Planet R

September 16, 2014

CRANberries

New package redcapAPI with initial version 1.0

Package: redcapAPI
Type: Package
Title: R interface to REDCap
Version: 1.0
Date: 2014-09-15
Author: Benjamin Nutter. Initiated by Jeffrey Horner and Will Gray with contributions from Jeremy Stephens, and Will Beasley
Maintainer: Benjamin Nutter
Description: Access data stored in REDCap databases using the Application Programming Interface (API). REDCap (Research Electronic Data CAPture) is a web application for building and managing online surveys and databases developed at Vanderbilt University. The API allows users to access data and project meta data (such as the data dictionary) from the web programmatically. The redcapAPI package facilitates the process of accessing data with options to prepare an analysis-ready data set consistent with the definitions in a database's data dictionary.
License: GPL-2
Imports: chron, httr, stringr
Suggests: DBI, Hmisc
LazyLoad: yes
URL: https://github.com/nutterb/redcapAPI/wiki, https://github.com/nutterb/redcapAPI, http://project-redcap.org
Packaged: 2014-09-15 13:10:44 UTC; nutterb
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2014-09-16 02:22:08

More information about redcapAPI at CRAN

September 16, 2014 01:13 AM

New package GlobalOptions with initial version 0.0.1

Package: GlobalOptions
Type: Package
Title: Generate functions to get or set global options
Version: 0.0.1
Date: 2014-9-15
Author: Zuguang Gu
Maintainer: Zuguang Gu
Depends: R (>= 2.10.0)
Imports: methods
Suggests: testthat (>= 0.3)
Description: Generate functions to get or set global options
URL: https://github.com/jokergoo/GlobalOptions
License: GPL (>= 2)
Packaged: 2014-09-15 21:09:26 UTC; IBM
Repository: CRAN
Date/Publication: 2014-09-16 02:03:23
NeedsCompilation: no

More information about GlobalOptions at CRAN

September 16, 2014 01:13 AM

September 15, 2014

CRANberries

New package SOR with initial version 0.22

Package: SOR
Type: Package
Title: Estimation using Sequential Offsetted Regression
Version: 0.22
Date: 2014-9-15
Author: Lee S. McDaniel, Jonathan S. Schildcrout
Maintainer: Lee S. McDaniel
Depends: Matrix, stats
Description: Estimation for longitudinal data following outcome dependent sampling using the sequential offsetted regression technique. Includes support for binary, count, and continuous data.
License: GPL (>= 2)
Packaged: 2014-09-15 14:42:51 UTC; lmcda4
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-09-15 17:05:56

More information about SOR at CRAN

September 15, 2014 03:13 PM

New package propOverlap with initial version 1.0

Package: propOverlap
Type: Package
Title: Feature (gene) selection based on the Proportional Overlapping Scores
Version: 1.0
Date: 2014-09-15
Author: Osama Mahmoud, Andrew Harrison, Aris Perperoglou, Asma Gul, Zardad Khan, Berthold Lausen
Maintainer: Osama Mahmoud
Description: A package for selecting the most relevant features (genes) in the high-dimensional binary classification problems. The discriminative features are identified using analyzing the overlap between the expression values across both classes. The package includes functions for measuring the proportional overlapping score for each gene avoiding the outliers effect. The used measure for the overlap is the one defined in the "Proportional Overlapping Score (POS)" technique for feature selection. A gene mask which represents a gene's classification power can also be produced for each gene (feature). The set size of the selected genes might be set by the user. The minimum set of genes that correctly classify the maximum number of the given tissue samples (observations) can be also produced.
Depends: R (>= 2.10), Biobase
LazyLoad: yes
License: GPL (>= 2)
Repository: CRAN
Packaged: 2014-09-15 12:28:08 UTC; osama
NeedsCompilation: no
Date/Publication: 2014-09-15 17:06:03

More information about propOverlap at CRAN

September 15, 2014 03:13 PM

New package hot.deck with initial version 1.0

Package: hot.deck
Type: Package
Title: Multiple Hot-deck Imputation
Version: 1.0
Date: 2014-09-03
Author: Skyler Cranmer, Jeff Gill, Natalie Jackson, Andreas Murr, Dave Armstrong
Maintainer: Dave Armstrong
Description: Performs multiple hot-deck imputation of categorical and continuous variables in a data frame.
License: GPL (>= 2)
Depends: R (>= 3.0), mice
Suggests: knitr, mitools, miceadds, Zelig
Packaged: 2014-09-15 14:26:05 UTC; armstrod
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-09-15 17:06:01

More information about hot.deck at CRAN

September 15, 2014 03:13 PM

New package nonnest2 with initial version 0.1-1

Package: nonnest2
Title: Tests of non-nested models
Version: 0.1-1
Author: Ed Merkle and Dongjun You
Maintainer: Dongjun You
Description: Testing non-nested models via theory supplied by Vuong (1989). Includes tests of model distinguishability and of model fit that can be applied to both nested and non-nested models. Also includes functionality to obtain confidence intervals associated with AIC and BIC.
Depends: R (>= 3.0.0)
Imports: CompQuadForm, mvtnorm, sandwich
License: GPL-2 | GPL-3
LazyData: yes
Packaged: 2014-09-15 11:53:15 UTC; Jun
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-09-15 14:20:59

More information about nonnest2 at CRAN

September 15, 2014 01:13 PM

New package binequality with initial version 0.6.1

Package: binequality
Type: Package
Title: Methods for Analyzing Binned Income Data
Version: 0.6.1
Date: 2014-09-14
Author: Samuel V. Scarpino, Paul von Hippel, and Igor Holas
Maintainer: Samuel V. Scarpino
Description: Methods for model selection, model averaging, and calculating metrics, such as the Gini, Theil, Mean Log Deviation, etc, on binned income data where the topmost bin is right-censored. We provide both a non-parametric method, termed the bounded midpoint estimator (BME), which assigns cases to their bin midpoints; except for the censored bins, where cases are assigned to an income estimated by fitting a Pareto distribution. Because the usual Pareto estimate can be inaccurate or undefined, especially in small samples, we implement a bounded Pareto estimate that yields much better results. We also provide a parametric approach, which fits distributions from the generalized beta (GB) family. Because some GB distributions can have poor fit or undefined estimates, we fit 10 GB-family distributions and use multimodel inference to obtain definite estimates from the best-fitting distributions. We also provide binned income data from all United States of America school districts, counties, and states.
License: GPL (>= 3.0)
LazyLoad: yes
Depends: R (>= 2.10), gamlss (>= 4.2.7), gamlss.cens (>= 4.2.7), gamlss.dist (>= 4.3.0)
Imports: survival (>= 2.37-7), ineq (>= 0.2-11)
Packaged: 2014-09-14 22:50:02 UTC; scarpino
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-09-15 07:27:50

More information about binequality at CRAN

September 15, 2014 07:13 AM

September 14, 2014

CRANberries

New package DLMtool with initial version 1.34

Package: DLMtool
Type: Package
Title: Data-Limited Methods Toolkit
Version: 1.34
Date: 2014-09-11
Author: Tom Carruthers
Maintainer: Tom Carruthers
Description: Simulation testing and implementation of data-limited fishery stock assessment methods
License: GPL-2
Depends: R (>= 2.10.0), methods, snowfall, boot, MASS
LazyData: yes
LazyLoad: yes
VignetteBuilder: knitr
Suggests: knitr
Packaged: 2014-09-14 05:06:54 UTC; Tom
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-09-14 10:12:18

More information about DLMtool at CRAN

September 14, 2014 09:13 AM

Alstatr

Python: Enthought Canopy Installation in Ubuntu

Enthought Canopy is a comprehensive Python analysis environment with easy installation and updates of the proven Enthought Python distribution - all part of a robust platform you can explore, develop and visualize on. (Ref. 1)

To install this, do the following:
  1. Visit the Enthought website's download page, and download the .sh file;
  2. After successful download, open your Terminal, or press Ctrl+Alt+T;
  3. In the terminal, set the directory to the location of the .sh file, for my case it's on the Download folder. Thus I run the command, cd ~/Downloads
  4. Next is to install this using the following command,

    See Figure 1.
  5. Press Enter to continue, and follow the instructions in the Terminal.
Figure 1: Canopy Installation Wizard in Ubuntu Terminal.
Run Canopy after successful installation. For machines with small desktop resolution, I suggest to resize the font in the editor. And to do that, simply go to Edit > Preference. In the General tab, click on the font, and customize it to your likings, just restart the program to see the changes made.
Figure 2: Enthought Canopy running on Ubuntu.
Enjoy coding!

