plsmselect
Linear and Smooth Predictor Modelling with Penalisation and Variable Selection
Fit a model with potentially many linear and smooth predictors. Interaction effects can also be quantified. Variable selection is done using penalisation. For l1-type penalties we use iterative steps alternating between using linear predictors (lasso) and smooth predictors (generalised additive model).
- Version0.2.0
- R versionunknown
- LicenseGPL-2
- Needs compilation?No
- Last release11/24/2019
Documentation
Team
Indrayudh Ghosal
Matthias Kormaksson
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Insights
Last 30 days
This package has been downloaded 145 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 2 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 1,885 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 Sep 11, 2024 with 29 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.
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Dependencies
- Imports4 packages
- Suggests4 packages