Changelog

Source: NEWS.md

predictset 0.4.0

This release corrects six defects that affected results, and changes two defaults. The three headline defects are listed first; the weighted, Mondrian and Jackknife+/CV+ fixes further down also change what you get back. Anyone using conformal_aci(), conformal_aps(), conformal_raps(), or passing a formula or fitted model as model should re-run their analysis.

Bug fixes

• conformal_aci() applied the online update with the operands reversed: alpha_t + gamma * (err_t - alpha) instead of alpha_t + gamma * (alpha - err_t) (Gibbs and Candes 2021, Eq. 2). A miscoverage event therefore narrowed the next interval instead of widening it, turning the intended negative feedback into positive feedback, and alpha_t ran away to a clip boundary. Under a variance shift this drove empirical coverage to 0.605 against a 0.90 target.

• The model argument was discarded whenever it was a formula or a fitted model object, and a plain lm(y ~ .) was fitted in its place. model = y ~ a silently fitted every column of x; lm(y ~ poly(v1, 3) + v2) was refitted as y ~ v1 + v2; ranger hyperparameters were dropped. Formulas and fitted models are now honoured, refitted on each conformal split. Objects that cannot be refitted raise an error naming make_model() instead of silently substituting a default.

• conformal_aps() and conformal_raps() could return the full label set for every observation. The set builder included the class that crossed the threshold rather than inverting the calibrated score, and randomize = TRUE randomised the calibration scores but never the set construction. With oracle probabilities on a four-class problem, APS returned a mean set size of 3.90 out of 4 at 99.9% coverage; it now returns 2.69 at 88.6%.

• conformal_jackknife(), conformal_cv(), and predict() clamped the interval bounds to the smallest and largest order statistics when the quantile index fell outside 1..n. Barber et al. (2021) define those bounds as infinite, which occurs for fewer than 9 observations at alpha = 0.10. They now return -Inf / Inf, matching conformal_split().

• conformal_weighted() substituted the mean calibration weight for the test-point weight, so every test point received the same quantile. The new weights_new argument gives the exact procedure of Tibshirani et al. (2019), in which each test point receives its own quantile. Omitting it with non-uniform weights now warns.

• A fitted glm passed to any classification method failed with “missing columns for class levels”, because the internal probability matrix was unnamed.

• coverage_by_bin() failed with “‘breaks’ are not unique” whenever predictions contained ties. Duplicate breaks are now collapsed, with a warning reporting the number of bins actually used.

• Conformal functions called set.seed() on the global random stream and left it altered. The seed argument now applies for the duration of the call only, and the user’s .Random.seed is restored on exit. Seeded calls remain reproducible.

• plot() failed with “need finite ‘ylim’ values” on unbounded intervals. These are now drawn to the plot edge with a message.

• conformal_mondrian() and conformal_mondrian_class() silently substituted the pooled quantile for groups with fewer than three calibration points, voiding the group-conditional guarantee for exactly the groups that needed it. Such groups now receive an unbounded interval, with a warning naming the number of calibration points required.

• Data frames containing non-numeric columns produced the misleading error “must not contain NaN or Inf values”. They now name the offending columns and suggest stats::model.matrix().

Changes in default behaviour

• conformal_aps() and conformal_raps() now default to randomize = TRUE, the method as published. Deterministic scoring (randomize = FALSE) remains available and now warns when the conformal quantile saturates at 1. Pass seed for reproducible randomised sets.

• Prediction sets are now the exact inversion of the calibrated score. New allow_empty argument on conformal_lac(), conformal_aps(), conformal_raps(), conformal_class_split(), and conformal_mondrian_class(); the default FALSE keeps the previous behaviour of replacing an empty set with the most probable class.

Other changes

• conformal_cqr()’s quantiles argument previously had no effect. It is now validated, recorded on the returned object, and checked for consistency with alpha.

• conformal_cv() and conformal_jackknife() now validate that x_new has the same number of columns as x.

• Jackknife+ prediction makes one call to predict_fun per leave-one-out model rather than one per (model, test point) pair.

• conformal_cv(x_new = NULL) records train_approximation = TRUE, and print() states that those intervals do not carry the CV+ guarantee.

• inst/CITATION now reports the installed version rather than a hardcoded one.

• paper/, Makefile, and llms*.txt are excluded from the source tarball, which removes an R CMD check WARNING about GNU extensions in paper/slides/Makefile and cuts the tarball from 4.2 MB.

• README and the accompanying paper: corrected the CRAN status, dependency count, and competitor versions, and softened the Mondrian and ACI exclusivity claims (see conformalForecast and AdaptiveConformal).

predictset 0.3.2

• Fixed predict() for LAC and Mondrian classification objects: randomize field now defaults to FALSE when not set by the fitting method.

predictset 0.3.1

• Add DOI links for all DESCRIPTION references per CRAN reviewer feedback.

predictset 0.3.0

CRAN release: 2026-03-19

Documentation

• Documented Jackknife+ and CV+ theoretical coverage guarantee (1-2alpha) per Barber et al. (2021)
• Documented ACI asymptotic (not finite-sample) coverage guarantee per Gibbs and Candes (2021)
• Documented CQR dependence on quantile model quality
• Documented deterministic vs randomized APS variants
• Added coverage guarantee footnotes to README and vignette method tables

Internal

• Added graphics and grDevices to DESCRIPTION Imports
• Added missing test dependencies to Suggests

predictset 0.2.0

New features

• conformal_mondrian() and conformal_mondrian_class() for group-conditional (Mondrian) conformal prediction
• conformal_weighted() for weighted conformal prediction under covariate shift
• conformal_aci() for adaptive conformal inference (sequential prediction)
• conformal_pvalue() for conformal p-values
• conformal_compare() for benchmarking multiple methods side-by-side
• coverage_by_group() and coverage_by_bin() for conditional coverage diagnostics
• Progress bars via verbose = TRUE for conformal_jackknife() and conformal_cv()

Improvements

• NA/NaN/Inf input validation with informative error messages
• Column dimension checks between training and test data
• Probability matrix column validation for classification methods
• Graceful handling of unseen factor levels in APS/RAPS/LAC scoring
• Negative scale model prediction warnings for normalized conformal

predictset 0.1.0

• Initial release with split conformal, CV+, Jackknife+, CQR (regression) and split, APS, RAPS, LAC (classification)