical).
A Laboratory for Recursive Partytioning
A computational toolbox for recursive partitioning.
The core of the package is ctree(), an implementation of
conditional inference trees which embed tree-structured
regression models into a well defined theory of conditional
inference procedures. This non-parametric class of regression
trees is applicable to all kinds of regression problems, including
nominal, ordinal, numeric, censored as well as multivariate response
variables and arbitrary measurement scales of the covariates.
Based on conditional inference trees, cforest() provides an
implementation of Breiman's random forests. The function mob()
implements an algorithm for recursive partitioning based on
parametric models (e.g. linear models, GLMs or survival
regression) employing parameter instability tests for split
selection. Extensible functionality for visualizing tree-structured
regression models is available. The methods are described in
Hothorn et al. (2006)