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Changelog

Source: NEWS.md

exuber 2.0.0

This release adds new methods from the docs/ research programme. We checked each one against a published number, either by a formula-exact check, a table lookup or a direct Monte Carlo reproduction of a theorem in the source paper. docs/README.md records what was checked and how.

Critical values

  • Default critical values now come from a shared precomputed store and no longer from the bundled radf_crit dataset. The store covers lags 0 to 4 and every n from the smallest the PSY window allows up to 4000, with 2000 replications each. When you call summary(), datestamp() or autoplot() on a radf() result, the one (n, lag) table it needs is fetched on first use and cached on disk (tools::R_user_dir("exuber", "cache")). A lagged specification therefore no longer requires you to simulate your own cv. The tables are nested, which means that every n of a lag is built from the same seeded paths. The values differ from the old bundled ones by Monte Carlo noise. The first use of a given (n, lag) needs network access. radf_mc_cv() and radf_wb_cv() remain available offline.
  • radf_crit, the bundled lag-0 table for n <= 600, is removed together with its print.crit method and data-raw/sim-crit.R. The simulation now lives in the sibling exubercrit repository.

Volatility-robust tests

  • radf_sbz(), radf_sbz_cv() and radf_sbz_union() implement the WLS/kernel-volatility SBZ test of Herwartz & Siedenburg. The test is split into a statistic (radf_sbz()), its bootstrap critical values (radf_sbz_cv(), with full datestamp() and autoplot() support) and the union-of-rejections test against the classic supDF (radf_sbz_union()). The union test was the original bundled radf_sbz_cv() and has been renamed. See vignette("volatility-robust-radf").
  • radf_kp() is a kernel-purge heteroskedasticity test.
  • radf_wb_cv(..., dist_skew = TRUE) is the skewness-corrected wild bootstrap of Hafner (2020).
  • radf_sign() and radf_sign_cv() implement the sign-based sGSADF of Harvey, Leybourne & Zu (2020), which is invariant to volatility and needs no bootstrap.
  • ssu_test() implements the stochastic explosive-coefficient tests of Kurozumi & Nishi (2025): SSU, and GSSU (type = "gssu", the double recursion over window starts). With union = TRUE it adds the union of rejections of the paper (UR/GUR) with SADF/GSADF. All critical values come from Table I of the paper.
  • cusum_test() implements the retrospective CUSUM tests of Kurozumi & Nishi (2025) ("cs", "gcs") and the two-sided CUSUM-of-squares tests ("cssq", "gcssq").
  • datestamp(..., option = "svadf") implements the SV-ADF asymmetric-threshold dating of Sarkar & Wells (2026). We added it as an option of datestamp() and not as a separate radf_svadf() function. The source is a preprint that has not been peer reviewed. This caveat is shown when the function is called and in the Caveats section of ?datestamp.

Dating and root inference

  • dating_pdc() implements the PDC/KS sequential sample-splitting dating. type = "wls" adds the correction of Kurozumi & Skrobotov (2023) for time-varying volatility.
  • radf_recovery() and radf_recovery_cv() implement the reverse-regression dating of crisis origination and recovery of Phillips & Shi (2014). f_c and the overall false-detection rate are exploratory until they are validated further. This is flagged when the function is called and in ?radf_recovery.
  • dating_hls() implements the SSR/BIC single-bubble dating of Harvey, Leybourne & Sollis (2017).
  • dating_hlw() implements the two-step SSR/BIC multi-bubble wrapper of Harvey, Leybourne & Whitehouse (2020), including the run-joining rule of the paper for fragmented step-1 detections (join = 3; 0 disables it).
  • dating_knp() implements the bias-corrected dating of Kejriwal, Nguyen & Perron (2025). The breaks argument dates several bubbles at once with the dynamic programme of the paper, which finds the exact global minimizer in O(breaks * n^2).

Real-time monitoring

  • monitor() implements the training and monitoring procedure of Phillips & Shi (2020) (Family A). It adds the closed-form SADF and GSADF_s0 boundaries of Kurozumi (2020) and the FLUC boundary of Homm & Breitung (2012).
  • monitor_cusum() implements the CUSUM monitoring of Homm & Breitung (2012). It adds the volatility-robust CUSUMV kernel variant of Astill et al. (2023) and the finite-sample boundary of Homm & Breitung.
  • lbi_test() and monitor_lbi() implement the static LBI test of Breitung & Diegel (2025) and its sequential mCUSUM/wCUSUM extension.

