funGp

Construction and smart selection of Gaussian process models for analysis of computer experiments with emphasis on treatment of functional inputs that are regularly sampled. This package offers: (i) flexible modeling of functional-input regression problems through the fairly general Gaussian process model; (ii) built-in dimension reduction for functional inputs; (iii) heuristic optimization of the structural parameters of the model (e.g., active inputs, kernel function, type of distance). An in-depth tutorial in the use of funGp is provided in Betancourt et al. (2024) doi:10.18637/jss.v109.i05 and Metamodeling background is provided in Betancourt et al. (2020) doi:10.1016/j.ress.2020.106870. The algorithm for structural parameter optimization is described in https://hal.science/hal-02532713.

Last 30 days

This package has been downloaded 430 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 10 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 6,061 times in the last 365 days. That's a lot of interest! Someone might even write a blog post about it. The day with the most downloads was Jul 21, 2024 with 139 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.