This package provides an extensible framework for automatically placing direct labels onto multicolor plots. Label positions are described using positioning methods that can be re-used across several different plots. There are heuristics for examining trellis and ggplot objects and inferring an appropriate positioning method.

Read in activity measurements from standard file formats used by circadian rhythm researchers, currently only ClockLab format, and process and plot the data. The central type of plot is the actogram, as first described in "Activity and distribution of certain wild mice in relation to biotic communities" by MS Johnson (1926) doi:10.2307/1373575.

This package provides classes and methods for spatial data; the classes document where the spatial location information resides, for 2D or 3D data. Utility functions are provided, e.g. for plotting data as maps, spatial selection, as well as methods for retrieving coordinates, for subsetting, print, summary, etc.

This package provides routines for the statistical analysis of landmark shapes, including Procrustes analysis, graphical displays, principal components analysis, permutation and bootstrap tests, thin-plate spline transformation grids and comparing covariance matrices. See Dryden, I.L. and Mardia, K.V. (2016). Statistical shape analysis, with Applications in R (2nd Edition), John Wiley and Sons.

This package provides a range of tools for social network analysis, including node and graph-level indices, structural distance and covariance methods, structural equivalence detection, network regression, random graph generation, and 2D/3D network visualization.

This package provides helper functions with a consistent interface to coerce and verify the types and shapes of values for input checking.

This package estimates previously compiled regression models using the rstan package, which provides the R interface to the Stan C++ library for Bayesian estimation. Users specify models via the customary R syntax with a formula and data.frame plus some additional arguments for priors.

svglite is a graphics device that produces clean SVG (Scalable Vector Graphics) output, suitable for use on the web, or hand editing. Compared to the built-in svg(), svglite is considerably faster, produces smaller files, and leaves text as is.

This package provides regression models for grouped and coarse data, under the coarsened at random assumption.

Similarly to the FNN package, this package allows calculation of the k nearest neighbors (kNN) of a data matrix. The implementation is based on cover trees introduced by Alina Beygelzimer, Sham Kakade, and John Langford (2006) doi:10.1145/1143844.1143857.

This package contains all the datasets for the spatstat package.

Visualise complex relations in texts. This is done by providing functionalities for displaying text co-occurrence networks, text correlation networks, dependency relationships as well as text clustering. Feel free to join the effort of providing interesting text visualisations.

This package provides data structures that are stored on disk but behave (almost) as if they were in RAM by transparently mapping only a section in main memory.

This package provides a derivative-free optimization by quadratic approximation based on an interface to Fortran implementations by M. J. D. Powell.

The aim of the ggplot2 package is to aid in visual data investigations. This focus has led to a lack of facilities for composing specialized plots. This package aims to be a collection of mainly new statistics and geometries that fills this gap.

Multiple imputation using Fully Conditional Specification (FCS) implemented by the MICE algorithm as described in http://doi.org/10.18637/jss.v045.i03. Each variable has its own imputation model. Built-in imputation models are provided for continuous data (predictive mean matching, normal), binary data (logistic regression), unordered categorical data (polytomous logistic regression) and ordered categorical data (proportional odds). MICE can also impute continuous two-level data (normal model, pan, second-level variables). Passive imputation can be used to maintain consistency between variables. Various diagnostic plots are available to inspect the quality of the imputations.

This package provides a framework to create Bootstrap 3 HTML reports from knitr Rmarkdown.

This package provides R bindings to the Sundown Markdown rendering library (https://github.com/vmg/sundown). Markdown is a plain-text formatting syntax that can be converted to XHTML or other formats.

This lightweight package that adds progress bar to vectorized R functions apply. The implementation can easily be added to functions where showing the progress is useful e.g. bootstrap.

This package simplifies custom CSS styling of both shiny and rmarkdown via Bootstrap Sass. It supports both Bootstrap 3 and 4 as well as their various Bootswatch themes. An interactive widget is also provided for previewing themes in real time.

This package includes functions to compute the area under the curve of selected measures: the area under the sensitivity curve (AUSEC), the area under the specificity curve (AUSPC), the area under the accuracy curve (AUACC), and the area under the receiver operating characteristic curve (AUROC). The curves can also be visualized. Support for partial areas is provided.

This is package for regression modeling using rules with added instance-based corrections.

This package supports twin models that are able to estimate the dynamic behaviour of the variance components in the classical twin models with respect to age using B-splines and P-splines.

This package provides tools to create dynamic, submission-ready manuscripts, which conform to American Psychological Association manuscript guidelines. It provides R Markdown document formats for manuscripts (PDF and Word) and revision letters (PDF). Helper functions facilitate reporting statistical analyses or create publication-ready tables and plots.