Artifacts using Cluster (146)

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Contains many functions useful for data analysis, high-level graphics, utility operations, functions for computing sample size and power, importing and annotating datasets, imputing missing values, advanced table making, variable clustering, character string manipulation, conversion of R objects to LaTeX and html code, and recoding variables.

Functions for latent class analysis, short time Fourier transform, fuzzy clustering, support vector machines, shortest path computation, bagged clustering, naive Bayes classifier, ...

Ordination methods, diversity analysis and other functions for community and vegetation ecologists.

A package that implements some simple graph handling capabilities.

Useful utilities ['goodies'] from Seminar fuer Statistik ETH Zurich, some of which were ported from S-plus in the 1990's. For graphics, have pretty (Log-scale) axes, 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 ...

CLUster Ensembles.

Exploratory data analysis methods to summarize, visualize and describe datasets. The main principal component methods are available, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, Multiple Factor Analysis when variables are structured in groups, etc. and hierarchical cluster analysis. F. Husson, S. Le and J. Pages (2017) ...

This is a set of tools for dendrograms and tree plots using 'ggplot2'. The 'ggplot2' philosophy is to clearly separate data from the presentation. Unfortunately the plot method for dendrograms plots directly to a plot device without exposing the data. The 'ggdendro' package resolves this by making available functions that extract the dendrogram plot data. The package provides implementations for tree, rpart, as well as diana and agnes cluster diagrams.

Interface to a large number of classification and regression techniques, including machine-readable parameter descriptions. There is also an experimental extension for survival analysis, clustering and general, example-specific cost-sensitive learning. Generic resampling, including cross-validation, bootstrapping and subsampling. Hyperparameter tuning with modern optimization techniques, for single- and multi-objective problems. Filter and wrapper methods for feature selection. Extension of basic learners ...

Provides the class unions that must be preloaded in order for the basic tools in the OOMPA (Object-Oriented Microarray and Proteomics Analysis) project to be defined and loaded. It also includes vectorized operations for row-by-row means, variances, and t-tests. Finally, it provides new color schemes. Details on the packages in the OOMPA project can be found at <http://oompa.r-forge.r-project.org/>.