Changelog

Source: NEWS.md

regsensitivity 0.2.0

The Oster equality identified-set plot draws the actual branches

• plot() on an analysis = "oster", delta_type = "eq" sweep now groups the roots by branch rather than by sort order. Up to three roots come back per delta, sorted within each delta, so the root continuing a given branch moves between the beta1/beta2/beta3 columns wherever a fold adds two roots or one escapes to infinity. Drawing a column as a line cut continuous branches into pieces and joined unrelated ones; a heuristic that broke the line at an asymptote and at delta = 1 suppressed the false connectors but left visible gaps in curves that are not broken.

  Branch identity does not need a heuristic. delta(beta) is a ratio of cubics; between two consecutive poles of it the map is continuous and single valued, so the roots in one pole interval are one branch, and walking that interval in increasing beta traverses it end to end. A branch that folds back is not a function of delta, so the branches are drawn with geom_path().

• A zero of the denominator of delta(beta) that is also a zero of its numerator is a hole, not an asymptote, and no longer splits a branch. With a single control covariate it is the only real root of the denominator, which made that case a single continuous curve reported as two.

• The spurious root – the one solving the cubic at every delta, since it is that common root – no longer survives at scattered delta and draws a stray spur along the asymptote. Its residual is set by how accurately polyroot() locates it, which is well above the few digits of machine epsilon the filter allowed.

• plot() warns when a delta grid is too coarse to draw anything, and errors when Oster’s cubic has no real solution anywhere on it. Passing delta = c(-3, 3) – the endpoints, as Stata’s delta() takes them, rather than the grid – silently produced an empty panel.

The identified set is now computed everywhere

• The region rxbar > rmax(cbar) > rybar no longer raises an error. regsen_bounds() and regsen_breakdown() compute it like any other, and the numbers agree with an independent search over the constraint set of DMP (2026) Theorem 5 to within 0.01, continuously across rmax.

  What stood in the way was the solver for the constraint Assumption A3 places on z. That constraint is a quadratic inequality whose leading coefficient turns negative exactly when rxbar * ||c|| > 1; its solution set is then the complement of an interval, not an interval, and the solver returned the hull of the two pieces. Sampling that hull hands the optimizer selection equations that violate A3, so the region was refused rather than reported. The feasible set is now represented as the list of intervals it is, and sampled in proportion to their lengths.

• The breakdown point in rxbar is no longer capped at rmax(cbar). That ceiling binds only when rybar is unrestricted; with rybar finite the breakdown point can lie well past it. Where no rxbar overturns the conclusion – the horizontal arm of the breakdown frontier – the reported value is now +Inf instead of the ceiling.

• regsen_breakdown() gains direction = "rybar", giving the frontier rybar_bf(rxbar) of DMP (2026) Theorem 4 over a grid of rxbar. This is the form the paper’s Figure 1 plots, and the only one that can describe the frontier’s horizontal arm.

Assumption A6 in its general form

• regsen_bounds() and regsen_breakdown() gain clow, the lower end of DMP Assumption A6, R(W2 ~ W1 . W0) in [clow, cbar]. Asserting that the controls are at least somewhat endogenous tightens the identified set and raises the breakdown point. clow = 0, the default, is the previous behaviour.

Parallel computation

• regsen_bounds(), regsen_breakdown() and regsen_multi() gain an ncores argument, joining regsen_boot(), and all four default to the session setting of the new regsen_cores(). The grid points of an identified set, the values of a breakdown frontier and the treatments of a multi-treatment sweep are independent and are spread across the cores; on the bundled data a finite-rybar frontier runs about three times faster on six cores. Results are identical for any number of cores: nothing but the bootstrap draws random numbers, and it seeds each replicate itself.
• The default stays serial, as CRAN policy requires of a package that has not been asked. regsen_cores("auto") sets the session to all but two of the machine’s cores; regsen_cores(n) sets a number. Work inside a bootstrap replicate or a treatment always runs serially, so cores are not oversubscribed by nesting.
• The socket backend that Windows uses can be selected on any machine with options(regsensitivity.backend = "psock"), and the test suite runs it everywhere, so a change that works under fork but not on Windows is caught before it ships. Arguments a worker closure touches are forced before the closure is built: a socket worker is a fresh session and would otherwise receive an unevaluated promise into a frame it does not have.
• A replicate or grid point that errors is now confined to its own slot. mclapply() preschedules jobs onto cores and marks every job on a core as failed when one errors, so a single bad bootstrap resample used to take a whole core’s worth of replicates down with it.