Reference

  1. Enthought Canopy. Enthought Scientific Computing Solutions.

by Al-Ahmadgaid Asaad (noreply@blogger.com) at September 14, 2014 12:52 AM

September 13, 2014

Removed CRANberries

Package MASSI (with last version 1.1) was removed from CRAN

Previous versions (as known to CRANberries) which should be available via the Archive link are:

2013-09-30 1.1
2013-08-20 1.0

September 13, 2014 09:13 AM

CRANberries

New package recosystem with initial version 0.2.4

Package: recosystem
Type: Package
Title: Recommender System using Matrix Factorization
Version: 0.2.4
Date: 2014-09-12
Author: Yixuan Qiu, Chih-Jen Lin, Yu-Chin Juan, Yong Zhuang, Wei-Sheng Chin and other contributors. See file AUTHORS for details.
Maintainer: Yixuan Qiu
Description: This package is an R wrapper of the libmf library (http://www.csie.ntu.edu.tw/~cjlin/libmf/) for recommender system using matrix factorization. It is typically used to approximate an incomplete matrix using the product of two matrices in a latent space. Other common names for this task include "collaborative filtering", "matrix completion", "matrix recovery", etc.
License: BSD_3_clause + file LICENSE
Copyright: see file COPYRIGHTS
URL: https://github.com/yixuan/recosystem
BugReports: https://github.com/yixuan/recosystem/issues
SystemRequirements: C++11
Depends: methods
Imports: Rcpp (>= 0.11.0)
Suggests: knitr
LinkingTo: Rcpp
VignetteBuilder: knitr
Packaged: 2014-09-12 20:52:34 UTC; qyx
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2014-09-13 08:01:57

More information about recosystem at CRAN

September 13, 2014 07:13 AM

September 12, 2014

CRANberries

New package treatSens with initial version 1.0

Package: treatSens
Type: Package
Title: Sensitivity analysis for causal inference
Version: 1.0
Date: 2014-09-02
Author: Nicole Bohme Carnegie, Masataka Harada, Jennifer Hill
Maintainer: "Nicole Bohme Carnegie"
Depends: R (>= 3.1.0)
Suggests: doParallel, foreach
Description: Utilities to investigate sensitivity to unmeasured confounding in parametric models with either binary or continuous treatment.
License: GPL-2
LazyLoad: yes
LazyData: yes
Packaged: 2014-09-12 17:24:24 UTC; carnegin
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-09-12 20:27:07

More information about treatSens at CRAN

September 12, 2014 07:13 PM

New package nettools with initial version 1.0.1

Package: nettools
Type: Package
Title: A Network Comparison Framework
Version: 1.0.1
Date: 2014-09-02
Authors@R: c(person("Michele","Filosi",role=c("aut","cre"), email = "filosi@fbk.eu"), person("Roberto", "Visintainer", role = "aut", email = "visintainer@fbk.eu"), person("Samantha", "Riccadonna", role = "aut", email = "samantha.riccadonna@fmach.it"), person("Giuseppe", "Jurman", role = "ctb", email = "jurman@fbk.eu"), person("Cesare", "Furlanello", role = "ctb", email = "furlan@fbk.eu"))
Maintainer: Michele Filosi
Depends: R (>= 2.14.1), methods
Imports: parallel, rootSolve, dtw, WGCNA, minet, Matrix, minerva, combinat, igraph
Description: A collection of network inference methods for co-expression networks, quantitative network distances and a novel framework for network stability analysis.
License: CC BY-NC-SA 4.0
Packaged: 2014-09-11 10:37:46 UTC; michele
Author: Michele Filosi [aut, cre], Roberto Visintainer [aut], Samantha Riccadonna [aut], Giuseppe Jurman [ctb], Cesare Furlanello [ctb]
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-09-12 16:16:26

More information about nettools at CRAN

September 12, 2014 03:13 PM

Alstatr

R: k-Means Clustering on an Image

Enough with the theory we recently published, let's take a break and have fun on the application of Statistics used in Data Mining and Machine Learning, the k-Means Clustering.
k-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. (Wikipedia, Ref 1.)
We will apply this method to an image, wherein we group the pixels into k different clusters. Below is the image that we are going to use,
Colorful Bird From Wall321
We will utilize the following packages for input and output:
  1. jpeg - Read and write JPEG images; and,
  2. ggplot2 - An implementation of the Grammar of Graphics.

Download and Read the Image

Let's get started by downloading the image to our workspace, and tell R that our data is a JPEG file.

Cleaning the Data

Extract the necessary information from the image and organize this for our computation:

The image is represented by large array of pixels with dimension rows by columns by channels -- red, green, and blue or RGB.

Plotting

Plot the original image using the following codes:

Clustering

Apply k-Means clustering on the image:

Plot the clustered colours:

Possible clusters of pixels on different k-Means:

Originalk = 6
Table 1: Different k-Means Clustering.
k = 5k = 4
k = 3k = 2

I suggest you try it!

Reference

  1. K-means clustering. Wikipedia. Retrieved September 11, 2014.

by Al-Ahmadgaid Asaad (noreply@blogger.com) at September 12, 2014 12:40 PM

R: Image Analysis using EBImage

Currently, I am taking Statistics for Image Analysis on my masteral, and have been exploring this topic in R. One package that has the capability in this field is the EBImage from Bioconductor, which will be showcased in this post.

Installation


For those using Ubuntu, you may likely to encounter this error:

It has something to do with the tiff.h C header file, but it's not that serious since mytechscribblings has an effective solution for this, do check that out.

Importing Data

To import a raw image, consider the following codes:

Output of display(Image).
Yes, this is the photo that we are going to use for our analysis. Needless to say, that's me and my friends. In the proceeding section we will do image manipulation and other processing.

Image Properties

So what do we get from our raw image? To answer that, simply run print(Image). This will return the properties of the image, including the array of pixel values. With these information, we apply mathematical and statistical operations to do enhancement on the image.

There are two sections (Summary and array of the pixels) in the above output, with the following entries for the first section:

CodeValueDescription
Table 1: Information from 1st section of print(Image).
colormodeColorThe type (Color/Grayscale) of the color of the image.
storage.modedoubleType of values in the array.
dim1984 1488 3Dimension of the array, (x, y, z).
nb.total.frames:3Number of channels in each pixel, z entry in dim.
nb.render.frames1Number of channels rendered.

The second section is the obtained values from mapping pixels in the image to the real line between 0 and 1 (inclusive). Both extremes of this interval [0, 1], are black and white colors, respectively. Hence, pixels with values closer to any of these end points are expected to be darker or lighter, respectively. And because pixels are contained in a large array, then we can do all matrix manipulations available in R for processing.

Adjusting Brightness

It is better to start with the basic first, one of which is the brightness. As discussed above, brightness can be manipulated using + or -:

LighterDarker
Table 2: Adjusting Brightness.
Output of display(Image1).
Output of display(Image2).

Adjusting Contrast

Contrast can be manipulated using multiplication operator(*):

LowHigh
Table 3: Adjusting Contrast.
Output of display(Image3).
Output of display(Image4).

Gamma Correction

Gamma correction is the name of a nonlinear operation used to code and decode luminance or tristimulus values in video or still image systems, defined by the following power-law expression: \begin{equation}\nonumber V_{\mathrm{out}} = AV_{\mathrm{in}}^{\gamma} \end{equation} where $A$ is a constant and the input and output values are non-negative real values; in the common case of $A = 1$, inputs and outputs are typically in the range 0-1. A gamma value $\gamma< 1$ is sometimes called an encoding gamma (Wikipedia, Ref. 1).

$\gamma = 2$$\gamma = 0.7$
Table 4: Adjusting Gamma Correction.
Output of display(Image5).
Output of display(Image6).

Cropping

Slicing array of pixels, simply mean cropping the image.

Output of the above code.

Spatial Transformation

Spatial manipulation like rotate (rotate), flip (flip), and translate (translate) are also available in the package. Check this out,


Color Management

Since the array of pixels has three axes in its dimension, for example in our case is 1984 x 1488 x 3. The third axis is the slot for the three channels: Red, Green and Blue, or RGB. Hence, transforming the color.mode from Color to Grayscale, implies disjoining the three channels from single rendered frame (three channels for each pixel) to three separate array of pixels for red, green, and blue frames.

OriginalRed Channel
Table 5: Color Mode Transformation.
Green ChannelBlue Channel

To revert the color mode, simply run

Filtering

In this section, we will do smoothing/blurring using low-pass filter, and edge-detection using high-pass filter. In addition, we will also investigate median filter to remove noise.

Low-Pass (Blur)
Table 6: Image Filtering.
High Pass

OriginalFiltered
Table 7: Median Filter.
From Google, Link Here.
Output of display(medFltr)

For comparison, I run median filter on first-neighborhood in Mathematica, and I got this
Clearly, Mathematica has better enhancement than R for this particular filter. But R has a good foundation already, as we witness with EBImage. There are still lots of interesting functions in the said package, that is worth exploring, I suggest you check that out.

For the meantime, we will stop here, but hoping we can play more on this topic in the succeeding post.

References

  1. Gamma Correction. Wikipedia. Retrieved August 31, 2014.
  2. Gregoire Pau, Oleg Sklyar, Wolfgang Huber (2014). Introduction to EBImage, an image processing and analysis toolkit for R.

by Al-Ahmadgaid Asaad (noreply@blogger.com) at September 12, 2014 11:57 AM

R you ready?

plot_9

Today I stumbled across a figure in an explanation on multiple factor analysis which contained pictograms.

 

abdi_mfa

Figure 1 from Abdi & Valentin (2007), p. 8.

