# tehtuner

# tehtuner 0.3.0

Adds support for classification trees in Step 2 by setting
`step2 = 'classtree'`

with a given threshold of
`threshold`

.

Adds the `print.tunevt`

method.

# tehtuner 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.

# tehtuner 0.2.0

Adds the `parallel`

option to `tunevt`

to
support parallel backends.

# tehtuner 0.1.1

This patch reconciles an invalid URI in the `tunevt`

documentation’s references.

# tehtuner 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.