bigDM

Implements several spatial and spatio-temporal scalable disease mapping models for high-dimensional count data using the INLA technique for approximate Bayesian inference in latent Gaussian models (Orozco-Acosta et al., 2021 doi:10.1016/j.spasta.2021.100496; Orozco-Acosta et al., 2023 doi:10.1016/j.cmpb.2023.107403 and Vicente et al., 2023 doi:10.1007/s11222-023-10263-x). The creation and develpment of this package has been supported by Project MTM2017-82553-R (AEI/FEDER, UE) and Project PID2020-113125RB-I00/MCIN/AEI/10.13039/501100011033. It has also been partially funded by the Public University of Navarra (project PJUPNA2001).

Last 30 days

This package has been downloaded 461 times in the last 30 days. Enough downloads to make a small wave in the niche community. The curiosity is spreading! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 25 times.

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

This package has been downloaded 4,652 times in the last 365 days. That's enough downloads to impress a room full of undergrads. A commendable achievement indeed. The day with the most downloads was Mar 28, 2025 with 74 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.