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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.
Interactive Grammar of Graphics
An implementation of an interactive grammar of graphics, taking the best parts of 'ggplot2', combining them with the reactive framework of 'shiny' and drawing web graphics using 'vega'.
Gradient-Based Coenospace Vegetation Simulator
Simulates the composition of samples of vegetation according to gradient-based vegetation theory. Features a flexible algorithm incorporating competition and complex multi-gradient interaction.
Time-Varying Effect Models
Fits time-varying effect models (TVEM). These are a kind of application of varying-coefficient models in the context of longitudinal data, allowing the strength of linear, logistic, or Poisson regression relationships to change over time. These models are described further in Tan, Shiyko, Li, Li & Dierker (2012)
Bayesian Additive Models for Location, Scale, and Shape (and Beyond)
Infrastructure for estimating probabilistic distributional regression models in a Bayesian framework.
The distribution parameters may capture location, scale, shape, etc. and every parameter may depend
on complex additive terms (fixed, random, smooth, spatial, etc.) similar to a generalized additive model.
The conceptual and computational framework is introduced in Umlauf, Klein, Zeileis (2019)
Generalised Additive Extreme Value Models
Methods for fitting various extreme value distributions with parameters of
generalised additive model (GAM) form are provided. For details of distributions
see Coles, S.G. (2001)
Easy Analysis and Visualization of Factorial Experiments
Facilitates easy analysis of factorial experiments, including purely within-Ss designs (a.k.a. "repeated measures"), purely between-Ss designs, and mixed within-and-between-Ss designs. The functions in this package aim to provide simple, intuitive and consistent specification of data analysis and visualization. Visualization functions also include design visualization for pre-analysis data auditing, and correlation matrix visualization. Finally, this package includes functions for non-parametric analysis, including permutation tests and bootstrap resampling. The bootstrap function obtains predictions either by cell means or by more advanced/powerful mixed effects models, yielding predictions and confidence intervals that may be easily visualized at any level of the experiment's design.
Multivariate Functional Principal Component Analysis for Data Observed on Different Dimensional Domains
Calculate a multivariate functional principal component analysis
for data observed on different dimensional domains. The estimation algorithm
relies on univariate basis expansions for each element of the multivariate
functional data (Happ & Greven, 2018)
Mark-Recapture Distance Sampling
Animal abundance estimation via conventional, multiple covariate and mark-recapture distance sampling (CDS/MCDS/MRDS). Detection function fitting is performed via maximum likelihood. Also included are diagnostics and plotting for fitted detection functions. Abundance estimation is via a Horvitz-Thompson-like estimator.
Variable Selection for Model-Based Clustering of Mixed-Type Data Set with Missing Values
Full model selection (detection of the relevant features and estimation of the number of clusters) for model-based clustering (see reference here