This package parses HTTP request data in application/json, multipart/form-data, or application/x-www-form-urlencoded format. It includes an example of hosting and parsing HTML form data in R using either httpuv or Rhttpd.
This package includes functions for processing GeoJson objects relying on RFC 7946. The geojson encoding is based on json11, a tiny JSON library for C++11. Furthermore, the source code is exported in R through the Rcpp and RcppArmadillo packages.
This library implements immutable ropes for GNU Guile. A rope is a data structure that represents text strings. It is useful for text editing, because text can be inserted at an arbitrary point without requiring the moving of a lot of data.
Python-RSA is a pure-Python RSA implementation. It supports encryption and decryption, signing and verifying signatures, and key generation according to PKCS#1 version 1.5. It can be used as a Python library as well as on the command line.
Python-RSA is a pure-Python RSA implementation. It supports encryption and decryption, signing and verifying signatures, and key generation according to PKCS#1 version 1.5. It can be used as a Python library as well as on the command line.
Debugging functionality for Ruby. This is completely rewritten debug.rb which was contained by the ancient Ruby versions. It is included with Ruby itself, but this package is made available so that the latest version can be made available independently from Ruby.
Recoll finds documents based on their contents as well as their file names. It can search most document formats, but you may need external applications for text extraction. It can reach any storage place: files, archive members, email attachments, transparently handling decompression.
Provide functions for performing abundance and compositional based binning on metagenomic samples, directly from FASTA or FASTQ files. Functions are implemented in Java and called via rJava. Parallel implementation that operates directly on input FASTA/FASTQ files for fast execution.
This package implements several new association indices that can control for various types of errors. Also includes existing association indices and functions for simulating the effects of different rates of error on estimates of association strength between individuals using each method.
The archdata package provides several types of data that are typically used in archaeological research. It provides all of the data sets used in "Quantitative Methods in Archaeology Using R" by David L Carlson, one of the Cambridge Manuals in Archaeology.
This package provides tools for geometric morphometric analysis. The package includes tools of virtual anthropology to align two not articulated parts belonging to the same specimen, to build virtual cavities as endocast (Profico et al, 2021 <doi:10.1002/ajpa.24340>).
Interface with and extract data from the United Nations Comtrade API <https://comtradeplus.un.org/>. Comtrade provides country level shipping data for a variety of commodities, these functions allow for easy API query and data returned as a tidy data frame.
This package provides functions for working with code lists and vectors with codes. These are an alternative for factor that keep track of both the codes and labels. Methods allow for transforming between codes and labels. Also supports hierarchical code lists.
Collection of convenience functions to make working with administrative records easier and more consistent. Includes functions to clean strings, and identify cut points. Also includes three example data sets of administrative education records for learning how to process records with errors.
This package provides functionality for testing familial hypotheses. Supports testing centers belonging to the Huber family. Testing is carried out using the Bayesian bootstrap. One- and two-sample tests are supported, as are directional tests. Methods for visualizing output are provided.
Fit a geographically weighted logistic elastic net regression. Detailed explanations can be found in Yoneoka et al. (2016): New algorithm for constructing area-based index with geographical heterogeneities and variable selection: An application to gastric cancer screening <doi:10.1038/srep26582>.
Perform common calculations based on published stable isotope theory, such as calculating carbon isotope discrimination and intrinsic water use efficiency from wood or leaf carbon isotope composition. See Mathias and Hudiburg (2022) in Global Change Biology <doi:10.1111/gcb.16407>.
This package provides a monthly summary of Iowa liquor (class E) sales from January 2015 to October 2020. See the package website for more information, documentation and examples. Data source: Iowa Data portal <https://data.iowa.gov/resource/m3tr-qhgy.csv>.
An efficient and incremental approach for calculating the differences in orbit counts when performing single edge modifications in a network. Calculating the differences in orbit counts is much more efficient than recalculating all orbit counts from scratch for each time point.
Runs resampling-based tests jointly, e.g., sign-flip score tests from Hemerik et al., (2020) <doi:10.1111/rssb.12369>, to allow for multivariate testing, i.e., weak and strong control of the Familywise Error Rate or True Discovery Proportion.
This package implements state-of-the-art block bootstrap methods for extreme value statistics based on block maxima. Includes disjoint blocks, sliding blocks, relying on a circular transformation of blocks. Fast C++ backends (via Rcpp') ensure scalability for large time series.
This package provides several classifiers based on probabilistic models. These classifiers allow to model the dependence structure of continuous features through bivariate copula functions and graphical models, see Salinas-Gutiérrez et al. (2014) <doi:10.1007/s00180-013-0457-y>.
The utility of this package includes finite mixture modeling and model-based clustering through Manly mixture models by Zhu and Melnykov (2016) <DOI:10.1016/j.csda.2016.01.015>. It also provides capabilities for forward and backward model selection procedures.
Consistent user interface to the most common regression and classification algorithms, such as random forest, neural networks, C5 trees and support vector machines, complemented with a handful of auxiliary functions, such as variable importance and a tuning function for the parameters.