|
delta.true()
|
Calculate true interaction effects in the simulation study |
|
diff_reward()
|
Calculate the difference between rewards (outcomes) |
|
evf()
|
Calculate empirical value functions |
|
mlearn.wsvm.cv()
|
Fit a nested cross-validation of weighted kernel support vector
machines (SVMs) |
|
mlearn.wsvm()
|
Fit weighted kernel support vector machines (SVMs) |
|
mlearn.wsvm.tune()
|
Fit cross-validated weighted kernel support vector machines (SVMs) |
|
mu.true()
|
Calculate true main effects in the simulation study |
|
pi.true()
|
Calculate true propensity scores in the simulation study |
|
rfcv2()
|
Customized function for training random forest |
|
shift()
|
Lead/lag for vectors and lists |
|
simulate_data()
|
Generate a dataset for estimating individualized treatment rules |
|
summarize_rec()
|
Summarize recommendations for multicategory treatment setup |