Package index

Sample size calculation

Plan fixed negative binomial designs for recurrent-event outcomes, with Wald or score-test sizing.

sample_size_nbinom()

Sample size calculation for negative binomial outcomes

print(<sample_size_nbinom_result>)

Print method for sample_size_nbinom_result objects

summary(<sample_size_nbinom_result>)

Summary for sample_size_nbinom_result objects

print(<sample_size_nbinom_summary>)

Print method for sample_size_nbinom_summary objects

Simulation

Simulate recurrent-event data, seasonal intensity patterns, and NB monitoring or SSR scenarios.

nb_sim()

Simulate recurrent events with fixed follow-up

nb_sim_seasonal()

Simulate recurrent events with seasonal rates

sim_gs_nbinom()

Simulate group sequential clinical trial for negative binomial outcomes

sim_ssr_nbinom()

Simulate adaptive group sequential trials with sample size re-estimation

check_gs_bound()

Check group sequential bounds

summarize_gs_sim()

Summarize group sequential simulation results

summarize_ssr_sim()

Summarize adaptive SSR simulation results

Analysis

Analyze interim negative binomial data with Wald or score tests and estimate information for monitoring or SSR.

blinded_ssr()

Blinded sample size re-estimation for recurrent events

unblinded_ssr()

Unblinded sample size re-estimation for recurrent events

calculate_blinded_info()

Calculate blinded statistical information

estimate_nb_mom()

Method of Moments Estimation for Negative Binomial Parameters

mutze_test() print(<mutze_test>)

Wald or score test for treatment effect using negative binomial model

cut_data_by_date()

Cut simulated trial data at a calendar date

cut_completers()

Cut data for completers analysis

cut_date_for_completers()

Find calendar date for target completer count

compute_info_at_time()

Compute statistical information at analysis time

get_analysis_date()

Find calendar date for target event count

get_cut_date()

Determine analysis date based on criteria

Group sequential design

Extend negative binomial designs to group sequential monitoring via gsDesign boundaries.

gsNBCalendar()

Group sequential design for negative binomial outcomes

update_gsNB()

Update group sequential bounds with observed information

toInteger()

Convert group sequential design to integer sample sizes

summary(<gsNB>)

Summary for gsNB objects

print(<gsNBsummary>)

Print method for gsNBsummary objects

Multiple imputation

Impute missing longitudinal negative binomial counts under MAR, reference-based MNAR, and composite ICE strategies.

impute_nb()

Multiple imputation for longitudinal negative binomial counts

fit_nb_glmm()

Fit a negative binomial GLMM for count imputation

impute_nb_mar()

Impute missing counts under Missing at Random (MAR)

impute_nb_mnar_ref()

Impute missing counts under a reference-based MNAR assumption

impute_nb_composite()

Apply the composite ICE strategy: replace post-ICE outcomes with baseline

Documentation site

Preview the pkgdown website locally over HTTP (avoids unstyled file:// pages).

preview_pkgdown_site()

Preview built pkgdown site in the browser

Interactive prototype

Launch the optional Shiny explorer for adaptive negative binomial SSR scenarios.

run_ssr_shiny()

Launch the SSR Shiny prototype

gsDesign re-exports

Spending functions and utilities re-exported from the gsDesign package for convenience.

reexports gsDesign sfBetaDist sfCauchy sfExponential sfExtremeValue sfExtremeValue2 sfGapped sfHSD sfLDOF sfLDPocock sfLinear sfLogistic sfNormal sfPoints sfPower sfStep sfTDist sfTrimmed sfTruncated sfXG1 sfXG2 sfXG3 gsBoundSummary

Objects exported from other packages