Changelog • rifttable

rifttable 0.7.2

CRAN release: 2026-01-18

Bug fixes:
- survdiff_ci() now uses the conf.level argument correctly in confidence interval estimation using the MOVER approach (#8).
Internal:
- Anticipate changes in {dplyr} 1.2.0 in label handling in unit tests (thanks to @DavisVaughan, #10).

CRAN release: 2025-06-06

Breaking changes:
- Require base R pipe |> and thus R >= 4.1.
- Make the id variable identifying clustered observations within the same individual a global rifttable() option for the entire data set, not only for specific estimators.
New functionality:
- Expand input checks to missing values in time/event variables, to missing effect modifiers for joint models and their levels, to nonexistent custom estimators, and to empty input data sets.
- Add type = "sum" estimator.
Internal and bug fixes:
- Cover entire package by unit tests.
- Return exposure consistently as a character.
- Let the design accept weight in addition to weights.
- Require {risks} >= 0.4.3.
- Use modern tidyselect, anonymous functions, and code style.

New functionality:
- Provide easier interface to code competing events by directly providing event = "event_variable@Event_Type_One" in the design. If multiple event types are present, estimate cumulative incidence and differences/ratios of cumulative incidence in a competing-event setting.
- Allow for clustered observations in survival data, e.g., multiple rows per person.
- Estimate ratios of survival and cumulative incidence, i.e., x-year risk ratios. Use MOVER estimation for confidence intervals of both ratios and differences in survival and cumulative incidence by default.
- Support weights, e.g., inverse-probability weights, directly via a weights argument in the design. Weighted estimates are currently available for many survival estimators: type = "cuminc", "surv", their differences and ratios (e.g., "cumincdiff"), and "hr". This is a breaking change for Cox models (type = "hr"), where providing weights in the arguments list now generates an error.
Bug fixes:
- Allow for @ in factor levels for a table1_design().
- rt_gt(): Output knitr-formatted tables for GitHub-flavored markdown also in Quarto .qmd, similar to .Rmd.
- Better handling of edge cases, e.g., ratios of 0, when rounding estimates.
Expanded documentation
- Restructure site.
- Separate documentation of estimators by outcome type.
- New FAQs on confidence levels, reference levels, custom functions, and joint models.

New functionality:
- Add overall argument exposure_levels to let user control handling of missing exposure levels (NA) or factors with empty levels as the exposure.
- type = "geomean" for geometric means.
Documentation: Expand FAQs.
Internal and bug fixes:
- Consider exposure or trend of "" as missing, and stratum = "" as no subsetting by the effect_modifier, instead of subsetting to effect modifier being an empty string. Input check that a stratum must be provided for joint models and strata are not empty.
- Consider missing type as "blank".
- Do not add empty rows/columns if type2 has empty results for some cells or if only a trend variable and no exposure is given.
- Rounding works even if result vector contains strings (e.g., no estimate).
- More safeguards for all-NA outcome variables. More input checks.
- Do not warn about non-0/1 outcomes in log-linear models for ratios of continuous variables.
- Add initial set of unit tests.

New functionality:
- Cox models (type = "hr") allow for weights, clustering, and robust standard errors.
- Argument ratio_digits_decrease: By default, decrease number of decimal digits shown for ratios by 1 digit for ratios > 3 and by 2 digits for ratios > 10. Leads to rounded ratios and confidence intervals of 1.23, 3.4, and 11.
- rt_gt() now indents the first column and applies markdown formatting to it by default.
New FAQ vignette.
Bug fixes:
- Binary outcomes returned NA instead of 0 in unstratified tables with all-null outcome.
- type = "maxfu" ignored digits and diff_digits.
- Allow for different exposure (strata labels) and arguments in one table.
- Show unstratified estimate if exposure is "", not just for NA.
Internal:
- rt_gt(): suppress random id of gt tables to keep git diff slim.
- Keep variables .event, .outcome, etc. available under their original names.
- Require {risks} >= 0.4.2.
- Examples load the breastcancer dataset from the risks package.

Breaking changes:
- to is set to ", " by default, instead of "-" for ratio variables and " to " for difference variables
- Custom functions are now directly called via the type variable, following a restructuring of all estimation functions with greater flexibility.
- design$type no longer accepts additional arguments, such as time points. Supply list instead via design$arguments.
- Suppression of strata with sparse or re-identifiable data with design$nmin now differentiates between counts of total observations or outcomes, depending on estimator.
New function table1_design(): Generate design of a descriptive “Table 1.”
New outcome option "variable@level" for categorical variables that displays level as a binary outcome. Used by table1_design().
Support unstratified tables displaying the trend/linear slope (trend variable in the design) without an exposure.
More customization:
- rifttable(reference = ...): Label for the reference category.
- design$ci: Width of confidence intervals.
- design$na_rm: Omitting observations with missing outcome data.
- design$arguments: Flexibly passing along any argument to estimation functions.
New vignette describing all estimators.
Internal:
- Drop dependency on R >= 4.1 and native pipe.
- Require {risks} >= 0.4.0.
- Remove dependency on {labelled} package.
- The {gt} and {quantreg} packages are now optional as soft dependencies.
- Compatible with {dplyr} 1.1.0, {tidyselect} 1.2.0

khsmisc::table2() “graduated” into its own package. See {khsmisc} Changelog for earlier versions.
Add breastcancer() dataset
Use R >= 4.1 native pipe, |>
Remove RMTL estimators