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R Interface to the Yacas Computer Algebra System

An interface to the yacas computer algebra system.

Stineman, a Consistently Well Behaved Method of Interpolation

A consistently well behaved method of interpolation based on piecewise rational functions using Stineman's algorithm.

Diagnostics Plots for Bicluster Data

Diagnostic tools based on two-way anova and median-polish residual plots for Bicluster output obtained from packages; "biclust" by Kaiser et al.(2008),"isa2" by Csardi et al. (2010) and "fabia" by Hochreiter et al. (2010). Moreover, It provides visualization tools for bicluster output and corresponding non-bicluster rows- or columns outcomes. It has also extended the idea of Kaiser et al.(2008) which is, extracting bicluster output in a text format, by adding two bicluster methods from the fabia and isa2 R packages.

Image Tools for Automated Wood Identification

This tool, wood vision tool, is intended to facilitate preprocessing and analyzing 2-dimensional wood images toward automated recognition. The former includes some basics such as functions to RGB to grayscale, gray to binary, cropping, rotation(bilinear), median/mean/Gaussian filter, and Canny/Sobel edge detection. The latter includes gray level co-occurrence matrix (GLCM), Haralick parameters, local binary pattern (LBP), higher order local autocorrelation (HLAC), Fourier transform (radial and azimuthal integration), and Gabor filtering. The functions are intended to read data using 'readTIFF(x,info=T)' from 'tiff' package. The functions in this packages basically assumes the grayscale images as input data, thus the color images should be subjected to the function rgb2gray() before used for some other functions.

Build Graphs for Landscape Genetics Analysis

Build graphs for landscape genetics analysis. This set of
functions can be used to import and convert spatial and genetic data
initially in different formats, import landscape graphs created with
'GRAPHAB' software (Foltete et al., 2012)

Glucose Variability Measures from Continuous Glucose Monitoring Data

Calculate different glucose variability measures,
including average measures of glycemia, measures of glycemic variability and measures of glycemic risk,
from continuous glucose monitoring data obtained from diabetic patients.
Boris P. Kovatchev, Erik Otto, Daniel Cox, Linda Gonder-Frederick, and William Clarke (2006)

Tools for Descriptive Statistics

A collection of miscellaneous basic statistic functions and convenience wrappers for efficiently describing data. The author's intention was to create a toolbox, which facilitates the (notoriously time consuming) first descriptive tasks in data analysis, consisting of calculating descriptive statistics, drawing graphical summaries and reporting the results. The package contains furthermore functions to produce documents using MS Word (or PowerPoint) and functions to import data from Excel. Many of the included functions can be found scattered in other packages and other sources written partly by Titans of R. The reason for collecting them here, was primarily to have them consolidated in ONE instead of dozens of packages (which themselves might depend on other packages which are not needed at all), and to provide a common and consistent interface as far as function and arguments naming, NA handling, recycling rules etc. are concerned. Google style guides were used as naming rules (in absence of convincing alternatives). The 'camel style' was consequently applied to functions borrowed from contributed R packages as well.