Changes in version 0.4.1 (2026-11-08)                  

After a case in which the reference group (150 respondents) was smaller
than the batch under examination (210): the functions refused to run,
and with subsamples of 100 the level-matched control samples came out
almost identical (the same gap in every sample), so the p values
overstated the evidence.

  - compare_batches() and neighbor_test() gain min_ratio (default 3).
    When the reference has fewer than min_ratio times the focal
    respondents, including a reference smaller than the batch, each
    sample takes a random subsample of the batch of floor(n_reference /
    min_ratio) respondents and pairs the reference with it. The focal
    value is the median over the focal subsamples (focal_gap_sample in
    neighbor_test()). min_ratio = 1 gives the former behaviour whenever
    the reference is at least as large as the batch.
  - Both functions report the share of rows that consecutive
    level-matched samples have in common (settings$matched_overlap) and
    warn when it is above 80%: the control then barely varies. In the
    case above the share was 100% with the whole reference, 87% with
    min_ratio = 2 and 63% with min_ratio = 3; with 5 the focal samples
    had 30 respondents and the near-duplicate test lost its power, hence
    the default of 3.
  - compare_batches()$tests gains n_sample, the size of the samples
    compared.

                        Changes in version 0.4.0                        

Tools to judge a whole batch of data, after a case in which 40 late
answers of a survey looked suspicious (many 4s, weak item correlations)
and turned out to be explained by their level and by the flat responders
of the first batch.

  - New compare_batches(): compares the item correlation structure of a
    batch with a reference group by permutation (mean inter-item
    correlation and the within-minus-between-scales structure), after
    leaving out flat responders (drop_flat) and against reference
    samples paired by response level (match_level); adds the Jennrich
    (1970) test. plot() types "null", "heatmap" and "structure".
  - New order_test(): are consecutive rows (items and, optionally,
    demographic profile columns) more alike than in a random order?
    Detects rows copied from the previous one without a submission time.
    plot() types "null" and "sequence".
  - New neighbor_test(): distance to the most similar respondent,
    against data shuffled by item and against level-matched control
    samples; returns the closest pairs. plot() types "null" and
    "distance".
  - scan_responses() gains the even-odd consistency index and flag
    (evenodd, evaluated with eo_min_scales = 6 scales or more), and the
    reference and ref_quantile arguments: the cut-offs of low_irv,
    mahal, psychsyn and evenodd are then taken from a reference group
    without flat responders.
  - flat is judged only in scales of flat_min_items (6) items or more,
    or in all items together when no scale is that long. In three- or
    four-item scales many genuine respondents give the same answer
    everywhere.
  - New simulated data batch_sim: two phases, with a hand-filled block
    in the second one.

                        Changes in version 0.3.1                        

Adjustments after screening two short one-factor scales (7 and 10 items)
in a large real sample (N = 4911), where three criteria flagged far too
many people.

  - psychsyn uses disjoint pairs: each item enters one pair only,
    strongest first. In a short one-factor scale almost every pair
    passes syn_r and the pairs reused the same items, so the
    within-person correlation was noise (45 % flagged). New argument
    syn_min_pairs (default 5): with fewer disjoint pairs the criterion
    is reported as not evaluable.
  - duplicate is evaluated only with at least dup_min_items items
    (default 15). With few items, genuine respondents share answers by
    chance (28-45 % flagged in the real case). Vectors that are flat in
    every scale are no longer counted as duplicates; they are already
    flagged by flat.
  - low_irv is computed among respondents with some variability (IRV
    > 0). Before, when more than 5 % of the sample responded flat the
    cut fell to 0 and low_irv repeated flat exactly.
  - exclude_responses() warns, and compare_fit() adds a note, when a
    rule removes more than half of the sample.
  - compare_fit() adds a note when an exclusion removes respondents for
    their Zh: Zh is estimated from the same items, so part of the gain
    in fit is circular.

                        Changes in version 0.3.0                        

  - All plots are now drawn with ggplot2 (new import). plot() returns
    the ggplot object invisibly, so it can be modified or saved with
    ggsave(); new argument base_size.
  - The help page of plot.response_scan() and of plot.fit_comparison()
    explains how to read each view.

                        Changes in version 0.2.0                        

  - New exclude_responses(): returns the data without the respondents
    that meet the chosen flags, patterns, number of flags or rows, ready
    for any model.
  - New compare_fit(): fits the same CFA (lavaan syntax) or EFA with
    each exclusion and compares fit indices, mean loading and factor
    correlations; improper solutions are reported. print() and plot()
    methods.
  - plot() gains five views: "days", "overlap", "profiles", "styles" and
    "scatter".
  - as.data.frame() prefixes the flags with flag_ so that they do not
    clash with the index of the same name.

                        Changes in version 0.1.0                        

  - First version: scan_responses() with print(), summary(),
    as.data.frame() and plot() methods (overview, indices, patterns,
    heatmap and time), and the simulated data set survey_sim.