Man pages for jitonglou/MultiMlearn
Statistical Learning Methods for Optimizing Individualized Treatment Rules
| 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 | Fit weighted kernel support vector machines (SVMs) |
| mlearn.wsvm.cv | Fit a nested cross-validation of weighted kernel support... |
| mlearn.wsvm.tune | Fit cross-validated weighted kernel support vector machines... |
| 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... |
| summarize_rec | Summarize recommendations for multicategory treatment setup |
