gsDesignNB 0.2.6

gsDesignNB 0.2.5

Added rr0 parameter to sample_size_nbinom() and blinded_ssr() to support non-inferiority and super-superiority testing.
Changed default event_gap to 0 in nb_sim().

Fix cut_date_for_completers() to support nb_sim_seasonal() output (no tte column).
Correct calculate_blinded_info() blinded information calculation to use subject-level exposure.

Fix toInteger.gsNB() to avoid unintended power changes by correctly recomputing information with max_followup, preserving delta1, and improving ratio-aware integer rounding.
Vignette updates and documentation fixes.

sample_size_nbinom() computes sample size or power for fixed designs with two treatment groups. Supports piecewise accrual, exponential dropout, maximum follow-up, and event gaps. Implements the Zhu and Lakkis (2014) and Friede and Schmidli (2010) methods.

gsNBCalendar() creates group sequential designs for negative binomial outcomes, optionally attaching calendar-time analysis schedules (via analysis_times) compatible with gsDesign. Inherits from both gsDesign and sample_size_nbinom_result classes.
compute_info_at_time() computes statistical information for the log rate ratio at a given analysis time, accounting for staggered enrollment.
toInteger() rounds sample sizes in a group sequential design to integers while respecting the randomization ratio.

nb_sim() simulates recurrent events for trials with piecewise constant enrollment, exponential failure rates, and piecewise exponential dropout. Supports negative binomial overdispersion via gamma frailty and event gaps.
nb_sim_seasonal() simulates recurrent events where event rates vary by season (Spring, Summer, Fall, Winter).
Group sequential simulation helpers: sim_gs_nbinom() runs repeated simulations with flexible cut rules via get_cut_date(), check_gs_bound() updates spending bounds based on observed information, and summarize_gs_sim() summarizes operating characteristics across analyses.

cut_data_by_date() censors follow-up at a specified calendar time and aggregates events per subject, adjusting for event gaps.
get_analysis_date() finds the calendar time at which a target event count is reached.
cut_completers() subsets data to subjects randomized by a specified date.
cut_date_for_completers() finds the calendar time at which a target number of subjects have completed their follow-up.

mutze_test() fits a negative binomial (or Poisson) log-rate model and performs a Wald test for the treatment effect, following Mütze et al. (2019).

blinded_ssr() estimates blinded dispersion and event rate from interim data and re-calculates sample size to maintain power, following Friede and Schmidli (2010).
calculate_blinded_info() estimates blinded statistical information for the log rate ratio from aggregated interim data.

Re-exports gsDesign(), gsBoundSummary(), and common spending functions (sfHSD(), sfLDOF(), sfLDPocock(), and more) for convenience.