I wanted to reproduce a similar figure in R using pictograms and additionally color them e.g. by group membership . I have almost no knowledge about image processing, so I tried out several methods of how to achieve what I want. The first thing I did was read in an PNG file and look at the data structure. The package png allows to read in PNG files. Note that all of the below may not work on Windows machines, as it does not support semi-transparency (see ?readPNG).

library(png)
img <- readPNG(system.file("img", "Rlogo.png", package="png"))
class(img)
## [1] "array"
dim(img)
## [1]  76 100   4

The object is a numerical array with four layers (red, green, blue, alpha; short RGBA). Let’s have a look at the first layer (red) and replace all non-zero entries by a one and the zeros by a dot. This will show us the pattern of non-zero values and we already see the contours.

l4 <- img[,,1]
l4[l4 > 0] <- 1
l4[l4 == 0] <- "."
d <- apply(l4, 1, function(x) {
 cat(paste0(x, collapse=""), "\n") 
})

To display the image in R one way is to raster the image (i.e. the RGBA layers are collapsed into a layer of single HEX value) and print it using rasterImage.

r_logo_layer_1

rimg <- as.raster(img) # raster multilayer object
r <- nrow(rimg) / ncol(rimg) # image ratio
plot(c(0,1), c(0,r), type = "n", xlab = "", ylab = "", asp=1)
rasterImage(rimg, 0, 0, 1, r) 

plot_1

Let’s have a look at a small part the rastered image object. It is a matrix of HEX values.

rimg[40:50, 1:6]
## [1,] "#C4C5C202" "#858981E8" "#838881FF" "#888D86FF" "#8D918AFF" "#8F938CFF"
## [2,] "#00000000" "#848881A0" "#80847CFF" "#858A83FF" "#898E87FF" "#8D918BFF"
## [3,] "#00000000" "#8B8E884C" "#7D817AFF" "#82867EFF" "#868B84FF" "#8A8E88FF"
## [4,] "#00000000" "#9FA29D04" "#7E827BE6" "#7E817AFF" "#838780FF" "#878C85FF"
## [5,] "#00000000" "#00000000" "#81857D7C" "#797E75FF" "#7F827BFF" "#838781FF"
## [6,] "#00000000" "#00000000" "#898C8510" "#787D75EE" "#797E76FF" "#7F837BFF"
## [7,] "#00000000" "#00000000" "#00000000" "#7F837C7B" "#747971FF" "#797E76FF"
## [8,] "#00000000" "#00000000" "#00000000" "#999C9608" "#767C73DB" "#747971FF"
## [9,] "#00000000" "#00000000" "#00000000" "#00000000" "#80847D40" "#71766EFD"
## [10,] "#00000000" "#00000000" "#00000000" "#00000000" "#00000000" "#787D7589"
## [11,] "#00000000" "#00000000" "#00000000" "#00000000" "#00000000" "#999C9604"

And print this small part.

plot(c(0,1), c(0,.6), type = "n", xlab = "", ylab = "", asp=1)
rasterImage(rimg[40:50, 1:6], 0, 0, 1, .6) 

plot_2

Now we have an idea of how the image object and the rastered object look like from the inside. Let’s start to modify the images to suit our needs.

In order to change the color of the pictograms, my first idea was to convert the graphics to greyscale and remap the values to a color ramp of may choice. To convert to greyscale there are tons of methods around (see e.g. here). I just pick one of them I found on SO by chance. With R=Red, G=Green and B=Blue we have

brightness = sqrt(0.299 * R^2 + 0.587 * G^2 + 0.114 * B^2)

This approach modifies the PNG files after they have been coerced into a raster object.

# function to calculate brightness values
brightness <- function(hex) {
  v <- col2rgb(hex)
  sqrt(0.299 * v[1]^2 + 0.587 * v[2]^2 + 0.114 * v[3]^2) /255
}

# given a color ramp, map brightness to ramp also taking into account 
# the alpha level. The defaul color ramp is grey
#
img_to_colorramp <- function(img, ramp=grey) {
  cv <- as.vector(img)
  b <- sapply(cv, brightness)
  g <- ramp(b)
  a <- substr(cv, 8,9)     # get alpha values
  ga <- paste0(g, a)       # add alpha values to new colors
  img.grey <- matrix(ga, nrow(img), ncol(img), byrow=TRUE)  
}

# read png and modify
img <- readPNG(system.file("img", "Rlogo.png", package="png"))
img <- as.raster(img)           # raster multilayer object
r <- nrow(img) / ncol(img)      # image ratio
s <- 3.5                        # size

plot(c(0,10), c(0,3.5), type = "n", xlab = "", ylab = "", asp=1)

rasterImage(img, 0, 0, 0+s/r, 0+s)  # original
img2 <- img_to_colorramp(img)       # modify using grey scale
rasterImage(img2, 5, 0, 5+s/r, 0+s)

plot_3

Great, it works! Now Let’s go and try out some other color palettes using colorRamp to create a color ramp.

plot(c(0,10),c(0,8.5), type = "n", xlab = "", ylab = "", asp=1)

img1 <- img_to_colorramp(img)
rasterImage(img1, 0, 5, 0+s/r, 5+s)

reds <- function(x) 
  rgb(colorRamp(c("darkred", "white"))(x), maxColorValue = 255)
img2 <- img_to_colorramp(img, reds)
rasterImage(img2, 5, 5, 5+s/r, 5+s)

greens <- function(x) 
  rgb(colorRamp(c("darkgreen", "white"))(x), maxColorValue = 255)
img3 <- img_to_colorramp(img, greens)
rasterImage(img3, 0, 0, 0+s/r, 0+s)

single_color <- function(...) "#0000BB"
img4 <- img_to_colorramp(img, single_color)
rasterImage(img4, 5, 0, 5+s/r, 0+s)

plot_4

Okay, that basically does the job. Now we will apply it to the wine pictograms.
Let’s use this wine glass from Wikimedia Commons. It’s quite big so I uploaded a reduced size version to imgur . We will use it for our purposes.

# load file from web
f <- tempfile()
download.file("http://i.imgur.com/A14ntCt.png", f)
img <- readPNG(f)
img <- as.raster(img)
r <- nrow(img) / ncol(img)
s <- 1

# let's create a function that returns a ramp function to save typing
ramp <- function(colors) 
  function(x) rgb(colorRamp(colors)(x), maxColorValue = 255)

# create dataframe with coordinates and colors
set.seed(1)
x <- data.frame(x=rnorm(16, c(2,2,4,4)), 
                y=rnorm(16, c(1,3)), 
                colors=c("black", "darkred", "garkgreen", "darkblue"))

plot(c(1,6), c(0,5), type="n", xlab="", ylab="", asp=1)
for (i in 1L:nrow(x)) {
  colorramp <- ramp(c(x[i,3], "white"))
  img2 <- img_to_colorramp(img, colorramp)
  rasterImage(img2, x[i,1], x[i,2], x[i,1]+s/r, x[i,2]+s)
}

plot_5

Another approach would be to modifying the RGB layers before rastering to HEX values.

img <- readPNG(system.file("img", "Rlogo.png", package="png"))
img2 <- img
img[,,1] <- 0    # remove Red component
img[,,2] <- 0    # remove Green component
img[,,3] <- 1    # Set Blue to max
img <- as.raster(img)
r <- nrow(img) / ncol(img)  # size ratio
s <- 3.5   # size
plot(c(0,10), c(0,3.5), type = "n", xlab = "", ylab = "", asp=1)
rasterImage(img, 0, 0, 0+s/r, 0+s)

img2[,,1] <- 1   # Red to max
img2[,,2] <- 0
img2[,,3] <- 0
rasterImage(as.raster(img2), 5, 0, 5+s/r, 0+s)

plot_6

To just colorize the image, we could weight each layer.

# wrap weighting into function
weight_layers <- function(img, w) {
  for (i in seq_along(w))
    img[,,i] <- img[,,i] * w[i]
  img
}

plot(c(0,10), c(0,3.5), type = "n", xlab = "", ylab = "", asp=1)
img <- readPNG(system.file("img", "Rlogo.png", package="png"))
img2 <- weight_layers(img, c(.2, 1,.2))
rasterImage(img2, 0, 0, 0+s/r, 0+s)

img3 <- weight_layers(img, c(1,0,0))
rasterImage(img3, 5, 0, 5+s/r, 0+s)

plot_7

After playing around and hard-coding the modifications I started to search and found the EBimage package which has a lot of features for image processing that make ones life (in this case only a bit) easier.

library(EBImage)
f <- system.file("img", "Rlogo.png", package="png")
img <- readImage(f) 
img2 <- img

img[,,2] = 0      # zero out green layer
img[,,3] = 0      # zero out blue layer
img <- as.raster(img)

img2[,,1] = 0
img2[,,3] = 0
img2 <- as.raster(img2)

r <- nrow(img) / ncol(img)
s <- 3.5
plot(c(0,10), c(0,3.5), type = "n", xlab = "", ylab = "", asp=1)
rasterImage(img, 0, 0, 0+s/r, 0+s)
rasterImage(img2, 5, 0, 5+s/r, 0+s)

plot_8

EBImage is a good choice and fairly easy to handle. Now let’s again print the pictograms.

f <- tempfile(fileext=".png")
download.file("http://i.imgur.com/A14ntCt.png", f)
img <- readImage(f)

# will replace whole image layers by one value
# only makes sense if there is a alpha layer that 
# gives the contours
#
mod_color <- function(img, col) {
  v <- col2rgb(col) / 255
  img = channel(img, 'rgb')
  img[,,1] = v[1]   # Red
  img[,,2] = v[2]   # Green
  img[,,3] = v[3]   # Blue
  as.raster(img)  
}

r <- nrow(img) / ncol(img)  # get image ratio
s <- 1                      # size

# create random data
set.seed(1)
x <- data.frame(x=rnorm(16, c(2,2,4,4)), 
                y=rnorm(16, c(1,3)), 
                colors=1:4)

# plot pictograms 
plot(c(1,6), c(0,5), type="n", xlab="", ylab="", asp=1)
for (i in 1L:nrow(x)) {
  img2 <- mod_color(img, x[i, 3])
  rasterImage(img2, x[i,1], x[i,2], x[i,1]+s*r, x[i,2]+s)
}

plot_9

Note, that above I did not bother to center each pictogram to position it correctly. This still needs to be done. Anyway, that’s it! Mission completed.

