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Changelog

tbl.now 0.15.0

  • autoplot() panels are now consistently colour-coded by process: red for everything reporting-related (the delay distribution, the delay calendar/holiday effects, the delay periodogram) and green for the epidemic (event-date) process (the observed cases and their calendar/holiday effects). This matches the colours the standalone diagnostic plots (plot_reporting_process() / plot_epidemic_process(), plot_scalogram(), …) already used, so a panel and its standalone twin read the same.
  • Every autoplot() panel now says which process it describes in its subtitle — either “Reporting delay process” or “Epidemic (event-date) process” — replacing the per-panel explanatory subtitles. A single panel therefore reads on its own.
  • The two periodogram panels are renamed “Cycles (periodogram)” (previously “Seasonality” / “Delay periodicity”).
  • Every autoplot() panel now has a standalone plot_*() twin that draws just that panel (identical data, colours and subtitle): plot_day_of_week_effects(), plot_week_of_year_effects(), plot_month_of_year_effects(), plot_holiday_effects(), plot_holiday_lag_effects() (each taking type = "epidemic" or type = "report"), plus plot_cycles(), plot_delay_distribution() and plot_observed_cases(). Use autoplot() for the grid and a plot_*() for one effect on its own.
  • The day-of-week, week-of-year, month-of-year, holiday and weekend/holiday-lag panels gained a measure argument (in autoplot() and every plot_*() twin). measure = "normalized" (the default) is the existing view — each value divided by its overall mean, 1 = average. measure = "percent" instead shows the share of cases falling in each group with its IQR (e.g. “10% of cases at the weekend versus 90% on weekdays”); the reporting version shares out the reports by report date. Percentages need Date event/report columns.
  • Vignettes: the Get Started guide documents the plot_*() twins and the measure argument, and marks the “Holiday effects”, “Do delay distributions drift over time?” and “Detecting batch reporting” sections as AI-written, pointing readers to the human-written batch-reporting article. The FluSight example analysis is flagged as a work in progress.

tbl.now 0.14.1

  • Strata are now carried into the model converters that can use them. tbl_now_to_epidist() keeps the strata as data columns (usable as covariates in an epidist formula), and tbl_now_to_baselinenowcast(format = "long") keeps them so you can build one reporting triangle per stratum. A single reporting-triangle matrix has no strata dimension, so format = "matrix" now pools the strata with a warning instead of erroring on duplicate cells. tbl_now_to_epinowcast() already passed strata as its grouping (by).

  • The nowcasting-models article was restructured: each package is now shown bare (from dengue_now) and then enriched — one dengue_seasonal object carrying a stratum and temporal effects flows through every converter — so the separate “Carrying delay effects into each model” section is gone. It adds a worked per-stratum baselinenowcast loop (one triangle per stratum). The workflow also had a bug: it used the plural estimate_and_apply_delays() (which expects a list of retrospective triangles) on a single triangle; it now uses the one-call baselinenowcast() wrapper for samples and notes the singular estimate_and_apply_delay() for a point nowcast.

  • New plot_reporting_hexamap(): draws the reporting triangle as an age-period-cohort hexamap (Jalal and Burke, 2020). Event date, report date and delay are the cohort, period and age (report = event + delay); each cell is a hexagon coloured by its report count, and a batch — a single report date — reads as a clean vertical stripe. The number of hexagons is bounded by a max_cells safety cap (the delay axis is auto-capped, with a message, rather than drawing an unbounded map). Replaces the reporting-V panel in the batch article.

  • Bug fix for issue #33: autoplot(x, strata = "race", by_strata = TRUE) no longer errors with a strata passed as column name.

  • autoplot() gained four holiday panels, which describe the attached temporal_effects() spec rather than the event unit:

    • "calendar_holiday" / "delay_holiday" — normalized cases / mean reporting delay by day type. The categories follow the spec: a holidays calendar plus weekend = TRUE gives Weekday/Weekend/Holiday, a calendar alone gives Non-holiday/Holiday, and a weekend effect alone gives Weekday/Weekend. A holiday falling on a weekend counts as a holiday.
    • "calendar_holiday_lag" / "delay_holiday_lag" — the same, by position relative to the nearest holiday ("2 before", "1 before", "Holiday", "1 after", …, plus "Other" as the reference), as asked for by holiday_lags. These show exactly the days the ..._holiday_lag_k / ..._holiday_lead_k columns flag, so you can check a lag is worth modelling before you model it. A date that is both after one holiday and before the next is attributed to the nearer one, ties going to the “after” side.

