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Utilities from 'Seminar fuer Statistik' ETH Zurich
Useful utilities ['goodies'] from Seminar fuer Statistik ETH Zurich, some of which were ported from S-plus in the 1990s. For graphics, have pretty (Log-scale) axes eaxis(), an enhanced Tukey-Anscombe plot, combining histogram and boxplot, 2d-residual plots, a 'tachoPlot()', pretty arrows, etc. For robustness, have a robust F test and robust range(). For system support, notably on Linux, provides 'Sys.*()' functions with more access to system and CPU information. Finally, miscellaneous utilities such as simple efficient prime numbers, integer codes, Duplicated(), toLatex.numeric() and is.whole().
Bayesian Regression Models using 'Stan'
Fit Bayesian generalized (non-)linear multivariate multilevel models
using 'Stan' for full Bayesian inference. A wide range of distributions
and link functions are supported, allowing users to fit -- among others --
linear, robust linear, count data, survival, response times, ordinal,
zero-inflated, hurdle, and even self-defined mixture models all in a
multilevel context. Further modeling options include both theory-driven and
data-driven non-linear terms, auto-correlation structures, censoring and
truncation, meta-analytic standard errors, and quite a few more.
In addition, all parameters of the response distribution can be predicted
in order to perform distributional regression. Prior specifications are
flexible and explicitly encourage users to apply prior distributions that
actually reflect their prior knowledge. Models can easily be evaluated and
compared using several methods assessing posterior or prior predictions.
References: Bürkner (2017)
Relative Distribution Methods
Tools for the comparison of distributions. This includes nonparametric estimation of the relative distribution PDF and CDF and numerical summaries as described in "Relative Distribution Methods in the Social Sciences" by Mark S. Handcock and Martina Morris, Springer-Verlag, 1999, Springer-Verlag, ISBN 0387987789.
Flexible Mixture Modeling
A general framework for finite mixtures of regression models using the EM algorithm is implemented. The E-step and all data handling are provided, while the M-step can be supplied by the user to easily define new models. Existing drivers implement mixtures of standard linear models, generalized linear models and model-based clustering.
SIMEX- And MCSIMEX-Algorithm for Measurement Error Models
Implementation of the SIMEX-Algorithm by Cook & Stefanski (1994)
Forecasting Mortality, Fertility, Migration and Population Data
Functions for demographic analysis including lifetable calculations; Lee-Carter modelling; functional data analysis of mortality rates, fertility rates, net migration numbers; and stochastic population forecasting.
Regression with Functional Data
Methods for regression for functional data, including function-on-scalar, scalar-on-function, and function-on-function regression. Some of the functions are applicable to image data.
Tidying Methods for Mixed Models
Convert fitted objects from various R mixed-model packages into tidy data frames along the lines of the 'broom' package. The package provides three S3 generics for each model: tidy(), which summarizes a model's statistical findings such as coefficients of a regression; augment(), which adds columns to the original data such as predictions, residuals and cluster assignments; and glance(), which provides a one-row summary of model-level statistics.
R Commander
A platform-independent basic-statistics GUI (graphical user interface) for R, based on the tcltk package.
Simple Interactive Controls for R using the 'tcltk' Package
A set of functions to build simple GUI controls for R functions. These are built on the 'tcltk' package. Uses could include changing a parameter on a graph by animating it with a slider or a "doublebutton", up to more sophisticated control panels. Some functions for specific graphical tasks, referred to as 'cartoons', are provided.