Found 75 results
1. YAML55 usages
org.renjin.cran » yamlBSD
Implements the 'libyaml' 'YAML' 1.1 parser and emitter (<http://pyyaml.org/wiki/LibYAML>) for R.
Last Release on Feb 13, 2021
Software (bootstrap, cross-validation, jackknife) and data for the book "An Introduction to the Bootstrap" by B. Efron and R. Tibshirani, 1993, Chapman and Hall. This package is primarily provided for projects already based on it, and for support of the book. New projects should preferentially use the recommended package "boot".
Last Release on Feb 14, 2021
Data input/output functions for data that conform to the Digital Imaging and Communications in Medicine (DICOM) standard, part of the Rigorous Analytics bundle.
Last Release on Feb 14, 2021
Functions for the input/output and visualization of medical imaging data that follow either the ANALYZE, NIfTI or AFNI formats. This package is part of the Rigorous Analytics bundle.
Last Release on Sep 17, 2024
5. Fail2 usages
org.renjin.cran » failBSD
More comfortable interface to work with R data or source files in a key-value fashion.
Last Release on Feb 16, 2021
6. Jdx1 usages
org.renjin.cran » jdxBSDGPL
Simplifies and extends data exchange between 'R' and 'Java'.
Last Release on May 1, 2022
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 rjson package. Originally, that was too slow for converting large R objects to JSON and was not extensible. rjson's performance is now similar to this package, and perhaps slightly faster in some cases. This package uses methods ...
Last Release on Feb 15, 2021
Provides implementation of various methods of Functional Data Analysis (FDA) and Empirical Dynamics. The core of this package is Functional Principal Component Analysis (FPCA), a key technique for functional data analysis, for sparsely or densely sampled random trajectories and time courses, via the Principal Analysis by Conditional Estimation (PACE) algorithm or numerical integration. PACE is useful for the analysis of data that have been generated by a sample of underlying (but usually not fully observed) ...
Last Release on Apr 30, 2022
Provides routines for density and moments of the Conway-Maxwell-Poisson distribution as well as functions for fitting the COM-Poisson model for over/under-dispersed count data.
Last Release on Feb 13, 2021