confoundvis

• rir_lm() computes the robustness of inference to replacement (RIR; Frank et al., 2013) from a fitted linear model: the threshold for significance, the percent of the estimate that would have to be due to bias, the number of cases that would have to be replaced with null-effect cases, and the corresponding path (estimate after replacing a proportion lambda of the cases). Results agree with konfound::konfound(index = "RIR") and reproduce the worked example of Frank et al.'s (2025) practical guide. A threshold argument allows a substantive effect threshold in place of statistical significance. from_konfound() gains index = "RIR" to import konfound's RIR output as a path.
• ITCV benchmarks following Frank et al.'s (2025) practical guide: itcv_lm() returns benchmark_corr_product (R_XZ * R_YZ, the impact of all covariates together) and itcv_ratio_to_benchmark (eq. 12), and covariate_impacts() adds each term's zero-order correlations, their product impact_zero, and itcv_ratio, the unconditional ITCV divided by that impact (eq. 10).
• sens_surface() returns the two-parameter sensitivity surface of which a path is one slice, and plot_sensitivity_surface() draws it with shaded zones, the null and significance boundaries, and optional benchmarks (sensemakr formal bounds are added automatically). Surfaces are available for partial R2 (Cinelli and Hazlett, 2020), ITCV (exact boundaries), E-values (joint bounding factor of Ding and VanderWeele, 2016), and Rosenbaum bounds (amplification of Rosenbaum and Silber, 2009, agreeing with sensitivitymv::amplify()). Boundaries are computed in closed form or by one-dimensional root finding.
• sens_table() collects several confoundsens objects into one table: framework, meaning of the sensitivity parameter, the framework's own summary statistic, null and significance crossings, and the rule used. Rows keep their own scales; the printed table says so.
• plot_sensitivity_contour() gains an itcv argument. Given the output of itcv_lm(), it draws, in closed form, the exact boundaries at which the magnitude of the confounder-adjusted partial correlation equals the critical value: in the original direction (solid) and in the reversed direction (dotted). It shades the region where the estimate is no longer significant, the region where it becomes significant with the opposite sign, and correlation pairs that cannot occur (non-positive-definite correlation matrix). The product curve is kept as a dashed reference. threshold may now be omitted when itcv is supplied, and a new limits argument sets the axis range.
• Benchmark labels in plot_sensitivity_contour() repel one another when **ggrepel** is installed (suggested), instead of overlapping.

Bug fixes

• itcv_lm(): for weighted fits, the R-squared values used for the unconditional ITCV were computed around an unweighted mean. They now equal the weighted R-squared reported by summary.lm(). Unweighted fits are unaffected.
• sens_report() no longer points to covariate_impacts() for frameworks that it does not benchmark (E-values, Rosenbaum bounds).
• robustness_points() now takes the direction of the original conclusion from the stored threshold when there is one. Paths that start on the wrong side of their threshold are therefore reported as not significant at the first grid value instead of returning NA. Results for paths produced by earlier versions are unchanged.

Other changes

• **DOS2** and **sensitivity2x2xk** are suggested for the cross-checks in the test suite.
• The workflow vignette adds the exact contour, a matched-pair example, and sens_table().