# Generalized Boosted Regression Models

## Documentation for package ‘gbm3’ version 3.0.1

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Code demos. Use demo() to run them.
- Package README

## Help Pages

available_distributions | Available Distributions
baseline_hazard | Baseline hazard function
calibrate_plot | Calibration plot
create_dist | Create distributions
distribution_name | What is the distribution name used here?
gbm | Generalized Boosted Regression Modeling
gbm.fit | Generalized Boosted Regression Modeling
gbm.perf | GBMT Performance
gbmParallel | Control parallelization options
gbmt | GBMT
gbmt_fit | GBMT fit
gbmt_performance | Get performance details for gbm fit
gbm_conc | Compute Information Retrieval measures.
gbm_dist | GBM Distribution
gbm_more | Perform additional boosting
gbm_object | Generalized Boosted Regression Model Object
gbm_roc_area | Compute Information Retrieval measures.
interact | Estimate the strength of interaction effects
interact.GBMFit | Estimate the strength of interaction effects
iteration_error | Extract errors from GBMFit objects
perf_pairwise | Performance for pairwise
permutation_relative_influence | Relative influence via permutation
plot.GBMFit | Marginal plots of fitted gbm objects
plot.GBMTPerformance | Plot GBM performance details
predict.GBMCVFit | Predictions for CV fitted GBM models
predict.GBMFit | Predict method for GBM Model Fits
pretty_gbm_tree | Print gbm tree components
print | Print model summary
print.GBMFit | Print model summary
quantile_rug | Quantile rug plot
relative_influence | Methods for estimating relative influence
summary.GBMFit | Summary of a GBMFit object
to_old_gbm | Convert GBMFit to previous gbm object
training_params | Training parameters
trees | Extract trees from GBMFit objects