Literature

Abdi, H., & Valentin, D. (2007). Multiple factor analysis (MFA). In N. Salkind (Ed.), Encyclopedia of Measurement and Statistics (pp. 1–14). Thousand Oaks, CA: Sage Publications. Retrieved from https://www.utdallas.edu/~herve/Abdi-MFA2007-pretty.pdf


by markheckmann at September 12, 2014 09:19 AM

Removed CRANberries

Package recosystem (with last version 0.2.3) was removed from CRAN

Previous versions (as known to CRANberries) which should be available via the Archive link are:

2014-09-11 0.2.3

September 12, 2014 09:13 AM

CRANberries

New package Mobilize with initial version 2.16-4

Package: Mobilize
Type: Package
Title: Mobilize plots and functions
Version: 2.16-4
Date: 2014-09-11
Author: Jeroen Ooms
Maintainer: Jeroen Ooms
Description: Some canned plots and functions designed for the mobilize project. Designed to be called remotely.
License: GPL
Depends: R (>= 2.14), stats, methods, Ohmage
Imports: ggplot2, wordcloud, reshape2
Suggests: XML, maps
Packaged: 2014-09-11 10:24:17 UTC; jeroen
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-09-12 07:53:18

More information about Mobilize at CRAN

September 12, 2014 07:13 AM

Removed CRANberries

Package MarkedPointProcess (with last version 0.2.22) was removed from CRAN

Previous versions (as known to CRANberries) which should be available via the Archive link are:

2014-09-09 0.2.22
2013-12-05 0.2.21
2011-10-23 0.2.20
2011-03-02 0.2.17
2010-09-16 0.2.15
2009-11-01 0.2.13
2009-06-29 0.2.11
2008-08-22 0.2.9
2008-03-27 0.2.8
2008-02-19 0.2.7
2008-02-03 0.2.5
2007-12-14 0.2.4
2007-10-04 0.2.3
2006-09-23 0.2.2

September 12, 2014 07:13 AM

Journal of Statistical Software

structSSI: Simultaneous and Selective Inference for Grouped or Hierarchically Structured Data

Vol. 59, Issue 13, Sep 2014

Abstract:

The R package structSSI provides an accessible implementation of two recently developed simultaneous and selective inference techniques: the group Benjamini-Hochberg and hierarchical false discovery rate procedures. Unlike many multiple testing schemes, these methods specifically incorporate existing information about the grouped or hierarchical dependence between hypotheses under consideration while controlling the false discovery rate. Doing so increases statistical power and interpretability. Furthermore, these procedures provide novel approaches to the central problem of encoding complex dependency between hypotheses.
We briefly describe the group Benjamini-Hochberg and hierarchical false discovery rate procedures and then illustrate them using two examples, one a measure of ecological microbial abundances and the other a global temperature time series. For both procedures, we detail the steps associated with the analysis of these particular data sets, including establishing the dependence structures, performing the test, and interpreting the results. These steps are encapsulated by R functions, and we explain their applicability to general data sets.

September 12, 2014 07:00 AM

Capabilities of R Package mixAK for Clustering Based on Multivariate Continuous and Discrete Longitudinal Data

Vol. 59, Issue 12, Sep 2014

Abstract:

R package mixAK originally implemented routines primarily for Bayesian estimation of finite normal mixture models for possibly interval-censored data. The functionality of the package was considerably enhanced by implementing methods for Bayesian estimation of mixtures of multivariate generalized linear mixed models proposed in Komárek and Komárková (2013). Among other things, this allows for a cluster analysis (classification) based on multivariate continuous and discrete longitudinal data that arise whenever multiple outcomes of a different nature are recorded in a longitudinal study. This package also allows for a data-driven selection of a number of clusters as methods for selecting a number of mixture components were implemented. A model and clustering methodology for multivariate continuous and discrete longitudinal data is overviewed. Further, a step-by-step cluster analysis based jointly on three longitudinal variables of different types (continuous, count, dichotomous) is given, which provides a user manual for using the package for similar problems.

September 12, 2014 07:00 AM

hmmm: An R Package for Hierarchical Multinomial Marginal Models

Vol. 59, Issue 11, Sep 2014

Abstract:

In this paper we show how complete hierarchical multinomial marginal (HMM) models for categorical variables can be defined, estimated and tested using the R package hmmm. Models involving equality and inequality constraints on marginal parameters are needed to define hypotheses of conditional independence, stochastic dominance or notions of positive dependence, or when the parameters are allowed to depend on covariates. The hmmm package also serves the need of estimating and testing HMM models under equality and inequality constraints on marginal interactions.

September 12, 2014 07:00 AM

Tidy Data

Vol. 59, Issue 10, Sep 2014

Abstract:

A huge amount of effort is spent cleaning data to get it ready for analysis, but there has been little research on how to make data cleaning as easy and effective as possible. This paper tackles a small, but important, component of data cleaning: data tidying. Tidy datasets are easy to manipulate, model and visualize, and have a specific structure: each variable is a column, each observation is a row, and each type of observational unit is a table. This framework makes it easy to tidy messy datasets because only a small set of tools are needed to deal with a wide range of un-tidy datasets. This structure also makes it easier to develop tidy tools for data analysis, tools that both input and output tidy datasets. The advantages of a consistent data structure and matching tools are demonstrated with a case study free from mundane data manipulation chores.

September 12, 2014 07:00 AM

A Kenward-Roger Approximation and Parametric Bootstrap Methods for Tests in Linear Mixed Models – The R Package pbkrtest

Vol. 59, Issue 9, Sep 2014

Abstract:

When testing for reduction of the mean value structure in linear mixed models, it is common to use an asymptotic χ2 test. Such tests can, however, be very poor for small and moderate sample sizes. The pbkrtest package implements two alternatives to such approximate χ2 tests: The package implements (1) a Kenward-Roger approximation for performing F tests for reduction of the mean structure and (2) parametric bootstrap methods for achieving the same goal. The implementation is focused on linear mixed models with independent residual errors. In addition to describing the methods and aspects of their implementation, the paper also contains several examples and a comparison of the various methods.

September 12, 2014 07:00 AM

dglars: An R Package to Estimate Sparse Generalized Linear Models

Vol. 59, Issue 8, Sep 2014

Abstract:

dglars is a publicly available R package that implements the method proposed in Augugliaro, Mineo, and Wit (2013), developed to study the sparse structure of a generalized linear model. This method, called dgLARS, is based on a differential geometrical extension of the least angle regression method proposed in Efron, Hastie, Johnstone, and Tibshirani (2004). The core of the dglars package consists of two algorithms implemented in Fortran 90 to efficiently compute the solution curve: a predictor-corrector algorithm, proposed in Augugliaro et al. (2013), and a cyclic coordinate descent algorithm, proposed in Augugliaro, Mineo, and Wit (2012). The latter algorithm, as shown here, is significantly faster than the predictor-corrector algorithm. For comparison purposes, we have implemented both algorithms.

September 12, 2014 07:00 AM

SNSequate: Standard and Nonstandard Statistical Models and Methods for Test Equating

Vol. 59, Issue 7, Sep 2014

Abstract:

Equating is a family of statistical models and methods that are used to adjust scores on two or more versions of a test, so that the scores from different tests may be used interchangeably. In this paper we present the R package SNSequate which implements both standard and nonstandard statistical models and methods for test equating. The package construction was motivated by the need of having a modular, simple, yet comprehensive, and general software that carries out traditional and new equating methods. SNSequate currently implements the traditional mean, linear and equipercentile equating methods, as well as the mean-mean, mean-sigma, Haebara and Stocking-Lord item response theory linking methods. It also supports the newest methods such as local equating, kernel equating, and item response theory parameter linking methods based on asymmetric item characteristic functions. Practical examples are given to illustrate the capabilities of the software. A list of other programs for equating is presented, highlighting the main differences between them. Future directions for the package are also discussed.

September 12, 2014 07:00 AM

phtt: Panel Data Analysis with Heterogeneous Time Trends in R

Vol. 59, Issue 6, Sep 2014

Abstract:

The R package phtt provides estimation procedures for panel data with large dimensions n, T, and general forms of unobservable heterogeneous effects. Particularly, the estimation procedures are those of Bai (2009) and Kneip, Sickles, and Song (2012), which complement one another very well: both models assume the unobservable heterogeneous effects to have a factor structure. Kneip et al. (2012) considers the case in which the time-varying common factors have relatively smooth patterns including strongly positively auto-correlated stationary as well as non-stationary factors, whereas the method of Bai (2009) focuses on stochastic bounded factors such as ARMA processes. Additionally, the phtt package provides a wide range of dimensionality criteria in order to estimate the number of the unobserved factors simultaneously with the remaining model parameters.

