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Changelog

Source: NEWS.md

projpred 2.9.1

CRAN release: 2025-10-28

Major changes

  • Fixed a major bug in forward search with 3-way or higher-order interactions. See “Bug fixes” below for details.

Bug fixes

  • Fixed a bug that caused forward search not to place lower-order terms of a 3-way or higher-order interaction term before that interaction term. (GitHub: #531, #532)
  • Fixed a bug that caused project() to construct an incorrect internal table of predictor terms (from which argument predictor_terms can select one or several terms from) in case of a group-level term that does not contain a group-level intercept. (GitHub: #533, #534)
  • Relaxed test to prevent spurious failures and unblock reverse dependencies. No functional changes.

projpred 2.9.0

CRAN release: 2025-07-08

Major changes

  • Subsampled PSIS-LOO CV (usable via argument nloo of cv_varsel()) has been fixed and is not experimental anymore. There are a few restrictions: Performance statistic "auc" (see argument stats of summary.vsel() and plot.vsel(); argument stat of suggest_size() is concerned as well) is not supported in case of subsampled PSIS-LOO CV. Furthermore, baseline = "best" (in summary.vsel() and plot.vsel()) is not supported in case of subsampled PSIS-LOO CV either. (GitHub: #94, #496)

  • The uncertainty interval for performance statistic "mse" is now based on a log-normal approximation (instead of a normal approximation) if argument deltas of summary.vsel() or plot.vsel() is FALSE. (GitHub: #496)

  • The standard error for performance statistic "rmse" is now computed via the delta method (instead of bootstrapping). The uncertainty interval for "rmse" is now based on a log-normal approximation (instead of bootstrapping) if argument deltas of summary.vsel() or plot.vsel() is FALSE and based on a normal approximation (instead of bootstrapping) if deltas is TRUE. (GitHub: #496)

  • Performance statistic "R2" (R-squared) has been added, see argument stats of summary.vsel() and plot.vsel(); argument stat of suggest_size() supports it as well. (GitHub: #483, #496)

  • The performance evaluation part of cv_varsel() with cv_method = "LOO" and validate_search = FALSE now always applies Pareto smoothing when computing the importance sampling weights (as long as the number of importance ratios in the tail is large enough; otherwise, no Pareto smoothing is applied). Previously, in case of projected draws with nonconstant weights (i.e., in case of clustering), no Pareto smoothing had been applied. (GitHub: #496, #507)

  • The threshold for high Pareto-\(\hat{k}\) values was updated to the one presented by Vehtari et al. (2024, “Pareto smoothed importance sampling”, Journal of Machine Learning Research, 25(72):1-58, https://www.jmlr.org/papers/v25/19-556.html). This threshold depends on the Monte Carlo sample size and is often close to the former fixed threshold of 0.7 (a short introduction may also be found in the LOO glossary). Correspondingly, the former “secondary” threshold of 0.5 is not used anymore either. (GitHub: #490, #498)

  • Argument type of summary.vsel() has gained options "diff.lower" and "diff.upper" (see the documentation for details). (GitHub: #511)

  • Argument deltas of plot.vsel() has gained option "mixed" which combines the point estimates from deltas = FALSE with the uncertainty bars from deltas = TRUE. (GitHub: #511)

  • For the latent projection, the function passed to argument latent_ll_oscale of extend_family() now needs to have an argument dis (at the second position). Similarly, the function passed to argument latent_ppd_oscale of extend_family() now needs to have an argument dis_resamp (again at the second position). This makes it possible, e.g., to use the latent projection for a log-normal response family. (GitHub: #513)

  • Argument verbose of project(), varsel(), and cv_varsel() has been changed from logical to integer. However, logical values continue to work (since as.integer() is applied internally). Global options projpred.extra_verbose and projpred.verbose_project are now deprecated because additional verbosity can be achieved via higher integer values for argument verbose. The new global option projpred.verbose may be used to set argument verbose of project(), varsel(), and cv_varsel() globally. (GitHub: #519)

  • Some global options have been renamed, so please use their new names from now on (although the old names will continue to work for a while) (GitHub: #500, #521):

    • Global option projpred.prll_cv has been renamed to projpred.parallel_cv.
    • Global option projpred.warn_prj_drawwise has been renamed to projpred.warn_proj_drawwise.
    • Global option projpred.check_conv has been renamed to projpred.check_convergence.
    • Global option projpred.prll_prj_trigger has been renamed to projpred.parallel_proj_trigger.