Inference

• regsen_boot() now reports a bias-corrected and accelerated (BCa) interval alongside the percentile interval, and returns it by default; type = "perc" restores the old choice. The acceleration comes from a delete-one jackknife over the units the bootstrap resamples – rows, or clusters under a cluster bootstrap. In the simulation design of the package’s paper at n = 500, coverage improves from 92.0% to 94.0% against a nominal 95% at the same interval width; the two agree by n = 1000. z0 and acceleration are returned so the correction can be inspected, and an endpoint that falls on an extreme replicate is reported as such.
• Bootstrap replicates are about three times faster on the bundled data, 5 milliseconds against 14. A replicate is now a row subset of model matrices built once, rather than a fresh model.frame() call per draw, which is what makes the BCa jackknife – one computation per row – affordable.

Bug fixes

• rmax, the rxbar at which the identified set becomes unbounded, was wrong for cbar < 1. It solved the cbar branch of DMP appendix equation (S18) unconditionally, when the maximising ||c|| is rxbar itself wherever A6 allows it. With cbar = 0.5 on the bundled data, calibrating against the ten geographic and climate covariates, it reported 1.48 where the set is in fact unbounded from 1.19, so regsen_bounds() printed a large finite number in place of an infinite one across that range, and the default rxbar grid ran past the point where the analysis says anything.
• A breakdown point for an upper-bound hypothesis (beta = bnd_ub(v)) was computed from the lower end of the identified set. With rybar finite the set is not symmetric around beta_med, so the reported value was wrong; the tested end is now chosen by the direction of the hypothesis.
• An equality hypothesis (beta = bnd_eq(v)) now breaks down at whichever end of the identified set can reach the hypothesised value, rather than always at the upper one.
• A name in compare or nocompare that is not a control on the right-hand side of the formula – a typo, or the treatment itself – is now an error. It used to be dropped silently, so a misspelt covariate quietly changed which variables calibrated the analysis.
• The sensitivity parameters are validated: cbar must lie in [0, 1], rxbar and rybar must be non-negative, r2long non-negative and maxovb non-negative or NA, and none may be missing. Out-of-range values used to reach the closed forms and the optimizer and come back as NA without explanation.
• Under analysis = "oster" with delta_type = "bound", a maxovb cap is now applied at delta >= 1 too. The raw set is the whole real line there, and the cap was skipped, so the reported bounds were -Inf and +Inf where the constraint says beta_med - maxovb and beta_med + maxovb.
• Comparison covariates that are collinear with one another are dropped, as the documentation already said and as Stata does; only those collinear with W0 were. Var(W1) is singular otherwise, and the analysis went through a regularised inverse of it. The test for collinearity with W0 is now relative to the covariate’s own variance, so a covariate measured in small units is no longer mistaken for a constant.
• regsen_boot() keeps replicates on which the breakdown point is +Inf – the hypothesis survived every value of the sensitivity parameter – in both intervals, where they count as +Inf. They were dropped along with failed replicates, so an interval whose upper tail is unbounded was reported with a finite upper endpoint. The count is returned as $infinite and printed.
• print() of a regsen_boot() result reports the interval for the magnitude of a signed (Oster) breakdown point correctly. Taking abs() of each endpoint reversed a negative interval and, for one straddling zero, hid that the magnitude may be as small as zero.
• regsen_table(label = ...) warns when caption is not given, since knitr::kable() emits a float only with a caption and the label had nowhere to attach; it was dropped silently. The r2long column head uses \mathrm rather than \text, so it no longer needs amsmath.
• scale_colour_regsen() and scale_fill_regsen() recycle the palette (with a warning) when a sweep has more than eight groups, instead of erroring inside plot().
• The default y-range of an identified-set plot is read off both bounds. It was read off the lower bound alone, which cropped the upper one whenever the set is not symmetric about beta_med (finite rybar, or Oster).
• plot() of a result whose sensitivity parameters are all single values says so, rather than failing with a subscript error.
• A logical subset containing NA treats NA as FALSE, as subset() does. The NA used to select a row of missing values and leave an NA in the row bookkeeping that regsen_boot() uses to line up a cluster column.
• The ngrid argument of regsen_bounds() and regsen_breakdown() is removed: it was documented but never used.
• The explorer app no longer errors when the rybar box is left at its default. A numeric input cannot carry Inf, so blank now means unrestricted, and a cleared box greys the panel instead of raising.
• Masten and Poirier (2026) is cited as published, in the American Economic Review 116(7), rather than as an arXiv preprint.

Documentation

• New vignette, Reading the output: interpretation, edge cases and decisions (vignette("interpreting-results")). It says what each number means, gives a decision path from breakdown point and rho_k to a verdict, and walks through the cases that confuse readers – a robust and a fragile conclusion on worlds where the omitted variable exists, a breakdown point of zero, a set that is unbounded early because the calibration set is weak, what goes in compare, when cbar matters, an infinite breakdown point under finite rybar, the two Oster breakdown points, and how to read the bootstrap print – each with the sentence to write in the paper. It ends with a list of common mistakes and a reporting checklist.