September 12, 2014 07:00 AM

September 11, 2014

Removed CRANberries

Package GENLIB (with last version 1.0) was removed from CRAN

Previous versions (as known to CRANberries) which should be available via the Archive link are:

2014-09-11 1.0

September 11, 2014 11:13 PM

CRANberries

New package lefse with initial version 0.1

Package: lefse
Type: Package
Title: Phylogenetic and Functional Analyses for Ecology
Version: 0.1
Date: 2014-09-04
Author: Nathan G. Swenson
Maintainer: Nathan G. Swenson
Description: Utilizing phylogenetic and functional information for the analyses of ecological datasets. The analyses include methods for quantifying the phylogenetic and functional diversity of assemblages.
License: GPL-2 | GPL-3
Depends: R (>= 2.10)
Imports: ape, picante, geiger, fBasics, SDMTools, vegan
Packaged: 2014-09-05 10:37:55 UTC; nathanswenson
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-09-11 21:03:26

More information about lefse at CRAN

September 11, 2014 07:13 PM

New package GENLIB with initial version 1.0

Package: GENLIB
Type: Package
Title: Genealogical Data Analysis
Version: 1.0
Date: 2012-04-04
Author: Louis Houde, Jean-Francois Lefebvre, Valery Roy-Lagace, Sebastien Lemieux
Maintainer: Marie-Helene Roy-Gagnon
Description: Takes genealogical data frames and calculates different demographic variables such as genetic contribution, kinship, etc...
License: GPL (>= 2)
LazyLoad: yes
Depends: R (>= 3.0), Rcpp (>= 0.9.10)
Imports: kinship2, methods, bootstrap, Matrix, lattice, quadprog, foreach, parallel, doParallel
LinkingTo: Rcpp
Packaged: 2014-09-11 16:31:06 UTC; sainte-justine\lefjea01
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2014-09-11 21:02:19

More information about GENLIB at CRAN

September 11, 2014 07:13 PM

Dirk Eddelbuettel

pkgKitten 0.1.2: Still creating R Packages that purr

A brown bag release 0.1.2 of pkgKitten is now on CRAN, following yesterday's 0.1.1 upload

Next time I'll try to remember that when I have parameters name and path, it won't work so well to call them as path and name ...

Changes in version 0.1.2 (2014-09-11)

  • Brown-bag fix of calling the new helper function with then correct order of arguments.

More details about the package are at the pkgKitten webpage and the pkgKitten GitHub repo.

Courtesy of CRANberries, there is also a diffstat report for this release

This post by Dirk Eddelbuettel originated on his Thinking inside the box blog. Please report excessive re-aggregation in third-party for-profit settings.

September 11, 2014 02:41 PM

CRANberries

New package gapmap with initial version 0.0.1

Package: gapmap
Type: Package
Title: Functions for Drawing Gapped Cluster Heatmap with ggplot2
Version: 0.0.1
URL: https://bitbucket.org/biovizleuven/gapmap/
Date: 2014-09-10
Author: Ryo Sakai
Maintainer: Ryo Sakai
Description: The gap encodes the distance between clusters and improves interpretation of cluster heatmaps. The gaps can be of the same distance based on a height threshold to cut the dendrogram. Another option is to vary the size of gaps based on the distance between clusters.
License: GPL-2 | GPL-3
Depends: ggplot2, grid, reshape2
Suggests: knitr, dendsort
VignetteBuilder: knitr
Packaged: 2014-09-11 10:21:51 UTC; Ryo
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-09-11 13:06:15

More information about gapmap at CRAN

September 11, 2014 01:13 PM

New package FRESA.CAD with initial version 1.0

Package: FRESA.CAD
Type: Package
Title: FeatuRE Selection Algorithms for Computer Aided Diagnosis
Version: 1.0
Date: 2014-09-10
Author: Jose Gerardo Tamez-Pena
Maintainer: Jose Gerardo Tamez-Pena
Description: FRESA.CAD provides a set of functions and feature selection algorithms for building Computer Aided Diagnosis Models
License: LGPL (>= 2)
Depends: R (>= 3.0.0),Hmisc,pROC,stringr,miscTools,survival
Suggests: class, cvTools, glmnet, speedglm, gplots, RColorBrewer, nlme, rpart
Packaged: 2014-09-11 12:24:47 UTC; Jose Tamez
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-09-11 15:08:50

More information about FRESA.CAD at CRAN

September 11, 2014 01:13 PM

New package lcmm with initial version 1.6.6

Package: lcmm
Type: Package
Title: Estimation of Extended Mixed Models Using Latent Classes and Latent Processes
Version: 1.6.6
Date: 2014-09-10
Author: Cecile Proust-Lima, Viviane Philipps, Amadou Diakite and Benoit Liquet
Maintainer: Cecile Proust-Lima
Description: Functions for the estimation of various extensions of the mixed models including latent class mixed models, joint latent latent class mixed models and mixed models for curvilinear univariate or multivariate longitudinal outcomes using a maximum likelihood estimation method.
License: GPL (>= 2.0)
Depends: R (>= 2.9.0), survival
LazyLoad: yes
Packaged: 2014-09-10 09:08:05 UTC; vp3
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2014-09-11 11:39:40

More information about lcmm at CRAN

September 11, 2014 11:13 AM

New package gdsfmt with initial version 1.1.0

Package: gdsfmt
Type: Package
Title: R Interface to CoreArray Genomic Data Structure (GDS) files
Version: 1.1.0
Date: 2014-09-10
Depends: R (>= 2.14.0)
Imports: methods
Suggests: parallel, RUnit
Author: Xiuwen Zheng
Maintainer: Xiuwen Zheng
BugReports: http://github.com/zhengxwen/gdsfmt/issues
Description: This package provides a high-level R interface to CoreArray Genomic Data Structure (GDS) data files, which are portable across platforms and include hierarchical structure to store multiple scalable array-oriented data sets with metadata information. It is suited for large-scale datasets, especially for data which are much larger than the available random-access memory. The gdsfmt package offers the efficient operations specifically designed for integers with less than 8 bits, since a single genetic/genomic variant, such like single-nucleotide polymorphism, usually occupies fewer bits than a byte. Data compression and decompression are also supported. It is allowed to read a GDS file in parallel with multiple R processes supported by the parallel package.
License: LGPL-3
URL: http://corearray.sourceforge.net/, http://github.com/zhengxwen/gdsfmt
Packaged: 2014-09-11 06:43:57 UTC; sts
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2014-09-11 12:33:04

More information about gdsfmt at CRAN

September 11, 2014 11:13 AM

New package recosystem with initial version 0.2.3

Package: recosystem
Type: Package
Title: Recommender System using Matrix Factorization
Version: 0.2.3
Date: 2014-09-08
Authors@R: c(person(given = "Yixuan", family = "Qiu", role = c("aut", "cre"), email = "yixuan.qiu@cos.name"), person(given = "Chih-Jen", family = "Lin", role = c("aut","cph"), comment = "original C++ code"), person(given = "Yu-Chin", family = "Juan", role = c("aut","cph"), comment = "original C++ code"), person(given = "Yong", family = "Zhuang", role = c("aut","cph"), comment = "original C++ code"), person(given = "Wei-Sheng", family = "Chin", role = c("aut","cph"), comment = "original C++ code"))
Maintainer: Yixuan Qiu
Description: This package is an R wrapper of the libmf library (http://www.csie.ntu.edu.tw/~cjlin/libmf/) for recommender system using matrix factorization. It is typically used to approximate an incomplete matrix using the product of two matrices in a latent space. Other common names for this task include "collaborative filtering", "matrix completion", "matrix recovery", etc.
License: BSD_3_clause + file LICENSE
Copyright: see file COPYRIGHTS
URL: https://github.com/yixuan/recosystem
BugReports: https://github.com/yixuan/recosystem/issues
SystemRequirements: C++11
Depends: methods
Imports: Rcpp (>= 0.11.0)
Suggests: knitr
LinkingTo: Rcpp
VignetteBuilder: knitr
Packaged: 2014-09-08 15:06:23 UTC; qyx
Author: Yixuan Qiu [aut, cre], Chih-Jen Lin [aut, cph] (original C++ code), Yu-Chin Juan [aut, cph] (original C++ code), Yong Zhuang [aut, cph] (original C++ code), Wei-Sheng Chin [aut, cph] (original C++ code)
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2014-09-11 10:00:26

More information about recosystem at CRAN

September 11, 2014 09:13 AM

New package rChoiceDialogs with initial version 1.0.6

Package: rChoiceDialogs
Type: Package
Title: rChoiceDialogs collection
Version: 1.0.6
Date: 2014-09-05
Authors@R: c(person("Alex","Lisovich",role=c("aut","cre"),email="alex.lisovich@gmail.com"), person("Roger","Day",role="aut",email="day01@pitt.edu"), person("Sun Microsystems, Inc",role="cph", comment="swing-layout-1.0.4.jar, download from http://www.java2s.com/Code/Jar/s/Downloadswinglayout104jar.htm"))
Description: Collection of portable choice dialog widgets
License: LGPL (>= 2.1)
Depends: rJava, utils
Imports: tcltk
Collate: 'zzz.R' 'rChoiceDialogs-package.R' 'rFileChooser.R' 'rListChooser.R' 'rMiscFunctions.R'
Packaged: 2014-09-10 20:49:54 UTC; alex
Author: Alex Lisovich [aut, cre], Roger Day [aut], Sun Microsystems, Inc [cph] (swing-layout-1.0.4.jar, download from http://www.java2s.com/Code/Jar/s/Downloadswinglayout104jar.htm)
Maintainer: Alex Lisovich
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-09-11 07:26:55

More information about rChoiceDialogs at CRAN

September 11, 2014 07:13 AM

New package OjaNP with initial version 0.9-8

Package: OjaNP
Type: Package
Title: Multivariate Methods Based on the Oja Median and Related Concepts
Version: 0.9-8
Date: 2014-09-10
Author: Daniel Fischer, Jyrki Möttönen, Klaus Nordhausen, Daniel Vogel
Maintainer: Klaus Nordhausen
Depends: R (>= 2.9.0), ICS, ICSNP
Description: The package provides functions for the Oja median, Oja signs and ranks and methods based upon them.
License: GPL (>= 2)
Encoding: latin1
LazyLoad: yes
Packaged: 2014-09-10 09:19:18 UTC; klaus
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2014-09-11 08:48:28

More information about OjaNP at CRAN

September 11, 2014 07:13 AM

New package funFEM with initial version 1.0

Package: funFEM
Type: Package
Title: Clustering in the Discriminative Functional Subspace
Version: 1.0
Date: 2014-09-06
Author: Charles Bouveyron
Depends: R (>= 2.10), MASS, fda, elasticnet
Maintainer: Charles Bouveyron
Description: The funFEM algorithm (Bouveyron et al., 2014) allows to cluster functional data by modeling the curves within a common and discriminative functional subspace.
License: GPL-2
LazyLoad: yes
Packaged: 2014-09-10 07:52:48 UTC; charles
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-09-11 07:36:22

More information about funFEM at CRAN

September 11, 2014 07:13 AM

Dirk Eddelbuettel

pkgKitten 0.1.1: Still creating R Packages that purr

A maintenance release 0.1.1 of pkgKitten is now on CRAN.

