steppedwedge 1.1.0

analyze() supports the Delayed Constant Treatment (DCT) model.
analyze() supports cluster-level random treatment effects via re = "tx".
analyze() can adjusts for covariates passed via load_data(covariates = ).
analyze() supports robust variance estimation via params(var_est = "robust").
load_data() accepts an exposure_time variable (in addition to automatically calculating exposure time when only a binary treatment variable is passed).
plot_effect_curves() visualizes estimated treatment effect curves.
plot_clusters() visualizes cluster-level data or fitted analysis objects.

The offset argument of analyze() has moved to load_data(). Passing offset through params() is deprecated.
load_data() now stores time_type as an attribute rather than a dataframe column. Access it with attr(data, "time_type").
params(return_ncs=TRUE) returns the natural cubic spline transformation matrix and the transformed coefficients and covariance matrices.
Documentation and vignettes include examples for binary outcomes, DCT models, and cluster plots.
The binomial outcome dataset is now sw_data_example_binom.
For spline-based model components, the number of knots can be configured via params(), including the exposure time spline of the NCS model (n_knots_exp) and the calendar time trend spline (n_knots_cal).
Fix to how estimand_time is coded in the TEH model.
An informative error is thrown if the user tries to use a robust variance estimator with a TEH model.
print() formats and displays estimates, standard errors, and p-values for analysis objects.

steppedwedge 1.0.0