pkgdown/extra.css

Skip to contents

Changelog

tbl.now 0.12.0

Batch detection, rebuilt around a conservation law

The report-batch detectors were rebuilt on a single, exact principle: a batch moves reports along the report axis without creating them, so a window of report dates spanning both the lull and the release has an unchanged total, whereas a genuine epidemic surge inflates it. The previous heuristic detect_report_batches() / plot_report_batches() (multi-signal robust-z, and the model-based conditional scan) are removed and replaced by three model-free, r lifecycle::badge("experimental") functions. Each derives its mathematics in a “The mathematics” section of its help page.

  • New batch_screen() returns, per (report date, stratum), the deficit (reports missing beforehand — sensitive to a batch) and delta (the window total minus its expected value — sensitive to a real surge), and classifies each date as "batch", "surge", "batch_and_surge", "hold_or_deletion" or "none". The transport (batch) test conditions on the window total, so its size does not depend on the unknown incidence nor on the quality of the baseline; the baseline itself is refit from report dates outside each candidate window, which makes delta invariant to a within-window batch pathwise. It handles all data types, including "count-cumulative" (signed increments), and takes a period argument that absorbs a fixed reporting schedule (weekends, holidays).
  • New batch_shape_test() tests whether a flagged report date drew on unusually old event dates, by a permutation rank-sum on the reporting delays. It is exactly distribution-free whenever incidence is locally log-linear.
  • New simulate_batch() plants a known batch (a deterministic close-and-release) in a tbl_now, for validation and teaching.
  • New Batch detection article, with worked examples on dengue (a planted batch), FluSight (count-cumulative), and a weekend reporting schedule.

tbl.now 0.10.1

  • autoplot()’s reporting-delay calendar panels (delay_weekday, delay_week, delay_month) are now normalized: each event date’s mean delay is divided by the overall mean delay, so 1 marks an average delay and a dashed reference line is drawn there. Previously the ungrouped panels plotted the raw mean delay while the by_strata = TRUE panels were already normalized. They now share one scale, matching the case-count calendar panels and making the calendar pattern comparable across strata (y-axis: "Normalized delay").
  • plot_delay_drift()’s window now defaults to 7 periods regardless of the time unit — 7 days for daily data, 7 weeks for weekly data. Previously the default was data-dependent (max(5, n_periods / 20)), which produced a very wide window on long series. Pass window = to smooth a specific series.
  • Internal: replaced the remaining base-R data-frame subsetting and column assignment (df[cond, ], df$col <- ...) outside the converters with the equivalent dplyr verbs (filter(), select(), slice(), mutate()). No user-facing behaviour change. The examples and vignettes now likewise use dplyr::filter() rather than [ (e.g. dplyr::filter(batches, batch)).

tbl.now 0.10.0

  • New get_nth_reported_cases(): the cumulative cases reported for each event date within a given delay. delay = 0 gives the initial snapshot, delay = 1 adds the delay-1 reports, and so on; delay = Inf (or the maximum delay) matches get_latest_reported_cases(). Documented alongside get_initial_reported_cases() and get_latest_reported_cases().

  • Performance: get_latest_reported_cases(), get_initial_reported_cases() and get_nth_reported_cases() are substantially faster (~3-4x on the bundled data) — the aggregation now runs on a declassed data frame and the tbl_now is reconstructed once, with identical output.

  • The experimental diagnostic functions (plot_delay_drift(), test_delay_drift(), test_delay_changepoint(), detect_report_batches(), plot_report_batches()) now carry a lifecycle experimental badge. test_delay_drift() and test_delay_changepoint() additionally emit a cli warning that they are experimental, their results are not guaranteed and their interface may change. Flagged batches, change points and trend changes are surfaced as potential (e.g. “potential batches”, “potential change point”).

  • New detect_report_batches() and plot_report_batches() to detect batch reporting — report dates on which a laboratory releases a backlog of many old cases at once. Working on the report-date axis, it flags a report date using up to four selectable robust-anomaly signals (volume, delay, span, gap), AND-ed together. Requiring the delay (long/dispersed delays) signal alongside volume is what distinguishes a batch from an epidemic peak: a peak also spikes the report volume, but its cases keep the normal short delay distribution, so its delay score stays low. detect_report_batches() returns a per-report-date table with the features, robust scores and a batch flag; plot_report_batches() shows the report-volume and mean-delay timelines with the flagged dates marked.

  • New test_delay_changepoint() complements test_delay_drift(): where the latter tests for a gradual monotonic trend, this tests for a single abrupt change point in the per-period delay summaries using Pettitt’s nonparametric test (implemented directly, no extra dependency). It reports the estimated change date, the before/after level of the statistic, the shift and a changepoint_detected verdict, per stat (median / mean / IQR / 10-90 spread) and per stratum, on mature data only.