It has only one small change: the function playWithPerPackageHelpPage() was factored out of the main function kitten() as I happened to be needing something just like playWithPerPackageHelpPage() to make packages created by the Rcpp function Rcpp.package.skeleton() a little nicer.

We also added a NEWS.Rd file which restates major release features. As it is so short, we include it in its entirety.

Changes in version 0.1.1 (2014-09-09)

  • New (exported) function playWithPerPackageHelpPage() which lets other packages create a non-complaint-generating package help page

Changes in version 0.1.0 (2014-06-13)

  • Initial public version and CRAN upload

More details about the package are at the pkgKitten webpage and the pkgKitten GitHub repo.

Courtesy of CRANberries, there is also a diffstat report for this release

This post by Dirk Eddelbuettel originated on his Thinking inside the box blog. Please report excessive re-aggregation in third-party for-profit settings.

September 11, 2014 01:12 AM

September 10, 2014

CRANberries

New package saery with initial version 1.0

Package: saery
Type: Package
Title: Small Area Estimation for Rao and Yu Model
Version: 1.0
Date: 2014-09-10
Author: Maria Dolores Esteban Lefler, Domingo Morales Gonzalez, Agustin Perez Martin
Maintainer: Agustin Perez Martin
Description: A complete set of functions to calculate several EBLUP (Empirical Best Linear Unbiased Predictor) estimators and their mean squared errors. All estimators are based on an area-level linear mixed model introduced by Rao and Yu in 1994 (see documentation). The REML method is used for fitting this model.
License: GPL-2
LazyData: true
Packaged: 2014-09-10 20:48:45 UTC; agus
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-09-10 23:18:02

More information about saery at CRAN

September 10, 2014 11:13 PM

New package Ohmage with initial version 2.11-4

Package: Ohmage
Type: Package
Title: R Client for Ohmage 2 server
Version: 2.11-4
Date: 2014-09-10
Author: Jeroen Ooms
Maintainer: Jeroen Ooms
Description: R Client for Ohmage 2 server. Implements basic R functions to retrieve and process data.
License: GPL
Depends: R (>= 2.14), stats, methods
Imports: RCurl, RJSONIO
Suggests: XML
Packaged: 2014-09-10 13:00:58 UTC; jeroen
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-09-10 15:14:12

More information about Ohmage at CRAN

September 10, 2014 03:13 PM

New package ForwardSearch with initial version 1.0

Package: ForwardSearch
Type: Package
Title: Forward Search using asymptotic theory
Version: 1.0
Date: 2014-09-10
Author: Bent Nielsen
Maintainer: Bent Nielsen
URL: http://users.ox.ac.uk/~nuff0078/
Description: Forward Search analysis of time series regressions. Implements the asymptotic theory developed in Johansen and Nielsen (2013, 2014).
License: GPL-3
Depends: robustbase
Packaged: 2014-09-10 11:19:57 UTC; Nielsen
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-09-10 13:56:09

More information about ForwardSearch at CRAN

September 10, 2014 01:13 PM

New package RImpala with initial version 0.1.4

Package: RImpala
Version: 0.1.4
Date: 2014-09-10
Title: R and Impala
Author: Vijay Raajaa, Austin Chungath Vincent, Sachin Sudarshana, Vikas Raguttahalli
Maintainer: Vijay Raajaa
Contact: Austin Chungath Vincent ,Vikas Raguttahalli , Sachin Sudarshana
Description: RImpala facilitates the connection and execution of distributed queries using Cloudera Impala, which is a massively parallel processing (MPP) SQL query engine that runs natively in Apache Hadoop. Impala supports jdbc integration which RImpala utilizes to establish the connection between R and Impala.
Depends: R (>= 2.7.0), rJava (>= 0.5-0)
SystemRequirements: Java (>= 1.5)
License: GPL-3
Packaged: 2014-09-10 08:47:33 UTC; root
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-09-10 11:43:20

More information about RImpala at CRAN

September 10, 2014 11:13 AM

New package RDML with initial version 0.4-2

Package: RDML
Type: Package
Title: Importing real-time thermo cycler (qPCR) data from RDML format files
Version: 0.4-2
LazyData: true
Date: 2014-09-10
Description: Imports real-time thermo cycler (qPCR) data from RDML format files and transforms to the appropriate formats of the 'qpcR' and 'chipPCR' packages.
Authors@R: c( person("Konstantin A.", "Blagodatskikh", email = "k.blag@yandex.ru", role = c("cre", "aut")), person("Stefan", "Roediger", email = "stefan.roedigier@hs-lausitz.de", role = "aut"), person("Michal", "Burdukiewicz", email = "michalburdukiewicz@gmail.com", role = "aut"))
License: GPL (>= 2)
URL: https://github.com/kablag/RDML
Depends: XML (>= 3.98-1.1)
Imports: chipPCR (>= 0.0.8-1)
Suggests: qpcR (>= 1.3-9)
Packaged: 2014-09-10 08:55:14 UTC; kablag
Author: Konstantin A. Blagodatskikh [cre, aut], Stefan Roediger [aut], Michal Burdukiewicz [aut]
Maintainer: Konstantin A. Blagodatskikh
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-09-10 11:38:16

More information about RDML at CRAN

September 10, 2014 11:13 AM

Removed CRANberries

Package bstats (with last version 1.1-11-5) was removed from CRAN

Previous versions (as known to CRANberries) which should be available via the Archive link are:

2014-05-04 1.1-11-5
2014-01-23 1.1-11-4
2013-11-26 1.1-11-1
2013-09-08 1.1-9-8
2013-08-07 1.1-8-8
2011-12-04 1.0-12-3
2011-11-18 1.0-11-17
2011-11-15 1.0-2
2011-11-10 1.0-1
2011-11-02 0.01-4

September 10, 2014 07:13 AM

September 09, 2014

CRANberries

New package vcrpart with initial version 0.2-1

Package: vcrpart
Type: Package
Title: Tree-Based Varying Coefficient Regression for Generalized Linear and Ordinal Mixed Models
Version: 0.2-1
Date: 2014-09-10
Authors@R: c( person("Reto", "Buergin", role = c("aut", "cre", "cph"), email = "rbuergin@gmx.ch"), person("Gilbert", "Ritschard", role = c("ctb", "ths"), email = "gilbert.ritschard@unige.ch"))
Maintainer: Reto Buergin
Description: Recursive partitioning algorithm for varying coefficient generalized linear models and ordinal 2-stage linear mixed models. Special features are coefficient-wise partitioning, non-varying coefficients and partitioning of time-varying variables in longitudinal ordinal regression.
License: GPL (>= 2)
Depends: R (>= 3.1.0), parallel, partykit
Imports: stats, grid, graphics, methods, nlme, rpart, numDeriv, ucminf, zoo, sandwich, strucchange
LazyLoad: yes
NeedsCompilation: yes
Packaged: 2014-09-09 22:27:25 UTC; rbuergin
Author: Reto Buergin [aut, cre, cph], Gilbert Ritschard [ctb, ths]
Repository: CRAN
Date/Publication: 2014-09-10 00:51:30

More information about vcrpart at CRAN

September 09, 2014 11:13 PM

New package eegkitdata with initial version 1.0

Package: eegkitdata
Type: Package
Title: Data for package eegkit
Version: 1.0
Date: 2014-09-09
Author: Nathaniel E. Helwig
Maintainer: Nathaniel E. Helwig
Description: Contains the example EEG data used in the package eegkit. Also contains code for easily creating larger EEG datasets from the EEG Database on the UCI Machine Learning Repository.
License: GPL (>= 2)
Packaged: 2014-09-09 17:33:17 UTC; Nate
Depends: R (>= 2.10)
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-09-10 00:27:03

More information about eegkitdata at CRAN

September 09, 2014 11:13 PM

New package eegkit with initial version 1.0-0

Package: eegkit
Type: Package
Title: Toolkit for electroencephalography data
Version: 1.0-0
Date: 2014-09-09
Author: Nathaniel E. Helwig
Maintainer: Nathaniel E. Helwig
Depends: R (>= 3.1.1), bigsplines, eegkitdata, ica, rgl
Description: Analysis and visualization tools for electroencephalography (EEG) data. Includes functions for plotting (a) EEG caps, (b) single- and multi-channel EEG time courses, and (c) EEG spatial maps. Also includes smoothing and Independent Component Analysis functions for EEG data analysis.
License: GPL (>= 2)
Packaged: 2014-09-09 17:42:07 UTC; Nate
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-09-10 00:55:52

