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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.
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.
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)
Extended Model Formulas
Infrastructure for extended formulas with multiple parts on the
right-hand side and/or multiple responses on the left-hand side
(see
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 < https://colorspace.R-Forge.R-project.org/>
and in the accompanying scientific paper: Zeileis et al. (2020, Journal of Statistical
Software,
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.
Trellis Graphics for R
A powerful and elegant high-level data visualization system inspired by Trellis graphics, with an emphasis on multivariate data. Lattice is sufficient for typical graphics needs, and is also flexible enough to handle most nonstandard requirements. See ?Lattice for an introduction.
Beta Regression
Beta regression for modeling beta-distributed dependent variables on the open unit interval (0, 1),
e.g., rates and proportions, see Cribari-Neto and Zeileis (2010)
Exploratory Trend Analysis and Visualization for Time-Series and Grouped Data
Provides a set of exploratory data analysis (EDA) tools for
visualizing trends, diagnosing data types for beginner-friendly workflows,
and automatically routing to suitable statistical tests or trend exploration
models. Includes unified plotting functions for trend lines, grouped boxplots,
and comparative scatterplots; automated statistical testing (e.g., t-test,
Wilcoxon, ANOVA, Kruskal-Wallis, Tukey, Dunn) with optional effect size
calculation; and model-based trend analysis using generalized additive
models (GAM) for count data, generalized linear models (GLM) for continuous
data, and zero-inflated models (ZIP/ZINB) for count data with potential
zero-inflation.
Also supports time-window continuity checks, cross-year
handling in compare_monthly_cases(), and ARIMA-ready preparation with
stationarity diagnostics, ensuring consistent parameter styles for
reproducible research and user-friendly workflows.Methods are
based on R Core Team (2024) < https://www.R-project.org/>,
Wood, S.N.(2017, ISBN:978-1498728331),
Hyndman RJ, Khandakar Y (2008)
Measuring Inequality, Concentration, and Poverty
Inequality, concentration, and poverty measures. Lorenz curves (empirical and theoretical).