Examples: visualization, C++, networks, data cleaning, html widgets, ropensci.

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SensoMineR — by Francois Husson, a year ago

Sensory Data Analysis

Statistical Methods to Analyse Sensory Data. SensoMineR: A package for sensory data analysis. S. Le and F. Husson (2008).

RProtoBuf — by Dirk Eddelbuettel, 2 days ago

R Interface to the 'Protocol Buffers' 'API' (Version 2 or 3)

Protocol Buffers are a way of encoding structured data in an efficient yet extensible format. Google uses Protocol Buffers for almost all of its internal 'RPC' protocols and file formats. Additional documentation is available in two included vignettes one of which corresponds to our 'JSS' paper (2016, . A sufficiently recent version of 'Protocol Buffers' library is required; currently version 3.3.0 from 2017 is the tested minimum.

MuFiMeshGP — by Romain Boutelet, a year ago

Multi-Fidelity Emulator for Computer Experiments with Tunable Fidelity Levels

Multi-Fidelity emulator for data from computer simulations of the same underlying system but at different input locations and fidelity level, where both the input locations and fidelity level can be continuous. Active Learning can be performed with an implementation of the Integrated Mean Square Prediction Error (IMSPE) criterion developed by Boutelet and Sung (2025, ).

keras3 — by Tomasz Kalinowski, 6 months ago

R Interface to 'Keras'

Interface to 'Keras' < https://keras.io>, a high-level neural networks API. 'Keras' was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations of the two), and runs seamlessly on both CPU and GPU devices.

RcppExamples — by Dirk Eddelbuettel, a year ago

Examples using 'Rcpp' to Interface R and C++

Examples for Seamless R and C++ integration The 'Rcpp' package contains a C++ library that facilitates the integration of R and C++ in various ways. This package provides some usage examples. Note that the documentation in this package currently does not cover all the features in the package. The site < https://gallery.rcpp.org> regroups a large number of examples for 'Rcpp'.

lay — by Alexandre Courtiol, 3 years ago

Simple but Efficient Rowwise Jobs

Creating efficiently new column(s) in a data frame (including tibble) by applying a function one row at a time.

RcppBDT — by Dirk Eddelbuettel, 5 months ago

'Rcpp' Bindings for the Boost Date_Time Library

Access to Boost Date_Time functionality for dates, durations (both for days and date time objects), time zones, and posix time ('ptime') is provided by using 'Rcpp modules'. The posix time implementation can support high-resolution of up to nano-second precision by using 96 bits (instead of 64 with R) to present a 'ptime' object (but this needs recompilation with a #define set).

rncl — by Francois Michonneau, 19 days ago

An Interface to the Nexus Class Library

An interface to the Nexus Class Library which allows parsing of NEXUS, Newick and other phylogenetic tree file formats. It provides elements of the file that can be used to build phylogenetic objects such as ape's 'phylo' or phylobase's 'phylo4(d)'. This functionality is demonstrated with 'read_newick_phylo()' and 'read_nexus_phylo()'.

seewave — by Jerome Sueur, a year ago

Sound Analysis and Synthesis

Functions for analysing, manipulating, displaying, editing and synthesizing time waves (particularly sound). This package processes time analysis (oscillograms and envelopes), spectral content, resonance quality factor, entropy, cross correlation and autocorrelation, zero-crossing, dominant frequency, analytic signal, frequency coherence, 2D and 3D spectrograms and many other analyses. See Sueur et al. (2008) and Sueur (2018) .

RInside — by Dirk Eddelbuettel, a year ago

C++ Classes to Embed R in C++ (and C) Applications

C++ classes to embed R in C++ (and C) applications A C++ class providing the R interpreter is offered by this package making it easier to have "R inside" your C++ application. As R itself is embedded into your application, a shared library build of R is required. This works on Linux, OS X and even on Windows provided you use the same tools used to build R itself. Numerous examples are provided in the nine subdirectories of the examples/ directory of the installed package: standard, 'mpi' (for parallel computing), 'qt' (showing how to embed 'RInside' inside a Qt GUI application), 'wt' (showing how to build a "web-application" using the Wt toolkit), 'armadillo' (for 'RInside' use with 'RcppArmadillo'), 'eigen' (for 'RInside' use with 'RcppEigen'), and 'c_interface' for a basic C interface and 'Ruby' illustration. The examples use 'GNUmakefile(s)' with GNU extensions, so a GNU make is required (and will use the 'GNUmakefile' automatically). 'Doxygen'-generated documentation of the C++ classes is available at the 'RInside' website as well.