NEWS
rollout 0.2.0.9000
rollout 0.2.0
- New function
add_binomial_outcome() generalizes add_binary_outcome() to support a binomial outcome with more than one trial. size (the number of trials, i.e. the denominator) can be a fixed value or the name of an existing column, so a per-row size that varies — e.g. a Poisson-distributed eligible count generated upstream with dplyr::mutate() — can be piped straight in: add_binomial_outcome(size = n_eligible, ...). add_binary_outcome() is now a size = 1 special case of this function.
add_binary_outcome() and add_binomial_outcome() both gain an include_error argument (default FALSE). By default, a .error column (if present) is no longer included in the summed linear predictor for these two outcomes, since a Bernoulli/binomial outcome's variance is already implied by p (and size). Set include_error = TRUE to restore the previous behavior of summing every .-prefixed column.- Breaking change:
add_binary_outcome()'s default output column is now named "y_binary" (previously "y_bin").
- The
term argument of the eval_*() helpers (eval_bias(), eval_greater_than(), eval_less_than(), eval_between(), and eval_quantile()) can now reference grouping variables, so true values, thresholds, bounds, and probabilities may vary across simulated parameter conditions — e.g., eval_bias(estimate, term = c(conditionimpl = beta)) when results are grouped by beta. Elements of term that do not resolve to a single value per group (for example, ones referencing a column that was not included in group_by()) now abort with guidance instead of silently returning NA.
eval_less_than() now correctly computes the proportion of values below zero when term = NULL; previously it computed the proportion above zero. Error messages in eval_greater_than() and eval_less_than() also now reference the correct function names.- New function
eval_coverage() computes confidence interval coverage across simulation replicates, i.e. the proportion of replicates whose interval [lower, upper] contains the true value for each term. It is designed to be called inside evaluate_model_results() alongside the other eval_*() helpers, e.g. eval_coverage(term = c(conditionintv = b_intv)) when results are grouped by b_intv and term. lower and upper default to the conf.low and conf.high columns created by extract_model_results(tidy_fun = \(m) broom.mixed::tidy(m, conf.int = TRUE)). When term = NULL, coverage is computed for a true value of zero.
rollout 0.1.0 (2026-01-13)