spm
Spatial Predictive Modeling
Introduction to some novel accurate hybrid methods of geostatistical and machine learning methods for spatial predictive modelling. It contains two commonly used geostatistical methods, two machine learning methods, four hybrid methods and two averaging methods. For each method, two functions are provided. One function is for assessing the predictive errors and accuracy of the method based on cross-validation. The other one is for generating spatial predictions using the method. For details please see: Li, J., Potter, A., Huang, Z., Daniell, J. J. and Heap, A. (2010) https:www.ga.gov.au/metadata-gateway/metadata/record/gcat_71407 Li, J., Heap, A. D., Potter, A., Huang, Z. and Daniell, J. (2011) doi:10.1016/j.csr.2011.05.015 Li, J., Heap, A. D., Potter, A. and Daniell, J. (2011) doi:10.1016/j.envsoft.2011.07.004 Li, J., Potter, A., Huang, Z. and Heap, A. (2012) https:www.ga.gov.au/metadata-gateway/metadata/record/74030.
- Version1.2.2
- R version≥ 2.10
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?No
- Last release05/06/2022
Documentation
Team
Jin Li
Insights
Last 30 days
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Last 365 days
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Data provided by CRAN