  • plot_delay_drift() gained a changepoint argument: set it to TRUE to mark the estimated change point of the median delay on the fan chart with a vertical line.

  • New plot_delay_drift() and test_delay_drift() to answer “do reporting delay distributions drift over time?”.

    • plot_delay_drift() draws a rolling fan chart of the count-weighted delay distribution indexed by event date: a solid rolling median, a dashed rolling mean, and 25-75% / 10-90% quantile bands. The recent, not-yet-fully reported region (after the level incompleteness cutoff) is shaded grey so the truncation-induced dip is not mistaken for drift. Supports by_strata.
    • test_delay_drift() runs an autocorrelation-robust monotonic-trend test (Hamed-Rao modified Mann-Kendall by default, with Yue-Pilon and block-bootstrap options via the new modifiedmk Suggests) on the per-period delay summaries, testing both a location statistic (median/mean) and a dispersion statistic (IQR / 10-90 spread), on mature data only. Returns a tidy tibble with the Kendall tau, Sen’s slope, p-value and a drift verdict, per stat and stratum.

  • autoplot() gained a by_strata argument (default FALSE). When TRUE, every panel is split by stratum: the calendar and delay boxplots become dodged boxes (one per stratum, side by side), the epidemic process and both periodograms become one coloured line per stratum (no area fill), and the delay distribution becomes dodged bars. Boxplots are normalized per stratum (1 = that stratum’s own average) so the calendar pattern is comparable across strata, and strata are coloured with a viridis scale. A companion strata argument chooses which columns to group on (defaults to the object’s strata; pass a subset such as strata = "gender" to override).

  • autoplot() now draws reporting-delay diagnostic panels alongside the case-count ones, so you can see delay effects: the mean reporting delay by day of week / week of year / month (delay_weekday, delay_week, delay_month), and a periodogram of the mean-delay series (delay_seasonality) that reveals periodicity in the delay itself. The delay panels are computed on the complete part of the series (before the incompleteness line) so recent truncation does not bias them.

  • autoplot() gained a panels argument to choose which panels to draw. It accepts the concrete panel keys, or the aliases "all" (default), "calendar" and "delay_calendar". Selecting a single panel returns it as a plain ggplot2 object instead of a patchwork. Unknown panels error; panels that do not apply to the data’s time unit are skipped with a warning.

  • New pkgdown article “One dataset, many nowcasts” now also demonstrates that temporal (delay) effect columns are carried into epinowcast (metareference/metareport), baselinenowcast (long) and epidist, with a table clarifying which target formats can hold covariates and how each model can use them.

  • temporal_effects() gained an after-holiday and after-weekend effect via the new holiday_lags and weekend_lags arguments. Each takes a non-negative integer depth N; materialising the spec then adds indicator columns ..._holiday_lag_1 … ..._holiday_lag_N (and likewise ..._weekend_lag_k) that flag dates falling exactly k working days after a holiday / weekend. Working days skip weekends and holidays, so the effect lands on the first day(s) back at work — designed to capture the rise in cases just after a holiday or weekend. holiday_lags requires a holidays calendar. The columns are picked up automatically by every tbl_now_to_*() converter (as covariate columns) and by diseasenowcasting::nowcast().

  • Documented and tested attaching temporal effects to the report date (in addition to the default event date) via add_temporal_effects(x, spec, date_type = "report_date"). Event- and report-date effects can coexist on the same tbl_now; both sets of columns (.event_* and .report_*) are carried through all converters.

tbl.now 0.9.0

  • Added as_tibble() and as.data.frame() methods for tbl_now with an opt-in compute_temporal_effects argument (default FALSE). Passing compute_temporal_effects = TRUE materialises the lazy temporal_effects() spec (holidays, Fourier terms, calendar effects) into columns before returning a plain tibble / data.frame; the input tbl_now is left unchanged. The default stays lazy on purpose, because dplyr relies on these coercions being cheap, non-materialising declassers internally (e.g. group_by()).
  • The tbl_now_to_*() converters now carry the (lazy) temporal-effect columns (holidays, Fourier seasonal terms, day-of-week / calendar effects) into the target format as covariate columns. The spec is materialised on demand via compute_temporal_effects() at conversion time (the input tbl_now is left unchanged), and the columns are passed to data.table, tsibble, baselinenowcast long format, epidist, and epinowcast (where they appear in the observations and metareference tables for use in the reference module). The baselinenowcast reporting-triangle matrix still cannot hold them.
  • Removed the %>% export and changed all the pipes to |>
  • Refactored converters.R for readability (dplyr column operations instead of base indexing, full variable names, lintr-clean).
  • The tbl_now_to_*() converters now keep the covariates and is_censored columns wherever the target format can hold them (data.table, tsibble, baselinenowcast long format, epidist linelist); the fixed modelling objects (enw_preprocess_data, the reporting-triangle matrix, the EpiNow2 series) still cannot carry them.
  • Added S3 methods on the other packages’ coercion generics so they accept a tbl_now directly: as_epidist_linelist_data(), as_reporting_triangle(), as_tsibble() and as.data.table(), each wrapping the matching tbl_now_to_*().
  • Fixed tbl_now_to_data_table() checking for baselinenowcast instead of data.table, and tbl_now_to_baselinenowcast(format = "long") no longer requiring baselinenowcast to be installed.