  • Bug fix: tbl_now_to_epinowcast() now passes a timestep to , inferred from the object’s report units ("days" -> "day", "weeks" -> "week") and overridable with the new timestep argument. It previously left on its "day" default whatever the data, so weekly data was laid out on a daily grid.

  • Bug fix: tbl_now_to_epinowcast() now derives the temporal-effect covariates on ’s completed date grid instead of carrying them through enw_complete_dates(). That function fills the (reference, report) grid and extends the reference axis into the nowcast horizon, but sets every non-schema column to NA on the rows it adds — so the covariates previously survived only on the original rows. Becasue the effects are functions of a date alone, they are n ow re-derived from the completed grid and cover every row, including the recent horizon dates a nowcast has to predict.

tbl.now 0.14.0

  • holiday_lags and weekend_lags in temporal_effects() now accept negative depths, placing the effect before the break instead of after it. A negative depth creates ..._holiday_lead_k / ..._weekend_lead_k indicator columns that flag dates exactly k working days before a holiday / weekend, counting backwards from it — so _lead_1 is the working day closest to the break. weekend_lags = -1 flags the Friday, weekend_lags = -3 flags the Wednesday, Thursday and Friday, and holiday_lags = -1 flags Christmas Eve. Working days skip weekends and holidays exactly as they do for positive depths, and holiday_lags still requires a holidays calendar for either sign. Use it to capture the reporting slowdown that precedes a break; attach one specification per direction to model both sides of it. Positive depths are unchanged.
  • ?temporal_effects gained a “Using a different holiday calendar” section. holidays has always accepted any almanac::rcalendar(), but the docs only showed cal_us_federal(); reporting holidays are local, so the section covers the building blocks (built-in hol_*() rules, custom rholiday() rules, weekend observance with hol_observe(), and editing a calendar with cal_add() / cal_remove()), and works through the New York City calendar as an example.

tbl.now 0.13.1

  • Fixed style in the batch reporting vignette
  • Improved the axis title position in the v triangle to better visualize the dates

tbl.now 0.13.0

  • Bug fix: batch_shape_test() no longer errors (“missing value where TRUE/FALSE needed”) on large count data. The standardised rank-sum expands counts to one value per item, so the group sizes could exceed the 32-bit integer range and their product overflowed to NA; the group sizes are now computed as doubles.

  • batch_test() now returns a lean, Benjamini-Hochberg-only result: report_date, stratum, reported, baseline, deficit, delta, p_transport, p_transport_bh and the batch flag, each documented under ?batch_test. The raw per-point classification column (and the p_creation/p_deletion/scale columns behind it) has been dropped: it was not multiplicity-corrected and over-identified, whereas batch controls the false discovery rate. (transport_discriminant() keeps its classification.)

  • batch_test() (and transport_discriminant()) now infer the calendar period from the object’s temporal effects: a day-of-week effect sets period = 7, a week-of-year effect period = 52 (see [add_temporal_effects()]). A period you pass still wins, with a note if it disagrees; and if the data is daily with no temporal effect, the function suggests period = 7.

  • The baseline_method argument of batch_test() and transport_discriminant() has been removed — the baseline is always the repeated-median local line. The running-median (local-constant) alternative had no advantage: it reduces to the same fit on a flat series and is biased the moment the series trends.

  • New covid_us dataset: a compact aggregation of the U.S. CDC COVID-19 Case Surveillance Public Use Data, with both event and report dates in 2020-2021 (a self-consistent “as of the end of 2021” snapshot), built to demonstrate batch reporting. Its reporting delay is huge and heavily right-skewed — cases were released to CDC in large backlog dumps — so batch_test() and the batch plots recover a clear, real signal (and correctly call the biggest December-2021 spikes surges, since they land on the Omicron wave). Prepared with duckdb from the 14 GB source (see data-raw/covid_us.R).

