The smurf package contains the implementation of the Sparse Multi-type Regularized Feature (SMuRF) modeling algorithm to fit generalized linear models (GLMs) with multiple types of predictors via regularized maximum likelihood. Next to the fitting procedure, following functionality is available:

• Selection of the regularization tuning parameter lambda using three different approaches: in-sample, out-of-sample or using cross-validation.

• S3 methods to handle the fitted object including visualization of the coefficients and a model summary.

This is a package providing tools for weighted k-Nearest neighbors for classification, regression and clustering.

This package provides functions to manipulate binary fingerprints of arbitrary length. A fingerprint is represented by an object of S4 class fingerprint. The bitwise logical functions in R are overridden so that they can be used directly with fingerprint objects. A number of distance metrics are also available. Fingerprints can be converted to Euclidean vectors (i.e., points on the unit hypersphere) and can also be folded. Arbitrary fingerprint formats can be handled via line handlers. Currently handlers are provided for CDK, MOE and BCI fingerprint data.

This package provides building blocks for allowing HTML widgets to communicate with each other, with Shiny or without (i.e., static .html files). It currently supports linked brushing and filtering.

This package provides 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.

This package provides tools for exploratory data analysis and data visualization of biological sequence (DNA and protein) data. It also includes utilities for sequence data management under the ACNUC system.

This package provides methods operating on rows and columns of matrices, e.g. rowMedians(), rowRanks(), and rowSds(). There are also some vector-based methods, e.g. binMeans(), madDiff() and weightedMedians(). All methods have been optimized for speed and memory usage.

The package includes the necessary functions to construct a self-organizing map of data, to evaluate the statistical significance of the observed data patterns, and to visualize the results.

This package implements the libyaml YAML 1.1 parser and emitter (http://pyyaml.org/wiki/LibYAML) for R.

This package provides routines for the polynomial spline fitting routines hazard regression, hazard estimation with flexible tails, logspline, lspec, polyclass, and polymars.

This package extends the grammar of graphics as implemented by ggplot2 to include the description of animation. It does this by providing a range of new grammar classes that can be added to the plot object in order to customise how it should change with time.

This package provides functions for importing and handling text files and formatted text files with additional meta-data, such including .csv, .tab, .json, .xml, .html, .pdf, .doc, .docx, .rtf, .xls, .xlsx, and others.

This package provides a tbl_df class that offers better checking and printing capabilities than traditional data frames.

This package provides functionality for generating (randomized) quasi-random numbers in high dimensions.

This is an R package for dimension reduction based on finite Gaussian mixture modeling of inverse regression.

This package provides a toolkit with infrastructure for representing, summarizing, and visualizing tree-structured regression and classification models.

This package provides an implementation of the Uniform Manifold Approximation and Projection dimensionality reduction by McInnes et al. (2018). It also provides means to transform new data and to carry out supervised dimensionality reduction. An implementation of the related LargeVis method of Tang et al. (2016) is also provided.

This package provides a parallel backend for the %dopar% function using the snow package.

This package provides an interface to the C code for Latent Dirichlet Allocation (LDA) models and Correlated Topics Models (CTM) by David M. Blei and co-authors and the C++ code for fitting LDA models using Gibbs sampling by Xuan-Hieu Phan and co-authors.

This package performs score test using saddlepoint approximation to estimate the null distribution. It also prepares summary statistics for meta-analysis and performs meta-analysis to combine multiple association results.

This package provides functions for reading, writing, plotting, analysing, and manipulating allelic and haplotypic data, including from VCF files, and for the analysis of population nucleotide sequences and micro-satellites including coalescent analyses, linkage disequilibrium, population structure (Fst, Amova) and equilibrium (HWE), haplotype networks, minimum spanning tree and network, and median-joining networks.

This package implements nested cross-validation applied to the glmnet and caret packages. With glmnet this includes cross-validation of elastic net alpha parameter. A number of feature selection filter functions (t-test, Wilcoxon test, ANOVA, Pearson/Spearman correlation, random forest, ReliefF) for feature selection are provided and can be embedded within the outer loop of the nested CV. Nested CV can be also be performed with the caret package giving access to the large number of prediction methods available in caret.

This package provides an all relevant feature selection wrapper algorithm. It finds relevant features by comparing original attributes' importance with importance achievable at random, estimated using their permuted copies (shadows).

This package offers a set of functions for extending dendrogram objects in R, letting you visualize and compare trees of hierarchical clusterings. You can adjust a tree's graphical parameters (the color, size, type, etc of its branches, nodes and labels) and visually and statistically compare different dendrograms to one another.