Changes in version 0.3.3                        

Fixes a bug when using step1 = "lasso" where columns were being subset
incorrectly when creating the design matrix for glmnet::cv.glmnet.

Fixes a bug when using step1 = "lasso" where factor variables were
treated as numeric. Factors are appropriately now converted into
indicator variables.

                        Changes in version 0.3.2                        

Minor documentation updates following JOSS publication.

                        Changes in version 0.3.1                        

Release corresponding to JOSS paper. Adds automated testing for Step 1
wrapper functions (e.g., vt1_lasso) and get_mnpp.

                 Changes in version 0.3.0 (2023-04-01)                  

Adds support for classification trees in Step 2 by setting step2 =
'classtree' with a given threshold of threshold.

Adds the print.tunevt method.

                        Changes in version 0.2.1                        

Fixes a bug where zbar was calculated using the mean difference in the
first column of the data instead of using the location of the variable
Y.

                        Changes in version 0.2.0                        

Adds the parallel option to tunevt to support parallel backends.

                 Changes in version 0.1.1 (2022-11-07)                  

This patch reconciles an invalid URI in the tunevt documentation's
references.

                        Changes in version 0.1.0                        

This is a new package that implements the Virtual Twins algorithm for
subgroup identification (Foster et al., 2011) while controlling the
probability of falsely detecting differential treatment effects when the
conditional treatment effect is constant across the population of
interest. These methods were originally presented in Wolf et al. (2022).

References

  - Foster, J. C., Taylor, J. M., & Ruberg, S. J. (2011). Subgroup
    identification from randomized clinical trial data. Statistics in
    Medicine, 30(24), 2867–2880. https://doi.org/10.1002/sim.4322

  - Wolf, J. M., Koopmeiners, J. S., & Vock, D. M. (2022). A permutation
    procedure to detect heterogeneous treatment effects in randomized
    clinical trials while controlling the type-I error rate. Clinical
    Trials. https://doi.org/10.1177/17407745221095855

Key function

  - tunevt() fits a Virtual Twins model using user-specified Step 1 and
    Step 2 models with parameter selection to control the probability of
    a false discovery.