• The two Oster breakdown points now carry the names Masten and Poirier

  2026. give them, in ?regsen_breakdown, the interpretation vignette and the paper: the explain away point (beta = bnd_eq(0); the psacalc number) and the sign change point (the default). The documentation also states that the sign change value is capped at one by their Theorem 2, not by convention, and that only maxovb lifts the cap. On the frontier data the two are -23.3 and 0.974, and a reader who took one for the other reached the opposite verdict.

Verification

• The identified set is now checked against worlds in which the omitted variable exists. Each test world simulates W2, reads the true beta_long, r_X, r_Y and c off it by the projections DMP (2026) define them through, and requires the package’s set at exactly those parameters to contain beta_long – with rybar unrestricted, with rybar finite (the optimizer), and with two-sided A6. A search over constructed omitted variables on fixed observed data stays inside the rybar = Inf set and reaches within 15% of its ends, so the set is neither invalid nor loose.
• Property tests pin what must hold on any data: sets nest as each parameter is relaxed and collapse to beta_med at rxbar = 0; the breakdown point is where the tested bound crosses the hypothesis; rmax is where the set becomes unbounded; rescaling Y or X scales the bounds as the model says, and rescaling, shifting or reordering the covariates leaves bounds and breakdown points unchanged, for both DMP and Oster.

Accuracy and speed

• The global optimizer behind the finite-rybar identified set now polishes its result with a local search. Bounds agree with a run twenty times as long to within 3e-4, where the previous setting could be off by 0.02.
• A breakdown search evaluates only the end of the identified set its hypothesis tests, and finds the crossing by root-finding rather than fixed-iteration bisection. Together these make a breakdown point at finite rybar several times faster despite the more accurate optimizer.

regsensitivity 0.1.2

Plots

• The Oster equality identified-set plot no longer draws a false vertical connector where the numerically ordered solution branches swap arms across an asymptote. Branches are now split exactly where a step would cross a vertical asymptote of delta(beta), computed from the model rather than guessed from the drawing. With several r2long values the curves are now drawn (and coloured) per r2long instead of being interleaved into one line.

• plot() gains xline, drawing vertical reference lines. This closes a gap against the Stata package, whose vignette uses xline() to mark values such as rmax or a breakdown point; those figures could not be reproduced in R before.

• New theme_regsen() and scale_colour_regsen() (with scale_color_regsen() and scale_fill_regsen() aliases), exported so a plot can be rebuilt with the same look. Defaults are aimed at print: a faint y-grid, a thin panel border, legend on top, and the colourblind-safe Okabe-Ito palette, which also survives greyscale printing.

• Axis and legend titles now use plotmath, so they read as the symbols in the papers rather than as parameter names.

• plot() gains base_size; use 9 for a two-column journal figure.

• Passing NA to title, subtitle, xtitle or ytitle drops the annotation. The auto-generated subtitle is no longer applied by default, since a journal figure carries its description in the caption.

Calibration

• New calibrate_rho() and calibrate_partial_r2(), computing the point-identified reference values of DMP (2026) Section 3.4 (Table 4) and Table 3, so the sensitivity parameters can be read against the observed covariates they are calibrated to.

Verification

• The Oster (2019) implementation is now checked against the paper rather than against its own past output. A test round-trips Proposition 3 into Proposition 2: the delta reported as the breakdown for a target beta, fed back through the identified set, must return that beta. It does, to machine precision, for several targets.
• results$breakdown is documented as signed. The sign carries the direction of selection; passing abs() of it back into regsen_bounds() lands on a different branch of the Oster cubic. The scalar $breakdown reports the magnitude and exists on regsen_bounds() output only – a regsen_breakdown() result carries the value in results$breakdown alone.

Replication fidelity

• The DMP (2026) Figure 1 reproduction now matches the published figure. The left panel gains the dashed bounds under the common-maximal-impact restriction rybar = rxbar and the Table 4 calibration tick marks; both zero crossings are reported and agree with the published 0.804 and 0.959. The right panel previously plotted the breakdown point against cbar, a different object from the paper’s, which is the breakdown frontier in (rxbar, rybar) space; it is now that frontier, one curve per cbar.
• plot() no longer paints a line along the panel edge when a stretch of the sweep is unbounded. Only the first point of each unbounded run is routed off-panel and the rest are blank, so the curve leaves the plot once instead of running along its boundary. Runs are detected per series, so one beginning at a group boundary still gets its own exit.

Bug fixes

• regsen_bounds(analysis = "oster", delta_type = "bound") reported a bounded identified set where the set is in fact unbounded. The solver correctly returns -Inf/Inf for |delta| <= dbar with dbar >= 1, but the routine accumulating bounds across the grid discarded infinite values alongside NA and carried the last finite bound forward. On the bundled data, delta = c(0.5, 1, 2) reported [1.31, 2.98] at every point instead of (-Inf, Inf) from dbar = 1 on.

