NEWS
posc 0.2.0 (2026-10-21)
New features
posc_emp() estimates a POSC directly from raw data, with no normality
assumption: a spline logistic regression on all pairwise differences (or a
random subset of max_pairs of them for large samples), set up
so that P(0) = 0.5 and P(-delta) = 1 - P(delta), with a person-level
bootstrap confidence band.posc_emp() also computes observed proportions in bins of the difference
(bins = number of equal-count bins or a vector of breakpoints). plot()
draws them as points with simultaneous (family-wise) confidence intervals,
computed by a max-t bootstrap on the empirical-logit scale so they are
asymmetric and stay within [0, 1]. posc_bins() returns them as a table.plot(x, mark = ...) highlights chosen differences: a guide line runs from
the x-axis up to the curve, with the probability labelled (add mark_ci = TRUE for its confidence interval). Works for
single curves and comparisons.posc_uni(), posc_multi(), posc_lm() and posc_emp() return a
"posc" object with print(), summary(),
plot(), autoplot(), predict() and as.data.frame() methods.
predict() gives probabilities and confidence intervals at any difference.plot(x, full = TRUE) draws the full sigmoid over negative and positive
differences.posc_comp() accepts any number of curves (named or with labels) and
uses a homogeneity test of the correlations when there are more than two.posc_multi() and posc_lm() gain adjust, which uses the adjusted
multiple correlation by default to correct the optimism of in-sample R.- Redesigned plots: by default just the curve and its confidence band, with
axis lines, very light gridlines and a probability axis in steps of 0.1.
Options add percentages (
percent = TRUE), remove gridlines
(grid = FALSE), an automatic title and
subtitle (title = TRUE, subtitle = TRUE), and for empirical curves the
binned proportions (bins = TRUE) and normal-theory curve
(reference = TRUE). Comparisons use a colour-blind-safe palette.
Development
- The core of the package was written by the author; Claude (Anthropic)
assisted with extending, documenting and testing the code in this version,
under the author's direction and review.
Bug fixes
- The x-axis of
posc_uni(), posc_multi() and posc_lm() plots was
stretched by a factor of sqrt(2) relative to the plotted probabilities.
Differences are now on the predictor's own scale.
posc_lm() referred to an object mdl that did not exist and assumed the
response was named y; it now works with any lm() model.- Functions no longer change the global
warn option.
Breaking changes
- Curves are computed for signed differences, and always as the probability
of a higher outcome. With a negative relationship the curve falls below
0.5, rather than being relabelled as the probability of a lower outcome.
ci.lvl is now level; colours are set in plot() rather than when the
curve is created; posc_multi(outcome_idx =) is now outcome = (the old
name still works, with a warning).- The returned object is a
"posc" list with a curve data frame
(delta, p, lower, upper), replacing predict_matrix and
posc_plot_object.
- Plots are no longer drawn automatically; call
plot() on the result.
- The unused ggExtra dependency was removed.
posc 0.1.0