tbl.now 0.8.0

  • Modified the update as the t_effect argument was not doing anything.
  • Fixed bug that errored complete_zeroes when is_censored was given.
  • Removed explicit zeroes from the converters (tbl_now_from_*) as they are not necessary in tbl_now.
  • Added censor_delays_above() to flag reports with an implausibly long delay as censored (their delay becomes an upper bound).
  • Improved documentation and README
  • Documented all internal functions with roxygen (@keywords internal + @noRd) and ensured every exported function has a @return.
  • Homogenized lifecycle badges.
  • Brought the censor_delays_above function from diseasenowcasting to tbl_now.
  • tbl_now_from_epinowcast() now accepts not only the raw long input but also a preprocessed enw_preprocess_data object or a fitted epinowcast object (grouping auto-detected), matching the format epinowcast uses for summaries and plots.
  • tbl_now_to_EpiNow2() gained a model argument: "estimate_infections" (default, the single date/confirm series) and "estimate_truncation" (a list of report-date snapshots, the one EpiNow2 model that uses the report dimension). Documentation clarified accordingly.
  • Fixed two converter requireNamespace() guards: tbl_now_to_data_table() checked for baselinenowcast instead of data.table, and tbl_now_to_baselinenowcast(format = "long") no longer requires baselinenowcast to be installed.

tbl.now 0.7.5

  • Bumped roxygen to version 8.0.0. This also resulted in updated documentation.
  • Changed autoplot()’s default level to 0.95
  • Added tests for converters and pillars.
  • Throws warning when converting to baselinenowcast if data is "count-cumulative".

tbl.now 0.7.3

  • Added the update_now() function to make it more intuitive to update the now.

tbl.now 0.7.0

  • Added an autoplot() method for tbl_now objects that produces a multi-panel diagnostic overview: the empirical delay distribution, the observed epidemic process with an incompleteness line (controlled by level), normalized calendar-effect boxplots (cases relative to the overall mean), and a periodogram to help choose Fourier seasons. Daily data shows both a day-of-week and a week-of-year boxplot panel; weekly data shows week-of-year. Built on ggplot2 and patchwork. The x-axis limits of each panel can be set individually (delay_distribution_xlim, event_date_xlim, calendar_effect_xlim, seasonality_xlim), and holidays from the temporal-effects spec are marked with red dots on the epidemic process.
  • Added converters to and from other packages, all of the form tbl_now_from_*() / tbl_now_to_*(): epinowcast, baselinenowcast, EpiNow2 (to only), epidist, data.table and tsibble. The tbl_now_from_*() functions wrap as_tbl_now() and forward ... to tbl_now(); the tbl_now_to_*() functions call into the target package. All accept a verbose argument that reports the choices made (the inferred now, data type, units, and column mapping).
  • as_tbl_now() gained methods for the classes produced by tbl_now_to_*() (enw_preprocess_data, reporting_triangle, epidist_linelist_data, tbl_ts and data.table), so a converted object can be turned straight back into a tbl_now.
  • Documented autoplot() and the converters in the introduction vignette.

tbl.now 0.6.4

  • Fixed dependency on R >= 4.2.0
  • Update function now defaults the censoring to FALSE if the update is censored but the original is not.

tbl.now 0.6.3

  • Added season length to seasons so we can get weekly seasonality.

tbl.now 0.6.2

  • Removed warning when using columns for temporal effects that cascaded into to_count.
  • Changed DESCRIPTION to fix ortographic error and trigger less messages of unknown words.

tbl.now 0.6.1

  • Changed links in description of tidy-select

tbl.now 0.6.0

  • Changed temporal effects to be lazy (as required by #17) so that now its easier to use dplyr functions without compromising them.
  • Bumped the deprecated dplyr’s *_at functions to use all_of()
  • Fixed to no warnings during test.
  • Users can now pass the .delay column directly (#6) and it will recalculate the missing column (i.e. event or report)
  • Added complete_zeroes to vignette (#13).