Authors: | Andrew O. Finley, Sudipto Banerjee, Alan E. Gelfand |
Title: | [download] (479)spBayes for Large Univariate and Multivariate Point-Referenced Spatio-Temporal Data Models |
Reference: | Vol. 63, Issue 13, Feb 2015Submitted 2013-06-03, Accepted 2014-09-04 |
Type: | Article |
Abstract: | In this paper we detail the reformulation and rewrite of core functions in the spBayes R package. These efforts have focused on improving computational efficiency, flexibility, and usability for point-referenced data models. Attention is given to algorithm and computing developments that result in improved sampler convergence rate and efficiency by reducing parameter space; decreased sampler run-time by avoiding expensive matrix computations, and; increased scalability to large datasets by implementing a class of predictive process models that attempt to overcome computational hurdles by representing spatial processes in terms of lower-dimensional realizations. Beyond these general computational improvements for existing model functions, we detail new functions for modeling data indexed in both space and time. These new functions implement a class of dynamic spatio-temporal models for settings where space is viewed as continuous and time is taken as discrete. |
Paper: | [download] (479)spBayes for Large Univariate and Multivariate Point-Referenced Spatio-Temporal Data Models (application/pdf, 2.9 MB) |
Supplements: | [download] (31)spBayes_0.3-9.tar.gz: R source package (application/x-gzip, 480.6 KB) |
[download] (34)v63i13.R: R example code from the paper (application/octet-stream, 14 KB) | |
Resources: | BibTeX | OAI |