  • In plot.vsel(), several defaults have been changed (GitHub: #517, #522):

    • Argument text_angle now defaults to 45 (previously, the default was NULL).
    • Argument size_position now defaults to "primary_x_top" (previously, the default was "primary_x_bottom").
    • Argument show_cv_proportions now defaults to FALSE (previously, the default was TRUE).

    These arguments can now also be controlled via global options, see section “Usage” of ?plot.vsel for their names and the main vignette for an illustration.

  • The changelog for version 2.6.0 did not contain a notification that cvfolds() had been deprecated and that the new name cv_folds() should be used instead. This changelog entry has been added now (see below), but is also mentioned here to make users aware of it (although a deprecation warning was already added in version 2.6.0 and will be kept until cvfolds() is eventually removed in a future release). (GitHub: #411)

Minor changes

  • When using the doFuture backend for parallelization, progression updates can now be received via the progressr package, see ?`projpred-package` (section “Parallelization”). (GitHub: #504)
  • Several enhancements concerning verbosity, e.g., the number of projected draws (resulting from clustering or thinning) is now printed out during the different steps of the computations and verbose-mode output is redirected to stderr() instead of stdout(). (GitHub: #506, #518)
  • For the CV parallelization (see argument parallel of cv_varsel()), a new global option projpred.export_to_workers may be set to a character vector of names of objects to export from the global environment to the parallel workers. (GitHub: #497, #510)
  • Added global options projpred.foreach_errorhandling and projpred.foreach_verbose whose values are passed to foreach::foreach()’s arguments .errorhandling and .verbose, respectively. The defaults for these new global options are the same as those for the respective foreach::foreach() arguments: "stop" for global option projpred.foreach_errorhandling and FALSE for global option projpred.foreach_verbose. (GitHub: commit 3231d13)
  • Added global options to control several arguments of plot.vsel() and plot.cv_proportions() (see section “Usage” of the help pages of these two functions). (GitHub: commit 3333043)
  • Changed the maintainer to Osvaldo Martin.

Bug fixes

  • Fixed a bug that caused an error when using the augmented-data or latent projection in combination with a single projected draw for performance evaluation in cv_varsel() with cv_method = "LOO" and validate_search = FALSE. (GitHub: #512)
  • Previously, in case of PSIS-LOO CV with validate_search = TRUE and thinned posterior draws for projection (i.e., argument(s) ndraws or ndraws_pred being used, not nclusters or nclusters_pred), print.vselsummary() incorrectly reported that the posterior draws had been clustered. This has now been fixed, so thinning is reported in such cases. (GitHub: #516)
  • Fixed the internal default extract_model_data function when using the latent projection for a custom reference model object. (GitHub: #523)

projpred 2.8.0

CRAN release: 2023-12-14

Major changes

  • Search results generated in an earlier varsel() or cv_varsel() call can now be re-used by the help of the new varsel.vsel() and cv_varsel.vsel() methods (i.e., by applying varsel() or cv_varsel() to the output of the earlier varsel() or cv_varsel() call). This can save a lot of time when re-running the predictive performance evaluation part multiple times based on the same search results. An illustration may be found in the updated main vignette (section “Preliminary cv_varsel() run”; a more general description may also be found in section “Speed”). (GitHub: #461, #463, #465, #466)
  • K-fold CV can now be combined with validate_search = FALSE. Related to this is an internal change which may cause subsampled PSIS-LOO CV (an experimental feature controlled by argument nloo of cv_varsel()) with clustered projection during the search (i.e., 1 < nclusters && nclusters < S, where S denotes the number of posterior draws in the reference model) to yield slightly different results due to different internal pseudorandom number generator (PRNG) states. Furthermore, if is.na(seed), then the PRNG state for code downstream of such a cv_varsel() call will be different due to this internal change. (GitHub: #464)
  • print.vselsummary() (and hence also print.vsel()) now prints the reference model’s performance evaluation results as well (not just those of the submodels). Correspondingly, a new helper function performances() has been added which allows to access the reference model’s (as well as the submodels’) performance evaluation results. (GitHub: #471)
  • Argument solution_terms of project() has been deprecated. Please use the new argument predictor_terms instead. (GitHub: #472)
  • For expert users of the augmented-data projection only: Objects of class augmat or augvec do not need to have an attribute called nobs_orig anymore, but a new attribute called ndiscrete, giving the number of (possibly latent) response categories instead of the number of observations (see ?`augdat-internals`). This simplifies the subsetting of such objects. (GitHub: #473)
  • By default, projpred now catches messages and warnings from the draw-wise divergence minimizers and throws their unique collection after performing all draw-wise divergence minimizations (i.e., draw-wise projections). This can be deactivated by setting global option projpred.warn_prj_drawwise to FALSE. Previously, projpred suppressed such messages and warnings. (GitHub: #478)
  • By default, projpred now checks the convergence of the draw-wise divergence minimizers and throws a warning in case of potential convergence problems. This can be deactivated by setting global option projpred.check_conv to FALSE. (GitHub: #478)

