Packaged: 2017-05-11 09:06:55 UTC; Alejandro

Packaged: 2017-09-19 20:45:35 UTC; Alejandro

Packaged: 2017-12-02 18:54:29 UTC; Alejandro

Packaged: 2017-12-06 15:59:07 UTC; Alejandro

Description: This package describes the associated cost games to inventory situations.
Packaged: 2014-06-10 20:28:17 UTC; Alejandro

Packaged: 2014-06-14 10:44:42 UTC; Alejandro

Packaged: 2014-06-24 18:11:46 UTC; Alejandro

Description: Provides a scaling method to obtain a standardized Moran's I measure. Moran's I is a measure for the spatial autocorrelation of a data set, it gives a measure of similarity between data and its surrounding. Researchers calculate Moran's I to express the spatial autocorrelation of their data. The range of this value must be [-1,1], but this does not happen in practice. This package scale the Moran's I value and map it into the theoretical range of [-1,1]. Once the Moran's I value is rescaled, it facilitates the comparison between projects, for instance, a researcher can calculate Moran's I in a city in China, with a sample size of n1 and area of interest a1. Another researcher runs a similar experiment in a city in Mexico with different sample size, n2, and an area of interest a2. Due to the differences between the conditions, it is not possible to compare Moran's I in a straightforward way. In this version of the package, the spatial autocorrelation Moran's I is calculated as proposed in Chen(2009) <arXiv:1606.03658>.

Author: Ivan Fuentes, Thomas DeWitt, Thomas Ioerger, Michael Bishop
Description: Provides a scaling method to obtain a standardized Moran's I measure. Moran's I is a measure for the spatial autocorrelation of a data set, it gives a measure of similarity between data and its surrounding. The range of this value must be [-1,1], but this does not happen in practice. This package scale the Moran's I value and map it into the theoretical range of [-1,1]. Once the Moran's I value is rescaled, it facilitates the comparison between projects, for instance, a researcher can calculate Moran's I in a city in China, with a sample size of n1 and area of interest a1. Another researcher runs a similar experiment in a city in Mexico with different sample size, n2, and an area of interest a2. Due to the differences between the conditions, it is not possible to compare Moran's I in a straightforward way. In this version of the package, the spatial autocorrelation Moran's I is calculated as proposed in Chen(2009) <arXiv:1606.03658>.
Imports: Rcpp, ggplot2, sp, e1071, graphics, grDevices, stats,