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Changelog

Source: NEWS.md

rtprep 0.1.0

Initial CRAN release. rtprep gives the response time preprocessing steps that precede an evidence accumulation model fit one interface: screening, aggregation, EZ-diffusion estimation, and data generation with known ground truth.

  • rt_screen() applies any screening rule to a response time vector and returns the same per-trial columns (.keep, .prob, .rule, .reason) whichever rule was used, with .by grouping so it works inside dplyr::mutate() and dplyr::reframe().

  • rt_keep() returns the keep decision as a logical vector and reports how many trials were dropped, so dplyr::filter(rt_keep(rt, rule, .by = id)) is the whole exclusion step.

  • screen_fits() returns the per-group fit diagnostics of a rule as a data frame.

  • report_screening() turns a screen into a Methods paragraph: the rule and its setting, the grouping the criterion was computed within, how much it removed in total and per cell, what it caught, whether it read accuracy, the keep policy, and the references for the criteria used. It separates what the rule excluded from what it never saw, so a missing response time is not counted as an exclusion. The returned object carries every number the paragraph quotes, and toBibtex() on it gives the BibTeX entries.

  • rule_cutoff(), rule_sd(), rule_mad(), rule_iqr(), rule_recursive(), rule_ewma(), and rule_none() implement absolute cutoffs, the SD and MAD criteria, Tukey’s quartile fences, the recursive criteria of van Selst and Jolicoeur (1994), the EWMA control chart of Vandekerckhove and Tuerlinckx (2007), and a pass-through baseline.

  • rule_all(), rule_any() and rule_then() combine rules. rule_all() removes the union of what its components remove, which is how a slow-tail criterion and a leading-edge detector cover between them what neither covers alone. rule_then() stages them, each fitted on the trials the last one left, which is what trimr::sdTrim(minRT = , sd = ) does and what a two-stage description in a Methods section usually means.

  • rule_hierarchical() shrinks each group’s centre and spread towards the values pooled over all groups, so a participant’s criterion is not estimated entirely from the data it is meant to clean. Experimental.

  • rule_oracle() removes exactly the trials named as contaminants. Only meaningful on generated data, where it is the ceiling the other rules are read against.

  • rule_mixture() fits a uniform-contaminant mixture with an ex-Gaussian, lognormal, or inverse Gaussian core by expectation maximisation and returns a per-trial posterior probability that the trial came from the decision process.

  • rule_mixture(use_accuracy = TRUE) is an experimental, off-by-default variant that puts accuracy inside the mixture likelihood.

  • .prob is always the probability that a trial is valid, and policy = "threshold" or "probabilistic" turns it into the .keep decision.

  • rt_summary() aggregates surviving trials into EZ-diffusion summary statistics by sample moments, robust moments, trimmed or Winsorized moments, or the analytic moments of a fitted mixture, and accepts .prob as weights.

  • ez_ddm() inverts those statistics into drift, bound, and non-decision time (Wagenmakers et al., 2007), with the published edge correction and s = 1 as the scaling convention.

  • adjust_accuracy() corrects accuracy counts for estimated contamination, vectorised over the rows of a summary table.

  • check_guessing() tests whether the fast trials a rule removed were guesses, by a Bayes factor against the chance rate.

  • screen_compare() applies several rules at once and reports drop rates, pairwise agreement, and the Jaccard overlap of the excluded sets. summary() returns those tables as an object with its own print() method, so assigning it is quiet; print() and plot() return their input invisibly, and plot() draws with ggplot2 when it is installed and base graphics when it is not.

  • r_contaminated() generates response times from a diffusion or racing diffusion core with leading-edge anticipations, delayed start-ups, or informationless responses added at a known rate, and returns the ground truth with the data.

  • rt_screen()’s result prints as a summary of the screen, reporting what was removed and why, rather than as one row per trial.

  • new_rule(fun = ) takes the screening function itself, so adding a rule needs neither an S3 method nor a registerS3method() call. The function declares what it needs by name – rt, response, rule, idx_by_group for a grouped rule, and any parameter stored on the rule – and the engine passes exactly that; an argument it cannot supply is an error when the rule is built rather than in the middle of a screen. The function may return a logical vector (TRUE = keep), a numeric vector of probabilities, or the full list(prob, reason, fit). description = gives the rule a sentence for print(), and reason = names what a dropped trial was dropped for.

  • rule_custom() does the whole thing in one call, for a rule used once: rule_custom("fast(0.35)", function(rt, cut) rt >= cut, cut = 0.35).

  • new_rule() and the apply_rule() generic are exported, so a package can add a screening family with one constructor and one method instead. ?extending documents both routes, and the engine checks the contract on every return.

  • ?rtprep describes the package and ?rtprep-glossary defines the terms the rest of the documentation uses.

  • rt_example is a small simulated data set with ground truth, used by the examples and the get-started vignette (vignette("rtprep")).

  • Equivalence tests check the screening rules against trimr and the mixture, aggregation, and accuracy functions against bmm.