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(2013) <arXiv:1606.03658>.
Imports: ggplot2, sp, e1071, graphics, grDevices, stats, utils,Rdpack,

Title: Irish Companies' Boards from 2003 to 2013
Authors@R: person(given = "Riccardo", family = "Rastelli", role = c("aut", "cre"), email = "riccardo.rastelli@ucdconnect.ie")
Description: This data package contains the boards' compositions of companies quoted in the Irish Stock Exchange at the end of each year from 2003 to 2013. The data have been first analysed in Friel, N., Rastelli, R., Wyse, J. and Raftery, A.E. (2016) <DOI:10.1073/pnas.1606295113>.

Packaged: 2017-08-02 10:31:22 UTC; rrastelli

Title: A Dynamic Bipartite Latent Space Model to Analyse Irish
Description: Provides the dataset and an implementation of the method illustrated in Friel, N., Rastelli, R., Wyse, J. and Raftery, A.E. (2016) <DOI:10.1073/pnas.1606295113>.

Title: Analysis of Longitudinal Data with Irregular Observation Times
Description: Analysis of longitudinal data for which the times of observation are random variables that are potentially associated with the outcome process. The package includes inverse-intensity weighting methods (Lin H, Scharfstein DO, Rosenheck RA (2004) <doi:10.1111/j.1467-9868.2004.b5543.x>) and multiple outputation (Pullenayegum EM (2016) <doi:10.1002/sim.6829>).
Packaged: 2019-02-05 01:13:03 UTC; Eleanor Pullenayegum