Boosted Multivariate Trees for Longitudinal Data
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Prediction for Boosted multivariate trees for longitudinal data.
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Boosted multivariate trees for longitudinal data
Boosted multivariate trees for longitudinal data.
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Partial plot analysis
Simulate longitudinal data
Variable Importance
Variable Importance (VIMP) plot
Implements Friedman's gradient descent boosting algorithm for modeling longitudinal response using multivariate tree base learners. Longitudinal response could be continuous, binary, nominal or ordinal. A time-covariate interaction effect is modeled using penalized B-splines (P-splines) with estimated adaptive smoothing parameter. Although the package is design for longitudinal data, it can handle cross-sectional data as well. Implementation details are provided in Pande et al. (2017), Mach Learn <DOI:10.1007/s10994-016-5597-1>.