  • New article, Finding batch reporting in CDC COVID-19 case surveillance data, written for public-health practitioners with no maths. It builds a made-up outbreak with a planted batch to show what each plot looks like (including a novel V reporting triangle – the reporting triangle rotated 45° so a batch is a horizontal slice), rehearses on a real dengue epidemic curve with simulated log-normal reporting and self-planted batches, finds the batches in the real covid_us data, adds a wavelet view (window-inner report-vs-event scalograms, via ), and ends with a one-page summary table. A new transport-vs-creation tutorial plants a hold, a batch and a surge in a made-up outbreak and colours each day the same way on the reporting timeline and in the creation/transport plane, so a reader can trace a bar to its dot and see why a batch goes up, a surge goes right, and a hold drifts up-and-left.

  • Every plot function now takes plotly = TRUE to return an interactive widget (hover, zoom) instead of a static plot: plot_reporting_process(), plot_epidemic_process(), plot_reporting_triangle(), plot_delay_profiles(), plot_delay_drift(), plot_transport_discriminant(), plot_reporting_v(), plot_scalogram(), diagnostic_plot() and autoplot(). Needs the (suggested) package.

  • New plot_reporting_v(): the reporting “V” – the same data as plot_reporting_triangle() (the same event-date x delay cells) rotated 45° so report date runs up the page and the data opens into a V (left arm = event date, right arm = delay). A batch, a diagonal in the square triangle, becomes a horizontal slice. The whole observable triangle is filled (pale-blue reported zeros + coloured reports).

  • New wavelet scalograms, plot_scalogram(type = "reporting") and plot_scalogram(type = "epidemic"), plus the paired plot_reporting_process() and plot_epidemic_process() bar charts. The scalogram splits the count series into fast wiggles (short periods) and slow swings (long periods) and shows the energy at each: a batch lights up as a bright short-period ridge in the reporting scalogram that the epidemic (event) scalogram lacks. These use a window-inner scalogram (, border_effects = "INNER"): computed from observed data only, with no border padding, so nothing is fabricated at the recent (“now”) edge that matters for nowcasting. Reporting views are drawn in red, epidemic views in green. plot_scalogram() defaults to the PAUL wavelet (wname), which localises a batch more sharply; takes a format argument for the x-axis date labels (default "%d/%b/%y"); and paints the region outside the cone of influence dark grey. The series is analysed on its own integer time grid, so weekly (or monthly) data is handled correctly, and the heat map tiles a uniform index relabelled with dates so it stays gapless even for long series.

  • The conservation monitors — plot_creation_transport() (the two window scores as stacked panels) together with the cumulative-backlog, reporting-lag, dashboard and transport-minus-creation “batch score” plots — live in devel/conservation_extras.R, kept out of the package: clean on large batches but noisy in general. The transport diagnostics keep their exported transport_discriminant() / plot_transport_discriminant().

  • simulate_batch() gains a held_fraction argument: the fraction of each closed date’s reports actually held back and released later (default 1, a full closure). With held_fraction = 0.5, roughly half of each day’s reports are held and half report on time – a realistic partial slow-down rather than a total blackout. Supported for "linelist" and "count-incidence" data (a cumulative total cannot be split).

  • The default lookback for batch_test() and transport_discriminant() is now 7 (a week of daily reporting) rather than 3.

  • The @details of the batch functions (batch_test(), transport_discriminant(), batch_shape_test(), simulate_batch()) and the batch plots were trimmed: the formal theorem / null-distribution derivations were replaced with concise, plain-language explanations.

  • New diagnostic_plot(): a gallery of complementary views of the reporting process for spotting reporting artefacts (above all batch reporting), laid out in two columns. The five panels are the reporting process (reports by report date), the reporting triangle (event date x delay), the per-date delay profiles, the reporting-delay drift (plot_delay_drift()), and the transport discriminant plane. Each is also its own exported function. Choose views with panels (a single one is returned as a plain plot), and every view is facetted by stratum. by = c("report", "event") switches the profiles panel; ... (e.g. period = 7) is routed to whichever panels accept it.