  NA still carries over, since it means the solver failed and carries no information; an infinity is a bound and now propagates. This is the one direction a sensitivity analysis must not err in, since it made results look more robust than they are. Previously validated numbers are unchanged – the snapshot tests pass untouched.

Interactive

• New regsen_explore(), a shiny application for moving the sensitivity parameters by hand and watching the identified set respond. is a Suggests, so the rest of the package works without it. Inputs are validated and the model is fitted once before the app opens, so a bad call reports at the console rather than as a banner in a browser tab.

Several treatments

• New regsen_multi(), running the analysis once per candidate treatment and collecting the breakdown points into one table, with a plot() method ordering treatments by fragility. Every treatment is analysed in the same specification – the others stay as controls – so the rows are comparable. A treatment whose analysis fails yields an NA row with the reason rather than aborting the sweep.

Inference

• regsen_boot() gains ncores, running replications in parallel: forking on macOS and Linux, a PSOCK cluster on Windows. A progress bar is not shown in parallel, since it cannot report meaningfully from several workers.
• Results for a given seed are now identical regardless of ncores. Each replicate seeds itself from a vector drawn once up front, rather than relying on parallel RNG substreams, which would have made the numbers depend on how the work happened to be divided.

Documentation

• New vignette “Publication-ready plots and tables”, covering figure sizing for journals, greyscale and colour-vision safety, and the LaTeX / HTML / Markdown table output.
• New vignette “Coming from the Stata package”, mapping every command and option in the Stata package’s own vignette onto its R equivalent. It doubles as a coverage check: a gap would show as a missing row rather than going unnoticed.

Interoperability

• New regsen_tidy() and regsen_glance(), broom-style tidiers. They are registered on generics::tidy()/glance() when is installed, so results flow into and anything else speaking that vocabulary; generics stays a Suggests.
• New autoplot() method, forwarding to plot().

Replication

• The DMP (2026) vignette now checks its numbers against the published values in a table, and stopifnot()s the comparison, so a regression stops the build instead of quietly shipping a wrong replication.
• Calibration labels in the Figure 1 overlay are staggered across three x positions; at a single x they collided into an unreadable stack.

Tables

• New as.data.frame() method returning the results as a plain data frame, so they can be passed to any table package (kableExtra, gt, modelsummary, huxtable, tinytable, xtable).
• New regsen_table() rendering results as LaTeX, HTML or Markdown, so the same call works in an Rmd/Qmd knitting to PDF, HTML or Markdown. LaTeX output uses booktabs and puts notes in a threeparttable, with notes = TRUE generating a note recording the analysis, hypothesis and sample size. Infinite bounds render as the correct symbol per format rather than being blanked out.

regsensitivity 0.1.1

• Fix a Windows-only crash when the DIRECT optimizer (DMP analysis with rybar < Inf and cbar > 0) explored parameter points where varx_bounds() returned NA endpoints. The downstream quad_ineq_bounds() did unguarded if (b1 <= r1 && b2 <= r1) comparisons which evaluated to NA and triggered “missing value where TRUE/FALSE needed”. Symptoms: BFG2020 vignette failed to build on Windows (Status: 1 ERROR on win-builder), but built clean on macOS due to floating-point determinism differences in nloptr’s DIRECT-L.
• Audit all bare if (...) numeric comparisons in dmp.R and guard against NA propagation. Every comparison now uses isTRUE() / isFALSE() so a single NA cannot poison the dispatch logic.
• Add 13 NA-robustness regression tests pinning the failure mode (tests/testthat/test-na-robustness.R).

regsensitivity 0.1.0

• Initial CRAN-ready release.
• Implements:
  • DMP (2026) identified set and breakdown frontier (rybar = Inf analytic, rybar < Inf, cbar = 0 analytic, rybar < Inf, cbar > 0 via DIRECT global optimization (nloptr)).
  • Oster (2019) identified set (eq and bound modes) and breakdown points.
  • Masten & Poirier (2026) maxovb extension.
• User API: regsen_bounds(), regsen_breakdown(), regsen_summary(), regsensitivity() dispatcher, hypothesis helpers (bnd_lb(), bnd_ub(), bnd_eq()).
• Visualization: plot.regsensitivity() (ggplot2).
• Vignettes:
  • regsensitivity: end-to-end tour with the BFG2020 application.
  • dmp2022-replication: reproduces every table and figure of Diegert, Masten & Poirier (2026) that uses the bundled data.
  • mp2022-stylized: reproduces the stylized Oster examples from Masten & Poirier (2026).
• Bundled data: bfg2020 (Bazzi, Fiszbein, Gebresilasse 2020, subset).