Minor changes

  • In as.matrix.projection(), nm_scheme = "auto" is deprecated. Please use nm_scheme = NULL instead.
  • The plot produced by plot.vsel() now includes a title and a subtitle, with the subtitle mentioning the nominal coverage as well as the type of the confidence intervals (CIs) explicitly. However, in case of a facetted plot (i.e., in case of multiple stats) and some stats implying a different CI type than other stats, the CI types are omitted (because mentioning them would make the subtitle too complicated). Note that title and subtitle can always be omitted with <plot.vsel() output object> + ggplot2::labs(title = NULL, subtitle = NULL). (GitHub: #468)
  • plot.vsel() has gained a new argument show_cv_proportions, allowing to omit the CV ranking proportions. (GitHub: #470)
  • Renamed summary.vsel()’s output element selection to perf_sub and made the names of this data.frame’s columns more consistent so that it is easier to handle that data.frame programmatically. This should not be a breaking change because elements of vselsummary objects (i.e., elements of objects returned by summary.vsel()) are not meant to be accessed directly (for elements perf_sub and perf_ref, the new helper function performances() has been added, see “Major changes” above). (GitHub: #471)
  • In summary.vsel() and plot.vsel(), the NA_character_ “string” (which was previously used as a placeholder for the predictor term of the intercept-only model at size 0) was replaced by the string "(Intercept)". (GitHub: #471)
  • Renamed project()’s output element solution_terms to predictor_terms. This should not be a breaking change because that element is meant to be accessed via predictor_terms(). (GitHub: #472)
  • Renamed elements solution_terms and solution_terms_cv of vsel objects (returned by varsel() and cv_varsel()) to predictor_ranking and predictor_ranking_cv, respectively. This should not be a breaking change because those elements are meant to be accessed via ranking(). (GitHub: #472)
  • Global option projpred.verbose_project now affects the verbosity of all projections performed by the built-in divergence minimizers (except for the built-in L1-projection divergence minimizer). In particular, the divergence minimizer (no matter whether built-in or user-specified) is also employed when calling varsel() or cv_varsel(), so setting option projpred.verbose_project to TRUE now shows the progress of the projections during a varsel() or cv_varsel() call. Previously, that option only affected the projections performed through project() (see the default for project()’s argument verbose). Usually, setting projpred.verbose_project to TRUE only makes sense when setting global option projpred.extra_verbose and argument verbose (of varsel() or cv_varsel()) to TRUE as well.
  • Added print() methods for objects of class refmodel and projection, mainly to avoid cluttering the console when printing such objects accidentally.
  • Argument extract_model_data of init_refmodel() is now allowed to be NULL for using an internal default.
  • print.vselsummary() and print.vsel() now use a minimum number of significant digits of 2 by default. The previous behavior can be restored by setting options(projpred.digits = getOption("digits")).
  • Added a new performance statistic, the geometric mean predictive density (GMPD). This is particularly useful for discrete outcomes because there, the GMPD is a geometric mean of probabilities and hence bounded by zero and one. For details, see argument stats of the ?summary.vsel help. (GitHub: #476)
  • project()’s argument verbose now gets passed to argument verbose_divmin (not projpred_verbose) of the divergence minimizer function (see argument div_minimizer of init_refmodel()).
  • Arguments lambda_min_ratio, nlambda, and thresh of varsel() and cv_varsel() have been deprecated. Instead, varsel() and cv_varsel() have gained a new argument called search_control which accepts control arguments for the search as a list. Thus, former arguments lambda_min_ratio, nlambda, and thresh should now be specified via search_control (but note that search_control is more general because it also accepts control arguments for a forward search). (GitHub: #477)
  • run_cvfun() has gained a new argument folds, accepting a vector of fold indices (the default is NULL, meaning that the folds are constructed internally, as before). This new argument is helpful, for example, to perform a stratified K-fold CV in a convenient manner (an example of this has been added to the ?run_cvfun help). (GitHub: #480)
  • plot.vsel() has gained a new argument size_position. Setting it to "primary_x_top" moves the text for the submodel sizes above the x-axis. Setting it to "secondary_x" moves that text into a secondary x-axis located at the top of the plot. (GitHub: #484)
  • The default alignments of the x-axis text in plot.vsel() have been changed: x-axis text is now right-aligned (left-aligned) for text_angle > 0 (< 0) and also top-aligned for -90 < text_angle && text_angle < 90 && text_angle != 0. We emphasize that alignments can always be customized with <plot.vsel() output object> + ggplot2::theme(axis.text.x.bottom = ggplot2::element_text(hjust = <hjust_value>, vjust = <vjust_value>)). (GitHub: #484)