Multivariate and panel tests

  • radf_common() and radf_common_cv() implement the common-bubble detection of Chen, Phillips & Shi (PCA followed by PSY).
  • cobubble_test() implements the co-explosivity test of Evripidou, Harvey, Leybourne & Sollis (2022).
  • contagion_reg() implements the bubble contagion regression of Greenaway-McGrevy & Phillips (2016), as a minimal subset of the paper.

Alternative paradigms

  • quantile_test() implements the quantile-based global test of Wu, Shi & Wu (2025).
  • monitor_quantile() implements the recursive quantile monitoring of Wu, Shi & Wu (2025): QPWY (expanding window) and QPSY (type = "qpsy", supremum over window starts). The default boundary is the asymptotic one. It is well sized near the median, but away from the median it is oversized in small samples, and QPSY is badly oversized, with a false-alarm rate of 20% to 47% at a nominal 5% in our checks. A caveat message at call time says so. boundary = "bootstrap" applies Algorithm 1 of the paper to the whole path: it resamples the centred first differences, cumulates them and recomputes the QPWY or QPSY path, and it takes the quantile of the path maxima. It follows the finite-sample distribution of the statistic and brings the false-alarm rate back to about 5% in most of our checks, at the cost of one full statistic path for each replicate.

Naming

  • Twelve of the functions above were named radf_* in earlier development snapshots of this unreleased version: cobubble_test, contagion_reg, monitor_cusum, dating_hls, dating_hlw, dating_knp, lbi_test, monitor_lbi, dating_pdc, monitor_quantile, quantile_test and ssu_test. We renamed them before release because none of them is a recursive-ADF-based test. We kept no deprecated aliases, since the old names never shipped in a CRAN release.
  • radf_monitor() is renamed to monitor(), through a brief intermediate monitor_radf() that was never released. It is the flagship of the real-time monitoring family, alongside monitor_cusum(), monitor_lbi() and monitor_quantile(). The name deliberately carries no radf or sadf token, so that it cannot be mistaken for a radf_*() variant despite its ADF-family internals. See vignette("naming-and-analysis").
  • exuber_functions() is new. It is a queryable registry of the family of every exported function (adf, test, dating, monitor, root, regression), so that finding the monitoring functions is a function call and does not rely on a naming convention.
  • radf_wb_cv2() and radf_wb_distr2() are renamed to radf_wb_ps_cv() and radf_wb_ps_distr(). The 2 suffix meant only that this was the second wild bootstrap added. The _ps suffix identifies the wild bootstrap of Phillips & Shi (2020), which fits a null AR model, resamples the residuals and supports a tb training-window boundary. It differs from radf_wb_cv(), which uses the non-parametric multiplier bootstrap of Harvey et al. (2016). The new names match the internal naming in this file (radf_wb_dgp_ps and radf_wb_ps, against radf_wb_dgp_hlst and radf_wb_hlst) and the package-wide pattern radf_<method>_<qualifier>_cv, as in radf_sign_dm_cv(). Unlike the renames above, radf_wb_cv2() shipped in the 1.0.0 release (the JSS paper). For that reason radf_wb_cv2() and radf_wb_distr2() remain as deprecated aliases that warn and forward to the new functions (.Deprecated(), see ?exuber-deprecated).
  • radf_tt_cv(), radf_sign_cv() and radf_sign_dm_cv() now all compute badf_cv and bsadf_cv, a time-varying boundary, and not only the three scalar critical values. The results of radf_tt(), radf_sign() and radf_sign_dm() therefore work with the full summary(), datestamp(), tidy() and autoplot() pipeline, and no longer only with summary() and tidy(). We found the problem first in radf_tt_cv(), after a user reported a datestamp() crash on a radf_sign() result. That report led us to check all three functions of the GLS-demeaned family, and the same fix applied to the other two. We validated each function in three ways. The last row of badf_cv is bit-identical to adf_cv, which is an exact identity. The empirical false-alarm rate is at or below the nominal 5% (radf_tt 3.3%, radf_sign 5.5%, radf_sign_dm 3.5%). The detection power on an identical synthetic bubble is in the same range as the 16% of the established radf() and radf_mc_cv() baseline: radf_tt 18%, radf_sign 20% and radf_sign_dm 8%. The lower power of radf_sign_dm is an expected property of the sign-based tests, which pay for their invariance to heteroskedasticity, and it is not a validation concern. See vignette("naming-and-analysis").

