DIFM

Bayesian factor models are effective tools for dimension reduction. This is especially applicable to multivariate large-scale datasets. It allows researchers to understand the latent factors of the data which are the linear or non-linear combination of the variables. Dynamic Intrinsic Conditional Autocorrelative Priors (ICAR) Spatiotemporal Factor Models 'DIFM' package provides function to run Markov Chain Monte Carlo (MCMC), evaluation methods and visual plots from Shin and Ferreira (2023)[https://doi.org/10.1016%2Fj.spasta.2023.100763]. Our method is a class of Bayesian factor model which can account for spatial and temporal correlations. By incorporating these correlations, the model can capture specific behaviors and provide predictions.

Last 30 days

This package has been downloaded 170 times in the last 30 days. Now we're getting somewhere! Enough downloads to populate a lively group chat. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 6 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 2,105 times in the last 365 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. The day with the most downloads was Apr 14, 2024 with 46 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.