Bug fixes

  • Fixed a bug sometimes causing plot.vsel() to produce extra (“empty”) ticks on the x-axis. (GitHub: #462)
  • Fixed a bug in summary.vsel() and plot.vsel() causing bootstrap results (i.e., standard error and confidence interval for RMSE and AUC) to be incorrect if deltas = TRUE. (GitHub: #474)
  • Fixed several bugs in summary.vsel() and plot.vsel() sometimes causing incorrect predictive performance results in case of subsampled PSIS-LOO CV (an experimental feature controlled by argument nloo of cv_varsel()). (GitHub: #475)
  • Fixed backward compatibility for the legacy structure of cvfits (the new structure was introduced by version 2.7.0, see GitHub pull request #456).

projpred 2.7.0

CRAN release: 2023-09-30

Major changes

  • The default search method is now "forward" search for all kinds of models (previously, "L1" search was used by default where available). The reason for this change is that in general, forward search is more favorable compared to L1 search (see section “Details” in ?varsel or ?cv_varsel). (GitHub: #453, #459)

  • Several enhancements with respect to projected draws with different (i.e., nonconstant) weights, which typically occurs in case of clustered projection (GitHub: #206, #439):

    • as.matrix.projection() now throws an error if the projected draws have nonconstant weights. (This error is the default behavior; it can be avoided by setting the new argument allow_nonconst_wdraws_prj to TRUE, but this is for expert use only because in that case, the weights of the projected draws are stored in an attribute wdraws_prj and handling this attribute requires special care, e.g., when subsetting the returned matrix.) Instead, a posterior::as_draws_matrix() method (as_draws_matrix.projection()) has been added which allows for a safer handling of these weights (e.g., with the help of posterior::resample_draws(), see section “Examples” of the ?as_draws_matrix.projection help). Just like as.matrix.projection(), as_draws_matrix.projection() also works for the more common case of projected draws with constant weights. A posterior::as_draws() method (as_draws.projection()) has also been added, but this is merely a wrapper for as_draws_matrix.projection().
    • proj_linpred() now also throws an error (by default) if the projected draws have nonconstant weights and has gained the new arguments allow_nonconst_wdraws_prj and return_draws_matrix. As in as.matrix.projection(), argument allow_nonconst_wdraws_prj is for expert use only. Instead, return_draws_matrix is the intended argument in case of projected draws with nonconstant weights. Similarly to as_draws_matrix.projection(), it requires the posterior package and returns a draws_matrix (with weighted draws if the projected draws have nonconstant weights and integrated is FALSE).
    • proj_predict() has gained an argument return_draws_matrix for converting the returned matrix to a draws_matrix (which again requires the posterior package). For proj_predict(), no further modifications were necessary because its argument nresample_clusters already takes weights of projected draws appropriately into account.

  • Added helper function run_cvfun() which can be used to create input for cv_varsel.refmodel()’s new argument cvfits (which is the same as init_refmodel()’s argument cvfits, but avoids having to call init_refmodel() or get_refmodel() twice). See the documentation of run_cvfun() for details. (GitHub: #458)

  • Users applying varsel() or cv_varsel() to an object of class vsel now need to use varsel(get_refmodel(<vsel_object>), <...>) or cv_varsel(get_refmodel(<vsel_object>), <...>) instead of varsel(<vsel_object>, <...>) and cv_varsel(<vsel_object>, <...>), respectively. The reason is that new methods varsel.vsel() and cv_varsel.vsel() have been added. Currently, these are only placeholders, but in a future release, they will offer new functionality.

