This package provides a set of tools to facilitate package development and make R a more user-friendly place. It is intended mostly for developers (or anyone who writes/shares functions). It provides a simple, powerful and flexible way to check the arguments passed to functions. The developer can easily describe the type of argument needed. If the user provides a wrong argument, then an informative error message is prompted with the requested type and the problem clearly stated--saving the user a lot of time in debugging.

This package provides functions used for local regression, likelihood and density estimation.

Extracts sentiment and sentiment-derived plot arcs from text using a variety of sentiment dictionaries conveniently packaged for consumption by R users. Implemented dictionaries include syuzhet (default) developed in the Nebraska Literary Lab, afinn developed by Finn Arup Nielsen, bing developed by Minqing Hu and Bing Liu, and nrc developed by Mohammad, Saif M. and Turney, Peter D. Applicable references are available in README.md and in the documentation for the get_sentiment function. The package also provides a hack for implementing Stanford's coreNLP sentiment parser. The package provides several methods for plot arc normalization.

This package provides functions to convert a page of plots drawn with the graphics package into identical output drawn with the grid package. The result looks like the original graphics-based plot, but consists of grid grobs and viewports that can then be manipulated with grid functions (e.g., edit grobs and revisit viewports).

Simultaneous tests and confidence intervals for general linear hypotheses in parametric models, including linear, generalized linear, linear mixed effects, and survival models. The package includes demos reproducing analyzes presented in the book "Multiple Comparisons Using R" (Bretz, Hothorn, Westfall, 2010, CRC Press).

This package provides non-parametric (and semi-parametric) kernel methods that seamlessly handle a mix of continuous, unordered, and ordered factor data types.

This package allows for data objects in R to be rendered as HTML tables using the JavaScript library DataTables (typically via R Markdown or Shiny). The DataTables library has been included in this R package.

This package provides useful tools for structural equation modeling.

This is a package for maximum likelihood estimation of censored regression (Tobit) models with cross-sectional and panel data.

This package implements an S3 class for storing and formatting time-of-day values, based on the difftime class.

The main function archetypes implements a framework for archetypal analysis supporting arbitrary problem solving mechanisms for the different conceptual parts of the algorithm.

Keep track of dates in terms of fractional calendar months per Damien Laker "Time Calculations for Annualizing Returns: the Need for Standardization", The Journal of Performance Measurement, 2008. Model dates as of close of business. Perform date arithmetic in units of "months" and "years". Allow "infinite" dates to model "ultimate" time.

This package provides tools to create pretty tables for HTML documents and other formats. Functions are provided to let users create tables, modify and format their content. It extends the officer package and can be used within R markdown documents when rendering to HTML and to Word documents.

UpSet plots are an improvement over Venn Diagram for set overlap visualizations. Striving to bring the best of the UpSetR and ggplot2, this package offers a way to create complex overlap visualisations, using simple and familiar tools.

This package performs angle-based outlier detection on a given data frame. It offers three methods to process data:

1. full but slow implementation using all the data that has cubic complexity;

2. a fully randomized method;

3. a method using k-nearest neighbours.

These algorithms are well suited for high dimensional data outlier detection.

This package provides basic wavelet routines for time series (1D), image (2D) and array (3D) analysis. The code provided here is based on wavelet methodology developed in Percival and Walden (2000); Gencay, Selcuk and Whitcher (2001); the dual-tree complex wavelet transform (DTCWT) from Kingsbury (1999, 2001) as implemented by Selesnick; and Hilbert wavelet pairs (Selesnick 2001, 2002).

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 tools for accessing the Botanical Information and Ecology Network (BIEN) database. The BIEN database contains cleaned and standardized botanical data including occurrence, trait, plot and taxonomic data. This package provides functions that query the BIEN database by constructing and executing optimized SQL queries.

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

This is a package for estimation of one-dimensional probability distributions including kernel density estimation, weighted empirical cumulative distribution functions, Kaplan-Meier and reduced-sample estimators for right-censored data, heat kernels, kernel properties, quantiles and integration.

This package provides a system for reporting messages, which offers certain useful features over the standard R system, such as the incorporation of output consolidation, message filtering, assertions, expression substitution, automatic generation of stack traces for debugging, and conditional reporting based on the current "output level".

Easily and flexibly insert Font Awesome icons into R Markdown documents and Shiny apps. These icons can be inserted into HTML content through inline SVG tags or i tags. There is also a utility function for exporting Font Awesome icons as PNG images for those situations where raster graphics are needed.

This package implements data manipulation methods such as cast, aggregate, and merge/join for Matrix and Matrix-like objects.

This R package contains examples from the book Regression for Categorical Data, Tutz 2012, Cambridge University Press. The names of the examples refer to the chapter and the data set that is used.