Performance

  • radf() is about 25 times faster on typical sample sizes (exubercore v0.3.1). The recursive grid used to re-form the full residual vector for every window, which cost O(n^3) in total. It now keeps running cross-products and uses the closed-form SSR = y'y - b'X'y, which costs O(n^2). At n = 400 the time per path drops from about 140 ms to about 6 ms. radf_mc_cv(), radf_wb_cv(), radf_sb_cv(), monitor(), dating_hlw(), radf_recovery() and every other loop over radf() speed up by the same factor. The regression is now parameterized as dy on (1, y_{t-1}, dy lags), which gives the same t-statistic on beta - 1. It is also more accurate than before at large n with large levels: the difference from lm() is about 3e-11 at n = 2000, where the old recursion gave about 2e-8.
  • Parallel runs (options(exuber.parallel = TRUE)) now reuse one worker cluster per session. Before, every call started and stopped a fresh future::multisession, and the start-up time of a few seconds dominated every small radf_mc_cv() or radf_wb_cv() job. exuber.ncores sets the size of the cluster, and the cluster is stopped when the namespace unloads. exuber.parallel now defaults to interactive(). Scripts, knitr and R CMD check therefore run serially unless they opt in, and a batch job no longer pays for worker start-up or leaves worker connections open for a handful of replications.
  • The panel statistic of radf() used apply(bsadf, 1, mean). The dispatch overhead of that call grows with the number of rows. It was 65 times slower than the equivalent rowMeans(bsadf) at n = 100 and 289 times slower at n = 1000, and it was the largest single part of the runtime of radf() at every sample size we tested. We replaced it with rowMeans(bsadf), which gives an identical result. tests/testthat is unchanged, with 879 tests passing.

API consistency

  • The package now uses one significance-level convention. Every function that took a level argument now takes sig_lvl on the 0 to 100 scale that datestamp() and autoplot() already used, where sig_lvl = 95 means a 5% test or 95% confidence. The affected functions were all unreleased, so we added no shims. They are lbi_test() and monitor_lbi() (0.95 becomes 95, 0.975 becomes 97.5, and so on), ssu_test(), monitor(), monitor_cusum(), rootstamp() (which took a confidence level such as 0.95), quantile_test() and monitor_quantile() (already on the 0 to 100 scale and only renamed), and cobubble_test() (which took a size, level = 0.05, and now takes sig_lvl = 95). A shared assert_sig_lvl() makes sig_lvl = 0.95 an immediate error everywhere, so it can no longer produce a wrong quantile silently.
  • monitor(adflag = ) is now lag, matching radf(). monitor_cusum(N = ) is now h, matching every other kernel-bandwidth argument. cobubble_test(lags = ) is now lag_grid, so that lag and lags cannot be confused. This mirrors tau and tau_grid in quantile_test().
  • cobubble_test() and radf_sbz_union() now return cobubble_test_obj and radf_sbz_union_obj, which have the _obj suffix that every other standalone class already carried.
  • dating_pdc() and radf_sbz() have their own print() methods. The former fell through to print.data.frame, which hid its attributes. The latter printed as a plain radf.
  • scale_exuber_manual(size_values = ) is deprecated in favor of linewidth_values, because the linewidth aesthetic of ggplot2 (3.4.0 and later) replaces size for lines. The deprecation warning that ggplot2 emitted from every autoplot() call is gone. autoplot(include_negative = ), deprecated since 1.0.0, is now forwarded to nonrejected and no longer ignored.
  • radf_tt(), radf_tt_cv() and monitor_quantile() carry the same experimental badge as the other new methods. The note on every standalone function that it does not plug into autoplot was wrong, because each has had its own autoplot() method. The note now says so.

Other

  • rootstamp() gives a confidence interval and a doubling time for the explosive root, using S3 dispatch. The default method fits a single sub-sample, and the radf_obj method runs every datestamp() episode at once. It consolidates three separate functions, explosive_root(), root_ci() and root_ci_datestamp(), before release.

  • Every autoplot() and augment() method now has its own help page and no longer shares one with the function it plots or tidies (?autoplot.monitor_cusum_obj, ?augment.radf_obj and so on). The pkgdown reference index is reorganized into subsections for each function, so that each function is listed next to the methods that consume its output.

  • sim_vol_break() is new. It generates i.i.d. Gaussian innovations whose standard deviation shifts permanently at a chosen break fraction. This is the non-stationary volatility process of Cavaliere & Taylor (2007), which the volatility-robust tests are built for, and stationary GARCH is not. sim_ps1() gained the same e argument for injecting innovations that sim_psy1() already had. The c, c1 and c2 arguments of sim_psy1() and sim_ps1() now accept any positive scalar, as documented, so a fixed explosive root can be set directly (c = 0.04, alpha = 0).

