Provides a data.table backend for 'dplyr'. The goal of
    'dtplyr' is to allow you to write 'dplyr' code that is automatically
    translated to the equivalent, but usually much faster, data.table
    code.

Track and
    document 'dplyr' data pipelines. As you filter, mutate, and join your
    way through a data set, 'dtrackr' seamlessly keeps track of your data
    flow and makes publication ready documentation of a data pipeline simple.

Methods to estimate the optimal treatment regime among all linear regimes via smoothed estimation methods, and construct element-wise confidence intervals for the optimal linear treatment regime vector, as well as the confidence interval for the optimal value via wild bootstrap procedures, if the population follows treatments recommended by the optimal linear regime. See more details in: Wu, Y. and Wang, L. (2021), "Resampling-based Confidence Intervals for Model-free Robust Inference on Optimal Treatment Regimes", Biometrics, 77: 465– 476, <doi:10.1111/biom.13337>.

We provide a comprehensive software to estimate general K-stage DTRs from SMARTs with Q-learning and a variety of outcome-weighted learning methods. Penalizations are allowed for variable selection and model regularization. With the outcome-weighted learning scheme, different loss functions - SVM hinge loss, SVM ramp loss, binomial deviance loss, and L2 loss - are adopted to solve the weighted classification problem at each stage; augmentation in the outcomes is allowed to improve efficiency. The estimated DTR can be easily applied to a new sample for individualized treatment recommendations or DTR evaluation.

Dynamic treatment regime estimation and inference via G-estimation, 
  dynamic weighted ordinary least squares (dWOLS) and Q-learning. Inference via 
  bootstrap and recursive sandwich estimation. Estimation and 
  inference for survival outcomes via Dynamic Weighted Survival Modeling (DWSurv). 
  Extension to continuous treatment variables. Wallace et al. (2017) 
  <DOI:10.18637/jss.v080.i02>; Simoneau et al. (2020) 
  <DOI:10.1080/00949655.2020.1793341>.