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Transformation Trees and Forests
Recursive partytioning of transformation models with
corresponding random forest for conditional transformation models
as described in 'Transformation Forests' (Hothorn and Zeileis, 2021,
Breaks for Additive Season and Trend
Decomposition of time series into
trend, seasonal, and remainder components with methods for detecting and
characterizing abrupt changes within the trend and seasonal components. 'BFAST'
can be used to analyze different types of satellite image time series and can
be applied to other disciplines dealing with seasonal or non-seasonal time
series, such as hydrology, climatology, and econometrics. The algorithm can be
extended to label detected changes with information on the parameters of the
fitted piecewise linear models. 'BFAST' monitoring functionality is described
in Verbesselt et al. (2010)
Conditional Visualization for Statistical Models
Exploring fitted models by interactively taking 2-D and 3-D sections in data space.
Tools for Descriptive Statistics
A collection of miscellaneous basic statistic functions and convenience wrappers for efficiently describing data. The author's intention was to create a toolbox, which facilitates the (notoriously time consuming) first descriptive tasks in data analysis, consisting of calculating descriptive statistics, drawing graphical summaries and reporting the results. The package contains furthermore functions to produce documents using MS Word (or PowerPoint) and functions to import data from Excel. Many of the included functions can be found scattered in other packages and other sources written partly by Titans of R. The reason for collecting them here, was primarily to have them consolidated in ONE instead of dozens of packages (which themselves might depend on other packages which are not needed at all), and to provide a common and consistent interface as far as function and arguments naming, NA handling, recycling rules etc. are concerned. Google style guides were used as naming rules (in absence of convincing alternatives). The 'BigCamelCase' style was consequently applied to functions borrowed from contributed R packages as well.
Colors for all
Color palettes for all people, including those with color vision deficiency. Popular color palette series have been organized by type and have been scored on several properties such as color-blind-friendliness and fairness (i.e. do colors stand out equally?). Own palettes can also be loaded and analysed. Besides the common palette types (categorical, sequential, and diverging) it also includes bivariate color palettes. Furthermore, a color for missing values is assigned to each palette.
Testing, Monitoring, and Dating Structural Changes: C++ Version
A fast implementation with additional experimental features for
testing, monitoring and dating structural changes in (linear)
regression models. 'strucchangeRcpp' 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. cumulative/moving
sum, recursive/moving estimates) and F statistics, respectively.
These methods are described in Zeileis et al. (2002)
Distribution of the 'BayesX' C++ Sources
'BayesX' performs Bayesian inference in structured additive regression (STAR) models. The R package BayesXsrc provides the 'BayesX' command line tool for easy installation. A convenient R interface is provided in package R2BayesX.
TeX-to-HTML/MathML Translators TtH/TtM
C source code and R wrappers for the tth/ttm TeX-to-HTML/MathML translators.
Fast Wild Cluster Bootstrap Inference for Linear Models
Implementation of fast algorithms for wild cluster bootstrap
inference developed in 'Roodman et al' (2019, 'STATA' Journal,
Classification and Regression with Structured and Mixed-Type Data
Implementation of Energy Trees, a statistical model to perform
classification and regression with structured and mixed-type data. The
model has a similar structure to Conditional Trees, but brings in Energy
Statistics to test independence between variables that are possibly
structured and of different nature. Currently, the package covers functions
and graphs as structured covariates. It builds upon 'partykit' to
provide functionalities for fitting, printing, plotting, and predicting with
Energy Trees. Energy Trees are described in Giubilei et al. (2022)