neuralGAM

Neural network framework based on Generalized Additive Models from Hastie & Tibshirani (1990, ISBN:9780412343902), which trains a different neural network to estimate the contribution of each feature to the response variable. The networks are trained independently leveraging the local scoring and backfitting algorithms to ensure that the Generalized Additive Model converges and it is additive. The resultant Neural Network is a highly accurate and interpretable deep learning model, which can be used for high-risk AI practices where decision-making should be based on accountable and interpretable algorithms.

Last 30 days

This package has been downloaded 22,705 times in the last 30 days. That's enough downloads to make it mildly famous in niche technical communities. A badge of honor! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 724 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 180,059 times in the last 365 days. This is the kind of download count that makes grant committees nod approvingly. A job well done, even the stoic reviewers might be impressed! The day with the most downloads was Nov 23, 2024 with 912 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.