  • Examples and vignettes now demonstrate each method on the data generating process it targets and no longer use sim_data throughout. The volatility-robust tests (radf_tt(), radf_kp(), radf_sign(), radf_sbz(), radf_wb_cv(), monitor_cusum(type = "kernel") and dating_pdc(type = "wls")) run on a volatility break. cobubble_test() and contagion_reg() run on sim_coexplosive(), radf_common() on sim_common(), ssu_test() on a stochastic root, and quantile_test() and monitor_quantile() on heavy-tailed innovations. dating_*() and radf_recovery() run on sim_ps1(), and every monitor runs on a bubble that starts after its training window. The vignettes now use the generators of the package in place of hand-written cumsum(rnorm()) processes.

Bug fixes

  • datestamp(), autoplot(), augment() and augment_join() no longer fail for a sieve-bootstrap cv with lag > 0. The truncation offset kept a + 2 that compensated for the old off-by-one in radf_sb_cv() (also fixed in this release), so it over-padded by two rows.

  • The panel critical values from radf_sb_cv() and radf_sb_distr() were wrong in every release since 0.1.0. The bootstrap loop overwrote the BSADF path of each series instead of summing it, so the panel null distribution was the BSADF of the last series divided by the number of series, and not the cross-sectional mean. The error is invisible for panels of about five series, where the two quantities happen to coincide. For narrow panels the test was oversized (8% at a nominal 5% for 2 series), and for wide panels it had no power (0% rejection under H0 and under the alternative for 10 series). This is fixed, and the empirical size is now 6%, 5% and 2.5% for 2, 5 and 10 series. A regression test pins the identity that a panel of identical copies has the same bootstrap distribution as the single series.

  • radf_sb_cv() and radf_sb_distr() silently truncated their output by 2 rows for any lag >= 1. This covers a fixed lag and also type = "aic" or "bic" whenever the selected lag was nonzero, which is the usual case. The indexing initmat[j, lag:1] in the bootstrap process was one column short of the lag + 1 values that dy_boot needs prepended. The drop rule for index 0 in R made it correct by accident at lag = 0 only. It is fixed (initmat[j, (lag + 1):1]), and bsadf_panel_cv and gsadf_panel_cv now have the documented nr - minw - lag rows for every lag. A regression test pins the row count across lag = 0:2.

  • The sig_lvl argument of datestamp(), autoplot() and autoplot2() now controls whether a series counts as rejecting the null. Before, diagnostics.radf_obj(), which decides internally which series get dated or plotted, used the 95% critical value for that decision whatever sig_lvl was. A call such as datestamp(x, cv, sig_lvl = 90) could therefore stop with "Cannot reject H0 at the 5% significance level" for a series that clearly rejects at the 10% level the caller asked for. sig_lvl only ever reached the episode threshold curve within a series and never the gate that decides which series are eligible. diagnostics() gained a sig_lvl argument, with a default of 95 so that default calls behave as before, and datestamp(), autoplot() and autoplot2() now pass their own sig_lvl to it.

  • radf() and everything built on it failed with subscript out of bounds on a named numeric vector, for example a prcomp() score column, which is what radf_common() passes to it. The names leaked into the internal bookkeeping of NA edges.

  • The range check for the default cv said that the store covers n <= 5000. It covers n <= 4000 and lag <= 4, and the message now says so before the function tries the network.

  • The seed argument of sim_psy1() now also covers a generator passed lazily to e or coef_noise, for example sim_psy1(n, seed = 1, e = sim_vol_break(n - 1)). Before, those arguments were evaluated before the seed was set.

exuber 1.1.0

CRAN release: 2025-08-31

  • Fixed targets not in the package itself nor in the base packages to use package anchors, i.e., use

exuber 1.0.1

CRAN release: 2023-02-12

Maintenance release to accommodate breaking changes in dplyr 1.1.0.

exuber 1.0.0

CRAN release: 2022-08-19

This first major release accompanies the publication of an article in the Journal of Statistical Software:

Vasilopoulos, K., Pavlidis, E., & Martínez-García, E. (2022). exuber: Recursive Right-Tailed Unit Root Testing with R. Journal of Statistical Software, 103(1), 1–26. https://doi.org/10.18637/jss.v103.i10

augment method for radf_obj and radf_cv

  • New arg trunc

  • Fixed inconsistencies among functions.

