Processes and investigates spectral data. pavo is useful for the examination of color pattern’s spatial structure using images, without the need for specialized photographic equipment or and/or extensive calibration and processing. It offers a solution for analyzing the physiology, ecology, and evolution of color patterns and visual perception. This tool provides a suite of built-in receptor sensitivities, illuminant and transmission data.
Provides several tools for specialized analysis of animal social networks. Asnipe offers also access to other tools for more general analysis of network structure. This software can (1) define a group by individual matrix, (2) generate an association matrix from observations of individuals co-occuring in time and space, (3) perform permutation tests on the observation stream or (4) calculate lagged association rates between individuals or classes of individuals.
Imports excel files into R. The readxl package makes it easy to get data out of Excel and into R. The range argument of this method provides many ways to limit the read to a specific rectangle of cells. The simplest usage is to provide an Excel-like cell range. This R package has no external dependencies, so it is easy to install and use on all operating systems. It is designed to work with any tabular data. Readxl is a part of the tidyverse, an ecosystem of packages designed with common APIs.
Allows R users to generate Graphical User Interfaces (GUIs) for R scripts. RGG is a general GUI framework for R that consists of two parts: an XML based GUI definition language and a GUI engine. It is available both as a stand-alone software (RGGRunner) and as a plug-in for JGR. The software can be used to make biostatistical and bioinformatical solutions directly available for routine applications.
Solves l1-regularized Gaussian maximum likelihood estimator (MLE). QUIC is an algorithm that uses a quadratic approximation leaning on Newton’s method. It can be applied to recovering a sparse inverse covariance matrix and is able to automatically identify the sparsity structure under block-diagonal case. This program is available as an R package or through a C++ implementation.
Performs graph classification and regression by machine learning algorithms. Graphkernels is a package in R and Python with efficient C++ implementations of various graph kernels including the following prominent kernel families: (1) simple kernels between vertex and/or edge label histograms, (2) graphlet kernels, (3) random walk kernels, and (4) the Weisfeiler-Lehman graph kernel.