    • Every panel carries a plain-language, grey caption explaining what it shows and what the colours mean, and legends are labelled in words.
    • The reporting process y-axis is capped at the 99th percentile only when a pathological dump (over 30x the median day, e.g. covid’s 1.8M-report day) would otherwise flatten the whole series; an ordinary batch spike – the very thing the plot exists to show – is left to tower.
    • The transport discriminant y-axis is limited to the batch region (with default clipping, so points stop at the panel edge) so the deep-negative “hold” dates do not squash the confirmed batches; the shaded region is now labelled “Potential batch region” and each confirmed batch gets a bold, unclipped date label.
    • The reporting triangle draws a third axis for report date: evenly spaced dashed diagonals (report = event + delay) running up-right at 45°, labelled by report date, so event date (x), delay (y) and report date are all readable off one plot (plot_reporting_triangle(report_ticks =), default 6; mark_batches = optionally highlights the biggest batch stripes). It also distinguishes an observable reported zero (muted blue) from a not yet reportable cell (blank), on the full calendar event axis.
    • The delay profiles draw in a single colour at fixed transparency.
    • The transport discriminant colours red only the batch_test()-confirmed batches (BH-corrected), not the raw per-point classification – which at level alpha painted 10-20% of points batch/surge/hold by construction, ignoring multiplicity and the heavy autocorrelation of the window statistics. The shaded batch region and the ±z* lines are drawn only as a reference for where a batch would sit.

  • New transport_discriminant(): exposes the plane behind batch_test()’s conservation law – for every report date the deficit (the transport axis: reports the preceding window is missing) and the window discriminant (the creation axis: the window total relative to its baseline), with robust standardised transport_z / creation_z and the same quadrant classification. A batch sits top-left (a deficit paid the spike, no net creation); a surge sits bottom-right. Returned as a transport_discriminant tibble and plotted by diagnostic_plot(panels = "transport").

  • The multi-panel autoplot() title changed from “Diagnostic plots” to “Automatic plot of effects” (that phrase now titles diagnostic_plot()).

  • batch_test(null_model = "auto") is now overdispersion-aware. The exact Poisson/Binomial null assumes Poisson counts and a baseline that captures the mean; real surveillance counts are overdispersed, and the conditional transport test is then badly anti-conservative (on clean but overdispersed Poisson data it can fake dozens of batches). auto now reserves the exact null for non-negative counts with no detected overdispersion (dispersion <= 1.5) and otherwise falls back to the dispersion-corrected robust null; signed (count-cumulative) increments still always use the robust null. This makes the default far more realistic on overdispersed data (e.g. filtered covid_colombia drops from ~125 flags to ~18; add period = 7 for its weekly reporting cadence to reach ~4). Force the old behaviour with null_model = "poisson" if you need it.

  • autoplot()’s empirical delay distribution panel now adapts to count-cumulative data: instead of a histogram of increments it shows the cumulative growth by delay — boxplots (on a log scale, with a dashed reference at 1) of the ratio of each event date’s cumulative count at a delay to its count at the previous delay. Ratios above 1 are upward revisions, below 1 downward ones, and they converge to 1 as reporting completes, so you can see the cumulative curve stabilise. The log scale makes a doubling and a halving symmetric about 1. linelist / count-incidence data keep the histogram, and the panel respects by_strata.

  • tbl_now_to_baselinenowcast(delays_unit = ) now defaults to NULL and is inferred from the object’s time units for the "matrix" format: when the event and report units are equal and either "days" or "weeks", that unit is used; otherwise the function errors asking you to supply delays_unit explicitly. (The "long" format never uses it.)

  • Added the covid_colombia dataset from diseasenowcasting to here.

  • Fixed several documentation issues that produced “could not resolve link” warnings when building the docs (links to internal helpers / to the un-declared trend package, a [0, 1] mis-parsed as a link, and a mis-ordered internal roxygen block).

  • to_count() now supports count-cumulative -> count-incidence by de-accumulating the series (increment = cumulative total minus the previous one within each event date and grouping). Because cumulative totals can be revised downward, an increment can be negative. This fixes autoplot() (and the other delay diagnostics) on count-cumulative data such as FluSight, which previously errored with “Transformation from data_type count-cumulative to count-incidence not implemented” (#26).

  • Updated SKILL.md (the AI-agent usage guide) to cover everything added since 0.10.0: reporting-delay autoplot() panels and the panels / by_strata selectors, plot_delay_drift() / test_delay_drift() / test_delay_changepoint(), the model-free batch detectors (batch_test(), batch_shape_test(), simulate_batch()), get_nth_reported_cases(), the after-holiday/weekend temporal-effect lags, as_tibble() / as.data.frame() coercion, and the new count-cumulative -> count-incidence support.

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_test() 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).