This package provides functions, data sets, examples, demos, and vignettes for the book Christian Kleiber and Achim Zeileis (2008), Applied Econometrics with R, Springer-Verlag, New York. ISBN 978-0-387-77316-2. (See the vignette "AER" for a package overview.)

This package provides a collection of lexical hash tables, dictionaries, and word lists.

The biglm package lets you create a linear model object that uses only codep^2 memory for p variables. It can be updated with more data using update. This allows linear regression on data sets larger than memory.

This package provides a simple git client for R based on libgit2 with support for SSH and HTTPS remotes. All functions in gert use basic R data types (such as vectors and data-frames) for their arguments and return values. User credentials are shared with command line git through the git-credential store and SSH keys stored on disk or ssh-agent.

This package converts between GeoJSON and Simple Feature objects.

This package provides an R interface to the Lawson-Hanson implementation of an algorithm for non-negative least squares (NNLS). It also allows the combination of non-negative and non-positive constraints.

Multivariate data analysis is the simultaneous observation of more than one characteristic. In contrast to the analysis of univariate data, in this approach not only a single variable or the relation between two variables can be investigated, but the relations between many attributes can be considered. For the statistical analysis of chemical data one has to take into account the special structure of this type of data. This package contains about 30 functions, mostly for regression, classification and model evaluation and includes some data sets used in the R help examples. It was designed as a R companion to the book "Introduction to Multivariate Statistical Analysis in Chemometrics" written by K. Varmuza and P. Filzmoser (2009).

This package contains an implementation of a function digest() for the creation of hash digests of arbitrary R objects (using the md5, sha-1, sha-256, crc32, xxhash and murmurhash algorithms) permitting easy comparison of R language objects, as well as a function hmac() to create hash-based message authentication code.

Please note that this package is not meant to be deployed for cryptographic purposes for which more comprehensive (and widely tested) libraries such as OpenSSL should be used.

This package provides an improved implementation (based on k-nearest neighbors) of the density peak clustering algorithm, originally described by Alex Rodriguez and Alessandro Laio (Science, 2014 vol. 344). It can handle large datasets (> 100,000 samples) very efficiently.

This package provides functions useful in the design and ANOVA of experiments. The content falls into the following groupings:

1. data,

2. factor manipulation functions,

3. design functions,

4. ANOVA functions,

5. matrix functions,

6. projector and canonical efficiency functions, and

7. miscellaneous functions.

There is a vignette called DesignNotes describing how to use the design functions for randomizing and assessing designs. The ANOVA functions facilitate the extraction of information when the Error function has been used in the call to aov.

This package is a collection of functions and layers to enhance ggplot2. The flagship function is ggMarginal(), which can be used to add marginal histograms/boxplots/density plots to ggplot2 scatterplots.

This package provides full screen and partial loading screens for Shiny with spinners, progress bars, and notifications.

This package provides an integrated set of functions for the analysis of multivariate normal datasets with missing values, including implementation of the EM algorithm, data augmentation, and multiple imputation.

This package provides functions for numerical analysis and linear algebra, numerical optimization, differential equations, plus some special functions. It uses Matlab function names where appropriate to simplify porting.

Makes it incredibly easy to build interactive web applications with R. Automatic "reactive" binding between inputs and outputs and extensive prebuilt widgets make it possible to build beautiful, responsive, and powerful applications with minimal effort.

This package provides an implementation of efficient approximate leave-one-out (LOO) cross-validation for Bayesian models fit using Markov chain Monte Carlo, as described in doi:10.1007/s11222-016-9696-4. The approximation uses Pareto smoothed importance sampling (PSIS), a new procedure for regularizing importance weights. As a byproduct of the calculations, we also obtain approximate standard errors for estimated predictive errors and for the comparison of predictive errors between models. The package also provides methods for using stacking and other model weighting techniques to average Bayesian predictive distributions.

This is a package to infer transmission trees from a dated phylogeny. It includes methods to simulate and analyze outbreaks. The methodology is described in Didelot et al. (2014) and Didelot et al. (2017).

The main janitor functions can: perfectly format data.frame column names; provide quick counts of variable combinations (i.e., frequency tables and crosstabs); and isolate duplicate records. Other janitor functions nicely format the tabulation results. These tabulate-and-report functions approximate popular features of SPSS and Excel. This package follows the principles of the "tidyverse" and works well with the pipe function %>%. janitor was built with beginning-to-intermediate R users in mind and is optimized for user-friendliness. Advanced R users can already do everything covered here, but with janitor they can do it faster and save their thinking for the fun stuff.

This package implements functionality for exploratory data analysis and nonparametric analysis of spatial data, mainly spatial point patterns, in the spatstat family of packages. Methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported.

The Tweedie compound Poisson distribution is a mixture of a degenerate distribution at the origin and a continuous distribution on the positive real line. It has been applied in a wide range of fields in which continuous data with exact zeros regularly arise. The cplm package provides likelihood based and Bayesian procedures for fitting common Tweedie compound Poisson linear models. In particular, models with hierarchical structures or extra zero inflation can be handled. Further, the package implements the Gini index based on an ordered version of the Lorenz curve as a robust model comparison tool involving zero-inflated and highly skewed distributions.

This is a package for creating tiny yet beautiful documents and vignettes from R Markdown. The package provides the html_pretty output format as an alternative to the html_document and html_vignette engines that convert R Markdown into HTML pages. Various themes and syntax highlight styles are supported.

This package provides a set of predicates and assertions for checking the properties of variables, such as length, names and attributes. This is mainly for use by other package developers who want to include run-time testing features in their own packages.

This package lets you use syntax inspired by the package glue to extract matched substrings in a more intuitive and compact way than by using standard regular expressions.

This package provides tools for pretty, human readable formatting of quantities.