  • Now radf stores the data that are later can be accessed with mat+

  • Advanced features on datestamping: New columns that indicate:

    • Signal
    • Peak
    • Ongoing
    • Nonrejected

  • New datestamping procedure rev_radf etc.

  • New bootstrap procedure radf_wb_cv2 and radf_wb_distr2

  • New coloring convention for plotting ds and obj classes

Bug Fixes

  • Now autoplot can include periods that have an ongoing bubble

exuber 0.4.2

CRAN release: 2020-12-18

  • Include printing methods for radf_obj and radf_cv.
  • Removed unused class definitions.
  • Using progress package for progress_bar.

exuber 0.4.1

CRAN release: 2020-05-12

Maintenance release for compatibility with dplyr v1.0.0.

exuber 0.4.0

CRAN release: 2020-05-04

Design

We have the following design in mind for future scalability. If you want make inference about radf models, then the estimation can be achieved with radf() function and return an object of class radf_obj, and the critical values can be achieved with radf_*_cv() and return an object of class radf_cv.

Breaking changes

exuberdata

  • We created a new package called exuberdata that accommodates critical values for up to 2000 observations. Critical values can be examined with exuberdata::radf_crit2. The package is created through drat R archive Template, and can be easily installed with install.packages('exuberdata', repos = 'https://kvasilopoulos.github.io/drat/', type = 'source') or through install_exuberdata wrapper function that is provided in exuber.

Improvements

  • The package zoo has been used as a dependency to import the method index(). We made the decision to remove zoo and create a new method index() internally.

exuber 0.3.0

CRAN release: 2019-07-15

Breaking changes

  • Changed opt_bsadf = conservative for the simulated critical values (crit), also reduced the size of the crit from 700 to 600 due to package size restrictions.
  • sim_dgp1() and sim_dgp2() have been renamed to sim_psy1() and sim_psy2() to better describe the origination of the dgp.
  • sim_dgp1() and sim_dgp2() have been soft-deprecated.
  • autoplot_radf() arranges automatically multiple graphs, to return to previous behavior we included the optional argument arrange which is set to TRUE by default.

Three new functions have been added to simulate empirical distributions for:

  • mc_dist(): Monte Carlo
  • wb_dist(): Wild Bootstrap
  • sb_dist(): Sieve Bootstrap

and a function that can calculate the p-values calc_pvalue() given the above distributions as argument.

Also methods tidy() and autoplot() have been added to turn the object into a tidy tibble and draw a particular plot with ggplot2, respectively.

New features

  • tidy() methods for objects of class radf, cv.
  • augment() methods for objects of class radf and cv.
  • augment_join() to combine object radf and cv into a single data.frame.
  • glance() method for objects of class radf.

Improvements

  • New printing output for the functions summary(), diagnostics() and datestamp().
  • New improved progressbar with more succinct printing for wb_cv()
  • seed argument to functions that are using rng. Also the option to declare a global seed for reproducibility with the option(exuber.global_seed = ###)

Bug Fixes

  • sb_cv() and wb_cv()now can parse data that contain a date-column. Similarly, to what radf() is doing.

exuber 0.2.1.9000

  • Website development

exuber 0.2.1

CRAN release: 2019-03-01

  • Changed DESCRIPTION to include sb_cv reference.
  • Renamed boolean to dummy from datestamp and diagnostics.
  • datestamp dummy is now an attribute.

exuber 0.2.0

CRAN release: 2019-02-04

Options

Some of the arguments in the functions were included as options, you can set the package options with e.g. options(exuber.show_progress = TRUE).

  • parallel option boolean, allows for parallel in critical values computation.
  • ncores option numeric, sets the number of cores, defaults to max - 1.
  • show_progress option boolean, allows you to disable the progress bar, defaults to TRUE.

New features

  • Panel estimation in radf()
  • Added sb_cv() function: Panel Sieve Bootstrapped critical values
  • Default critical values are supplied directly into summary(), diagnostics, datestamp() and autoplot(), without having to specify argument cv. The critical values have been simulated from mc_cv() function and stored as data. Custom critical values should be provided by the user with the option cv.
  • Added ggarrange() function, that can arrange a list of ggplot objects into a single grob.
  • Added fortify to arrange a data.frame from radf() function.

Improvements

  • Parallel and ncores arguments are now set as options.
  • Ability to remove progressbar from package options.
  • radf() can parse date from ts objects.
  • report() has been renamed into summary().
  • plot() has been renamed into autoplot().
  • plot() and report() are soft deprecated.

Bug Fixes

  • Progressbar appears in the beginning of the iteration
  • Plotting date now works without having to to include any additional plotting option