This package provides a collection of functions to visualize spatial data and models on top of static maps from various online sources (e.g Google Maps and Stamen Maps). It includes tools common to those tasks, including functions for geolocation and routing.

This package provides an R interface to Google's BigQuery database.

This package computes model II simple linear regression using ordinary least squares (OLS), major axis (MA), standard major axis (SMA), and ranged major axis (RMA).

This is a package for visualizing functional data and identifying functional outliers with bagplots, boxplots and rainbow plots.

Ggplot2 is an implementation of the grammar of graphics in R. It combines the advantages of both base and lattice graphics: conditioning and shared axes are handled automatically, and you can still build up a plot step by step from multiple data sources. It also implements a sophisticated multidimensional conditioning system and a consistent interface to map data to aesthetic attributes.

This package provides tools to get text from images of text using Abbyy Cloud Optical Character Recognition (OCR) API. With abbyyyR, one can easily OCR images, barcodes, forms, documents with machine readable zones, e.g. passports and get the results in a variety of formats including plain text and XML. To learn more about the Abbyy OCR API, see http://ocrsdk.com/.

This package provides a collection of ggplot2 color palettes inspired by plots in scientific journals, data visualization libraries, science fiction movies, and TV shows.

This package provides an implementation of the FastICA algorithm to perform independent component analysis (ICA) and projection pursuit.

This package provides two high quality and fast PPRNGs that may be used in an OpenMP parallel environment. In addition, there is a generator for one dimensional low-discrepancy sequence.

This package provides functions and datasets for bootstrapping from the book "Bootstrap Methods and Their Application" by A.C. Davison and D.V. Hinkley (1997, CUP), originally written by Angelo Canty for S.

This package provides functions for analysing, manipulating, displaying, editing and synthesizing time waves (particularly sound). This package processes time analysis (oscillograms and envelopes), spectral content, resonance quality factor, entropy, cross correlation and autocorrelation, zero-crossing, dominant frequency, analytic signal, frequency coherence, 2D and 3D spectrograms and many other analyses.

The zlog package offers functions to transform laboratory measurements into standardised z or z(log)-values. Therefore the lower and upper reference limits are needed. If these are not known they could be estimated from a given sample.

This package provides ten distributions supplementing those built into R. Inverse Gauss, Kruskal-Wallis, Kendall's Tau, Friedman's chi squared, Spearman's rho, maximum F ratio, the Pearson product moment correlation coefficient, Johnson distributions, normal scores and generalized hypergeometric distributions. In addition two random number generators of George Marsaglia are included.

This package provides 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.

This package provides functions that read and solve linear inverse problems (food web problems, linear programming problems).

Many models contain tuning parameters (i.e. parameters that cannot be directly estimated from the data). These tools can be used to define objects for creating, simulating, or validating values for such parameters.

Bayesian density estimates for univariate continuous random samples are provided using the Bayesian inference engine paradigm. The engine options are: Hamiltonian Monte Carlo, the no U-turn sampler, semiparametric mean field variational Bayes and slice sampling. The methodology is described in Wand and Yu (2020), arXiv:2009.06182.

This light-weight package helps you track and visualize the progress of parallel versions of vectorized R functions of the mc*apply family.

This package contains the core survival analysis routines, including definition of Surv objects, Kaplan-Meier and Aalen-Johansen (multi-state) curves, Cox models, and parametric accelerated failure time models.

This package provides implementation of methods for estimation of quantitative maps from Multi-Parameter Mapping (MPM) acquisitions including adaptive smoothing methods in the framework of the ESTATICS model. The smoothing method is described in Mohammadi et al. (2017). <doi:10.20347/WIAS.PREPRINT.2432>. Usage of the package is also described in Polzehl and Tabelow (2019), Magnetic Resonance Brain Imaging, Chapter 6, Springer, Use R! Series. <doi:10.1007/978-3-030-29184-6_6>.

This package handles very large numbers in R. Real numbers are held using their natural logarithms, plus a logical flag indicating sign. The package includes a vignette that gives a step-by-step introduction to using S4 methods.

Many approaches for both reading and creating XML (and HTML) documents (including DTDs), both local and accessible via HTTP or FTP. Also offers access to an XPath "interpreter".

This package provides counterparts to R string manipulation functions that account for the effects of ANSI text formatting control sequences.

In this package Cardoso's JADE algorithm as well as his functions for joint diagonalization are ported to R. Also several other blind source separation (BSS) methods, like AMUSE and SOBI, and some criteria for performance evaluation of BSS algorithms, are given. The package is described in Miettinen, Nordhausen and Taskinen (2017) <doi:10.18637/jss.v076.i02>.