NEWS.md
In tgerke/risks: Estimate risk ratios and risk differences using regression

risks 0.4.1

tidy.risks(): Increase default bootrepeats to 1000, consistent with summary().

risks 0.4.0

Breaking change: For consistency, the default option for model fitting (approach = "auto") now always uses marginal standardization after fitting a logistic model. The previous approach, which relied on different models fitted, is available as approach = "legacy".
If requesting approach = "margstd_delta" in presence of interaction terms involving the exposure variable, a warning is displayed. Model fitting with "auto" uses the bootstrap (i.e., "margstd_boot") in that case.
approach = "margstd_boot" bug fix: Keep categorical exposures of type factor in the correct order.
Include breastcancer dataset in the package.
Internal changes:
{addreg} and {logbin} are now soft dependencies (Suggests: instead of Imports:)
Remove {lifecycle} dependency
Compatibility with tidyselect 1.2.0 variable selection

risks 0.3.0

Breaking changes:
Rename approach = "glm_start" to "glm_startp" (for Poisson).
Rename approach = "margstd" to "margstd_boot".
For consistency with other approaches, no longer treat numeric variables with only two levels (e.g., 1 and 2) as categorical in approach = "margstd_boot".
New estimators:
approach = "margstd_delta", marginal standardization after fitting a logistic model with standard errors via the delta method.
approach = "margstd_boot" now also implements average marginal effects to handle continuous exposures.
approach = "duplicate", the case duplication method for risk ratios, proposed by Miettinen, with cluster-robust standard errors proposed by Schouten et al.
approach = "glm_startd", using the case duplication-based coefficients as starting values for binomial models.
rr_rd_mantel_haenszel(): New function for comparison purposes.
Changes to parameters:
approach = "auto", the default, now attempts model fitting in this order of priority: approach = "glm"; approach = "glm_startp" (for risk ratios only); approach = "margstd_delta". If all fail, the user is shown the error messages from a plain logistic model.
Bootstrap repeats (bootrepeats) for approach = "margstd_boot" now default to 1000.
Bug fixes:
summary.robpoisson(): Fix sandwich standard errors. tidy() output was correct.
Programming changes:
Do not attach the logbin package to the namespace; export logbin::conv.test() on its behalf. Move MASS package (needed only for testthat) to Suggests.
Remove usage of unexported functions from stats.
For approach = "margstd_boot", avoid two rounds of bootstrap for standard error and confidence intervals separately. Rewrite internal fitting function fit_and_predict(), replacing eststd(). Overall, bootstrapping is more than two times faster now.

risks 0.2.2

tidy(bootverbose = TRUE): For BC~a~ bootstrap confidence intervals, also return jacksd.low and jacksd.high, the jackknife-based Monte-Carlo standard errors for the upper and lower confidence limits.
riskdiff(): Remove leftover "logistic" parameter.
summary.risks(), tidy.risk(): fix error handling if no model converged.

risks 0.2.1

Fix bugs in bootci = "normal" and in summary.risks().
Return name of dataset.
Expand test coverage.

risks 0.2.0

Expand bootstrapping options after marginal standardization:
Parametric BCa bootstrap confidence intervals via the bcaboot package are the new default.
Parametric normality-based intervals are a backup.
Non-parametric bootstrapping with BCa intervals is retained as an option for completeness.
Remove precision weight option.
Expand documentation.