More information about eegkit at CRAN

September 09, 2014 11:13 PM

New package cmvnorm with initial version 1.0

Package: cmvnorm
Type: Package
Title: Complex multivariate Gaussian distribution
Version: 1.0
Date: 2014-01-10
Author: Robin K. S. Hankin
Depends: emulator (>= 1.2-15)
Imports: elliptic
Maintainer: Robin K. S. Hankin
Description: various utilities for the complex multivariate Gaussian distribution
VignetteBuilder: elliptic, emulator
License: GPL-2
Packaged: 2014-09-09 20:37:37 UTC; rhankin
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-09-10 00:24:00

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September 09, 2014 11:13 PM

New package micropan with initial version 1.0

Package: micropan
Type: Package
Title: Microbial Pan-genome Analysis
Version: 1.0
Date: 2014-09-03
Author: Lars Snipen and Kristian Hovde Liland
Maintainer: Lars Snipen
Description: A collection of functions for computations and visualizations of microbial pan-genomes.
Depends: R (>= 2.15.0), igraph
License: GPL-2
LazyData: FALSE
ZipData: TRUE
Packaged: 2014-09-09 09:30:39 UTC; larssn
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-09-09 14:18:33

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September 09, 2014 01:13 PM

New package couchDB with initial version 1.3.0

Package: couchDB
Type: Package
Title: Connect and work with couchDB databases
Version: 1.3.0
Date: 2014-02-25
Author: Aleksander Dietrichson
Maintainer: Aleksander Dietrichson
Depends: R (>= 3.0.0), RCurl, bitops, httr, rjson
Description: Interface to couchDB
SystemRequirements: couchDB instance to connect to and work on.
License: AGPL-3
Packaged: 2014-09-08 17:58:38 UTC; sasha
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-09-09 07:24:25

More information about couchDB at CRAN

September 09, 2014 07:13 AM

Alstatr

Translation Invariant of Lebesgue Outer Measure

Another proving problem, this time on Real Analysis.

Problem

  1. Prove that the Lebesgue outer measure is translation invariant. (Use the property that, the length of an interval $l$ is translation invariant.)

Solution

  1. Proof. The outer measure is translation invariant if for $y\in \mathbb{R}$, \begin{equation}\nonumber \mu^{*}(A)=\mu^{*}(A+y) \end{equation} Hence, we need to show that Case 1: $\mu^{*}(A)\leq \mu^{*}(A+y)$; and Case 2: $\mu^{*}(A+y)\leq \mu^{*}(A)$.

    Case 1: Consider a countable collection $\{I_n\}_{n=1}^{\infty}$, and let \begin{equation}\nonumber W = \left\{\displaystyle\sum_{n=1}^{\infty}l(I_n)\mid A\subseteq\displaystyle\bigcup_{n=1}^{\infty}I_n\right\} \end{equation} Then the outer measure of $A$ is, \begin{equation}\nonumber \mu^{*}(A)=\inf\,\{W\}. \end{equation} Now consider $x\in W$, then there is a particular collection $\hat{I}_n$ that covers $A$, such that $\displaystyle\sum_{n=1}^{\infty}l(\hat{I}_n)=x$, and that of course is the $\inf\,\{W\}$. Further, we see that the collection $\{\hat{I}_n+y\}$ covers $A+y$, that is, $A+y\subseteq \displaystyle\bigcup_{n=1}^{\infty}\{\hat{I}_n + y\}$. And from this, we obtain the following outer measure: \begin{equation}\nonumber \begin{aligned} \mu^{*}(A+y)&=\displaystyle\sum_{n=1}^{\infty}l(\hat{I}_n+y)\\ &=\displaystyle\sum_{n=1}^{\infty}l(\hat{I}_n),\;\text{since}\;l\;\text{is translation invariant}.\\ &=x. \end{aligned} \end{equation} And therefore, $W\subseteq\left\{\displaystyle\sum_{n=1}^{\infty}I_n\mid A+y\subseteq \displaystyle\bigcup_{n=1}^{\infty}I_n\right\}$, implying $\mu^{*}(A)\leq \mu^{*}(A+y)$.

    Case 2: Using the same flow of reasoning as in Case 1, consider a countable collection $\{I_n\}_{n=1}^{\infty}$, and let \begin{equation}\nonumber V = \left\{\displaystyle\sum_{n=1}^{\infty}l(I_n)\mid A+y\subseteq\displaystyle\bigcup_{n=1}^{\infty}I_n\right\} \end{equation} Then the outer measure of $A$ is, \begin{equation}\nonumber \mu^{*}(A+y)=\inf\,\{V\}. \end{equation} Now consider $x\in V$, then there is a particular collection $\hat{I}_n$ that covers $A+y$, such that $\displaystyle\sum_{n=1}^{\infty}l(\hat{I}_n)=x$, and that of course is the $\inf\,\{V\}$. Further, we see that the collection $\{\hat{I}_n+(-y)\}$ covers $A$, that is, $A\subseteq \displaystyle\bigcup_{n=1}^{\infty}\{\hat{I}_n + (-y)\}$. And from this, we obtain the following outer measure: \begin{equation}\nonumber \begin{aligned} \mu^{*}(A)&=\displaystyle\sum_{n=1}^{\infty}l(\hat{I}_n+(-y))\\ &=\displaystyle\sum_{n=1}^{\infty}l(\hat{I}_n),\;\text{since}\;l\;\text{is translation invariant}.\\ &=x. \end{aligned} \end{equation} And therefore, $V\subseteq\left\{\displaystyle\sum_{n=1}^{\infty}I_n\mid A\subseteq \displaystyle\bigcup_{n=1}^{\infty}I_n\right\}$, implying $\mu^{*}(A+y)\leq \mu^{*}(A)$.

    Since we have shown both cases, then $\mu^{*}(A)=\mu^{*}(A+y).\hspace{3.7cm}\blacksquare$

Reference

  1. Royden, H.L. and Fitzpatrick, P.M. (2010). Real Analysis. Pearson Education, Inc.

by Al-Ahmadgaid Asaad (noreply@blogger.com) at September 09, 2014 12:20 AM

Lebesgue Measure and Outer Measure Problems

More proving, still on Real Analysis. This is my solution and if you find any errors, do let me know.

Problems

Lebesgue Measure: Let $\mu$ be set function defined for all set in $\sigma$-algebra $\mathscr{F}$ with values in $[0,\infty]$. Assume $\mu$ is countably additive over countable disjoint collections of sets in $\mathscr{F}$.
  1. Prove that if $A$ and $B$ are two sets in $\mathscr{F}$, with $A\subseteq B$, then $\mu(A)\leq \mu(B)$. This property is called monotonicity.
  2. Prove that if there is a set $A$ in the collection $\mathscr{F}$ for which $\mu(A)<\infty$, then $\mu(\emptyset)=0$.
  3. Let $\{E_{k}\}_{k=1}^{\infty}$ be a countable collection of sets in $\mathscr{F}$. Prove that $\mu\left(\displaystyle\bigcup_{k=1}^{\infty}E_{k}\right)\leq \displaystyle\sum_{k=1}^{\infty}\mu(E_k)$
Lebesgue Outer Measure:
  1. By using property of outer measure, prove that the interval $[0,1]$ is not countable.
  2. Let $A$ be the set of irrational numbers in the interval $[0,1]$. Prove that $\mu^{*}(A)=1$.
  3. Let $B$ be the set of rational numbers in the interval $[0,1]$, and let $\{I_k\}_{k=1}^{n}$ be finite collection of open intervals that covers $B$. Prove that $\displaystyle\sum_{k=1}^{n}\mu^{*}(I_k)\geq 1$.
  4. Prove that if $\mu^{*}(A)=0$, then $\mu^{*}(A\cup B)=\mu^{*}(B).$

