This package provides auxiliary functions and data sets for "Ecological Models and Data", a book presenting maximum likelihood estimation and related topics for ecologists (ISBN 978-0-691-12522-0).

Given a protein multiple sequence alignment, it is a daunting task to assess the effects of substitutions along sequence length. The aaSEA package is intended to help researchers to rapidly analyze property changes caused by single, multiple and correlated amino acid substitutions in proteins.

The jsonlite package provides a fast JSON parser and generator optimized for statistical data and the web. It offers flexible, robust, high performance tools for working with JSON in R and is particularly powerful for building pipelines and interacting with a web API. In addition to converting JSON data from/to R objects, jsonlite contains functions to stream, validate, and prettify JSON data. The unit tests included with the package verify that all edge cases are encoded and decoded consistently for use with dynamic data in systems and applications.

This package provides methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling.

This is a package that allows conversion to and from data in JavaScript Object Notation (JSON) format. This allows R objects to be inserted into Javascript/ECMAScript/ActionScript code and allows R programmers to read and convert JSON content to R objects. This is an alternative to the rjson package.

This R package provides a single procedure guix.install(), which allows users to install R packages via Guix right from within their running R session. If the requested R package does not exist in Guix at this time, the package and all its missing dependencies will be imported recursively and the generated package definitions will be written to ~/.Rguix/packages.scm. This record of imported packages can be used later to reproduce the environment, and to add the packages in question to a proper Guix channel (or Guix itself). guix.install() not only supports installing packages from CRAN, but also from Bioconductor or even arbitrary git or mercurial repositories, replacing the need for installation via devtools.

This package provides an interface to Amazon Web Services storage services, including Simple Storage Service (S3).

This package provides an R interface to all Enrichr databases, a web-based tool for analyzing gene sets and returns any enrichment of common annotated biological functions.

This package provides a set of tools for post processing the outcomes of species distribution modeling exercises. It includes novel methods for comparing models and tracking changes in distributions through time. It further includes methods for visualizing outcomes, selecting thresholds, calculating measures of accuracy and landscape fragmentation statistics, etc.

This package provides functions for fitting general linear structural equation models (with observed and latent variables) using the RAM approach, and for fitting structural equations in observed-variable models by two-stage least squares.

This package analyzes gene expression (time series) data with focus on the inference of gene networks. In particular, GeneNet implements the methods of Schaefer and Strimmer (2005a,b,c) and Opgen-Rhein and Strimmer (2006, 2007) for learning large-scale gene association networks (including assignment of putative directions).

This package lets you standardize country names, convert them into one of 40 different coding schemes, convert between coding schemes, and assign region descriptors.

This is a package for parallel computing with a network of local and remote workers. It enables fast exchange of results between the workers through a Redis database. Key features include task queues, local caching, and sophisticated error handling.

This package provides a method to test genetic linkage with covariates by regression methods with response IBD sharing for relative pairs. Account for correlations of IBD statistics and covariates for relative pairs within the same pedigree.

This package provides tools for the estimation of indicators on social exclusion and poverty, as well as an implementation of Pareto tail modeling for empirical income distributions.

This package provides functions to perform k-prototypes partitioning clustering for mixed variable-type data according to Z.Huang (1998): Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Variables, Data Mining and Knowledge Discovery 2, 283-304.

This package provides an alternative to R's built-in functionality for handling regular expressions, based on the Onigmo library. It offers first-class compiled regex objects, partial matching and function-based substitutions, amongst other features.

The labeling package provides a range of axis labeling algorithms.

The first day of any MMWR week is Sunday. MMWR week numbering is sequential beginning with 1 and incrementing with each week to a maximum of 52 or 53. MMWR week #1 of an MMWR year is the first week of the year that has at least four days in the calendar year. This package provides functionality to convert dates to MMWR day, week, and year and the reverse.

This package provides helpers for reordering factor levels (including moving specified levels to front, ordering by first appearance, reversing, and randomly shuffling), and tools for modifying factor levels (including collapsing rare levels into other, "anonymizing", and manually "recoding").

This package provides tools to compute marginal effects from statistical models and return the result as tidy data frames. These data frames are ready to use with the ggplot2 package. Marginal effects can be calculated for many different models. Interaction terms, splines and polynomial terms are also supported. The two main functions are ggpredict() and ggeffect(). There is a generic plot() method to plot the results using ggplot2.

This package contains a simple SMTP client which provides a portable solution for sending email, including attachments, from within R.

Strex is a collection of string manipulation functions not provided by the stringi or stringr packages. The foremost of these is the extraction of numbers from strings. There are many other handy functionalities in strex.

This package provides tools to parse simple .ini configuration files to an structured list. Users can manipulate this resulting list with lapply() functions. This same structured list can be used to write back to file after modifications.