Solutions

  1. Proof. If $A\subseteq B$, then $A\subseteq A\cup (B\cap A^c)\Rightarrow A\subseteq A\cup (B\backslash A)$. Thus, \begin{equation}\nonumber \begin{aligned} \mu(A)&\leq \mu(A)+\mu(B\backslash A)\\ &\leq \mu(A)+\mu(B)-\mu(A)\\ &\leq \mu(B) \end{aligned} \end{equation} $\hspace{13.5cm}\blacksquare$
  2. Proof. For any set $A$, $\emptyset=A\cap A^c=A\backslash A$. Then, \begin{equation}\nonumber \begin{aligned} \mu(\emptyset)&=\mu(A\backslash A)=\mu(A)-\mu(A)\\ &=l(A)-l(A)=0. \end{aligned} \end{equation} $\hspace{13.5cm}\blacksquare$
  3. Proof. Since $\{E_k\}_{k=1}^{\infty}$ is a countable collection, then it can either be disjoint or not. If disjoint then by countable additivity property, \begin{equation}\nonumber \mu\left(\displaystyle\bigcup_{k=1}^{\infty}E_k\right)=\displaystyle\sum_{k=1}^{\infty}\mu(E_k). \end{equation} And that's not what we want. So we will consider the sequence $\{E_k\}$ to be non-disjoint sets. Because $\{E_k\}$ is increasing, then we can write this as \begin{equation}\nonumber \begin{aligned} \displaystyle\bigcup_{k=1}^{\infty}E_k&=E_1\cup (E_2\cap E_1^c)\cup (E_3\cap E_2^{c})\cup\cdots\subseteq E_1\cup E_2\cup E_3\cup\cdots\\ \mu\left(\displaystyle\bigcup_{k=1}^{\infty}E_k\right)&=\mu(E_1)+\mu(E_2\backslash E_1)+\mu(E_3\backslash E_2)+\cdots\\ &\leq \mu(E_1)+ \mu(E_2)+ \mu(E_3)+\cdots\\ \mu\left(\displaystyle\bigcup_{k=1}^{\infty}E_k\right)&=\mu(E_1)+\displaystyle\sum_{k=2}^{\infty}\mu(E_k-E_{k-1})\leq \displaystyle\sum_{k=1}^{\infty}\mu(E_k)\\ \mu\left(\displaystyle\bigcup_{k=1}^{\infty}E_k\right)&\leq \displaystyle\sum_{k=1}^{\infty}\mu(E_k) \end{aligned} \end{equation}$\hspace{13.5cm}\blacksquare$
  4. Proof. Let's prove this by contradiction, assume the interval $[0,1]$ is countable. Then we need to show that $\mu^{*}([0,1])=0$ for it to be countable. Now consider $\varepsilon >0$, such that $I = \{[\varepsilon - 0, 1 + \varepsilon]\}$ covers $[0,1]$. Then by property of outer measure that says, $\mu^{*}([a,b])$ is the length of $[a,b]$, we have \begin{equation}\nonumber \mu^{*}([0,1]) = \inf\,\{l(I)\} = (1+\varepsilon) - (0-\varepsilon) = 1+2\varepsilon \end{equation} This holds for each $\varepsilon >0$, thus $\mu^{*}([0,1])=1$ which is a contradiction.$\hspace{2.13cm}\blacksquare$
  5. Proof. If $\{A\cap [0,1]\}$ is the set of irrational numbers in the interval $[0,1]$, then $\{A^{c}\cap [0,1]\}$ is the set of rational numbers in the interval $[0,1]$. Now consider the following, \begin{equation}\nonumber \begin{aligned} \mu^{*}([0,1])&=\mu^{*}(A\cap [0,1])+\mu^{*}(A^{c}\cap [0,1])\\ \mu^{*}(A\cap [0,1])&=\mu^{*}([0,1]) - \mu^{*}(A^{c}\cap [0,1])\\ &=1 -\mu^{*}(A^{c}\cap [0,1]) \end{aligned} \end{equation} We need to show that $\mu^{*}(A^{c}\cap [0,1])$ has outer measure zero. To do that, let $a_1,a_2,\cdots\in \{A^{c}\cap [0,1]\}$. Since $\forall$ singleton $a_i\in \{A^{c}\cap [0,1]\}$, $\mu^{*}(a_i) = 0$, $i=1,2,\cdots$. Hence, \begin{equation} \nonumber \mu^{*}(A^c\cap [0,1])=\mu^{*}\left(\displaystyle\bigcup_{i=1}^{\infty}a_i\right)\leq\displaystyle\sum_{i=1}^{\infty}\mu^{*}(a_i)=\displaystyle\sum_{i=1}^{\infty}l(a_i)=0. \end{equation} Thus, $\mu^{*}([0,1])=1-0=1$.$\hspace{8.45cm}\blacksquare$
  6. Proof. It follows that $\displaystyle\bigcup_{k=1}^{n}I_k\supseteq \{B\cap [0,1]\}$. Now consider $x\in \{B\cap [0,1]\}$, then $x$ has least value of 0 and max of 1, since these are rational numbers. Thus the collection $\{I_k\}$ should at least include these points in order to cover $\{B\cap [0,1]\}$. Thus, \begin{equation}\nonumber \mu^{*}([0,1])=1\leq \mu^{*}\left(\displaystyle\bigcup_{k=1}^{n}I_k\right), \end{equation} by finite subadditivity of Lebesgue outer measure, \begin{equation}\nonumber 1\leq \mu^{*}\left(\displaystyle\bigcup_{k=1}^{n}I_k\right)\leq \displaystyle\sum_{k=1}^{n}\mu^{*}(I_k). \end{equation} $\hspace{13.5cm}\blacksquare$
  7. Proof. $\mu^{*}(A)=0$ if $A$ is countable. Consider $A=\emptyset$ and $B$ be nonempty set, then $\mu^{*}(A)=\mu^{*}(\emptyset)=0$. Then $A\cup B=\{\emptyset, B\}=\{B\}$, implies \begin{equation}\nonumber \mu^{*}(A\cup B)=\mu^{*}(B). \end{equation} $\hspace{13.5cm}\blacksquare$

Reference

  1. Royden, H.L. and Fitzpatrick, P.M. (2010). Real Analysis. Pearson Education, Inc.

by Al-Ahmadgaid Asaad (noreply@blogger.com) at September 09, 2014 12:20 AM

September 08, 2014

CRANberries

New package tlm with initial version 0.1.2

Package: tlm
Type: Package
Title: Effects under Linear, Logistic and Poisson Regression Models with Transformed Variables.
Version: 0.1.2
Date: 2014-09-08
Author: Jose Barrera-Gomez and Xavier Basagana
Maintainer: Jose Barrera-Gomez
Depends: R (>= 3.0.1), stats, utils, boot
Description: Computation of effects under linear, logistic and Poisson regression models with transformed variables. Logarithm and power transformations are allowed. Effects can be displayed both numerically and graphically in both the original and the transformed space of the variables.
License: GPL (>= 2)
Packaged: 2014-09-08 16:48:10 UTC; jbarrera
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-09-08 19:23:03

More information about tlm at CRAN

September 08, 2014 07:13 PM

New package funHDDC with initial version 1.0

Package: funHDDC
Type: Package
Title: Model-based clustering in group-specific functional subspaces
Version: 1.0
Date: 2014-09-06
Author: C. Bouveyron & J. Jacques
Maintainer: Charles Bouveyron
Depends: fda
Description: The package provides the funHDDC algorithm (Bouveyron & Jacques, 2011) which allows to cluster functional data by modeling each group within a specific functional subspace.
License: GPL-2
LazyLoad: yes
Packaged: 2014-09-08 14:12:24 UTC; charles
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-09-08 19:15:54

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September 08, 2014 07:13 PM

New package slackr with initial version 1.2

Package: slackr
Type: Package
Title: Send messages, images, R objects and files to Slack.com channels/users
Version: 1.2
Date: 2014-09-08
Author: Bob Rudis (@hrbrmstr) & Jay Jacobs (@jayjacobs)
Maintainer: Bob Rudis
Description: Slackr contains functions that make it possible to interact with Slack.com messaging platform. When you need to share information/data from R, rather than resort to copy/paste in e-mails or other services like Skype, you can use this package to send well-formatted output from multiple R objects and expressions to all teammates at the same time with little effort. You can also send images from the current graphics device, R objects (as RData), and upload files.
URL: http://github.com/hrbrmstr/slackr
BugReports: https://github.com/hrbrmstr/slackr/issues
License: MIT + file LICENSE
Suggests: testthat
Depends: R (>= 3.0.0), httr (>= 0.4.0), jsonlite, data.table (>= 1.9.2), ggplot2
Packaged: 2014-09-08 11:02:54 UTC; bob
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-09-08 13:31:26

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September 08, 2014 01:13 PM

New package lunar with initial version 0.1-04

Package: lunar
Type: Package
Title: Lunar Phase & Distance, Seasons and Other Environmental Factors
Author: Emmanuel Lazaridis [aut, cre]
Maintainer: Emmanuel Lazaridis
Depends: R (>= 2.10.0)
Description: Provides functions to calculate lunar and other environmental covariates.
License: MIT + file LICENSE
Encoding: UTF-8
LazyLoad: no
URL: http://statistics.lazaridis.eu
Authors@R: c(person(given = "Emmanuel", family = "Lazaridis", email="emmanuel@lazaridis.eu", role = c("aut", "cre")))
Version: 0.1-04
Date: 2014-09-04
Packaged: 2014-09-04 14:50:09 UTC; james
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-09-08 10:59:14

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September 08, 2014 09:13 AM

September 07, 2014

CRANberries

New package PerfMeas with initial version 1.2.1

Package: PerfMeas
Type: Package
Title: PerfMeas: Performance Measures for ranking and classification tasks
Version: 1.2.1
Date: 2014-09-07
Author: Giorgio Valentini, Matteo Re -- Universita' degli Studi di Milano
Maintainer: Giorgio Valentini
Description: Package that implements different performance measures for classification and ranking tasks. AUC, precision at a given recall, F-score for single and multiple classes are available.
License: GPL (>= 2)
LazyLoad: yes
Depends: limma, graph, RBGL
Packaged: 2014-09-07 09:09:55 UTC; valenti
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2014-09-07 11:28:42

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September 07, 2014 11:13 AM

New package bionetdata with initial version 1.0.1

Package: bionetdata
Type: Package
Title: Biological and chemical data networks
Version: 1.0.1
Date: 2014-09-07
Author: Matteo Re, Giorgio Valentini -- Universita' degli Studi di Milano
Maintainer: Matteo Re
Description: Data Package that includes several examples of chemical and biological data networks, i.e. data graph structured.
License: GPL (>= 2)
LazyLoad: yes
Depends: R (>= 2.10)
Packaged: 2014-09-07 09:28:57 UTC; valenti
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-09-07 13:10:15

More information about bionetdata at CRAN

September 07, 2014 11:13 AM