Minor changes

  • If an L1 search selects an interaction term before all involved lower-order interaction terms (including main-effect terms) have been selected, the predictor ranking is now automatically modified so that the lower-order interaction terms come before this interaction term. A corresponding warning is thrown, which may be deactivated by setting the global option projpred.warn_L1_interactions to FALSE. Previously, beginning with version 2.5.0, only a warning was thrown and this only if an L1 search selected an interaction term before all involved main-effect terms had been selected. (GitHub: #420)

  • Added a progress bar for project() (when using the built-in divergence minimizers). For this, project() has gained a new argument verbose which can also be controlled via the global option projpred.verbose_project. By default, the new progress bar is activated. (GitHub: #421)

  • Added a new argument parallel to cv_varsel(). With parallel = TRUE, costly parts of projpred’s cross-validation (CV) can be run in parallel. See the documentation of that new argument (and section “Note” of cv_varsel()’s documentation) for details. (GitHub: #422)

  • Added a warning for issue #323 (for multilevel Gaussian models, the projection onto the full model can be instable). (GitHub: #426)

  • plot.vsel() has gained the new arguments point_size and bar_thickness which control the size of the points and the thickness of the uncertainty bars, respectively. By default, the points are slightly larger now and the uncertainty bars slightly thicker than before. The previous appearance can be achieved by setting point_size = 1.5 and bar_thickness = 0.5. (GitHub: #429, #443)

  • plot.vsel(): Added argument ranking_colored for coloring the points and the uncertainty bars according to the magnitude of the (possibly cumulated) CV ranking proportions. (GitHub: #430; thanks to @yannmclatchie for the suggestion)

  • Added warnings for most of the problems described in section “Troubleshooting” of the main vignette. (GitHub: #431)

  • Output element p_type of project() has been removed. Instead, output element const_wdraws_prj has been added, but its definition is essentially the inverse of former element p_type (see the updated documentation of project()’s output). This should not be a breaking change for users (as p_type was mainly intended for internal use and the new element const_wdraws_prj is so, too) but this slightly enhances the cases where as.matrix.projection() used to throw a warning (and now throws an error; see “Major changes” above) concerning the weights of the projected draws and the cases where proj_predict() resamples from the projected draws using argument nresample_clusters. (GitHub: #432)

  • Improved handling of PSIS-LOO CV warnings. (GitHub: #438, #451)

  • Reduced peak memory usage during forward search. A global option projpred.run_gc has also been added, see the general package documentation (available online or by typing ?`projpred-package`). (GitHub: #442)

  • Slightly improved efficiency in K-fold and PSIS-LOO CV, especially in case of a large number of observations. Under very special conditions (refit_prj = FALSE, 1 < nclusters && nclusters < S, and 1 < nclusters_pred && nclusters_pred < S; note that 1 < nclusters requires forward search, S denotes the number of posterior draws in the reference model, and nclusters_pred is essentially unused if refit_prj = FALSE), this change might affect K-fold CV results, due to a different pseudorandom number generator (PRNG) state in folds other than the first one. Under similarly special conditions (refit_prj = FALSE and 1 < nclusters_pred && nclusters_pred < S), the PRNG state for LOO subsampling (see argument nloo) is affected. Furthermore, if is.na(seed), then the PRNG state for code downstream of such cv_varsel() calls will be different due to this change. (GitHub: #446)

  • Slightly improved efficiency at the end of cv_varsel(), especially in case of a large number of observations. If is.na(seed), then the PRNG state for code downstream of a cv_varsel() call with refit_prj = TRUE and 1 < nclusters_pred && nclusters_pred < S (where S denotes the number of posterior draws in the reference model) will be different due to this change. (GitHub: #447)

  • Slightly improved memory usage in varsel(), cv_varsel(), and project(). In case of LOO subsampling (see argument nloo) with clustered projection (i.e., 1 < nclusters && nclusters < S or 1 < nclusters_pred && nclusters_pred < S, where S denotes the number of posterior draws in the reference model), this change may lead to slightly different results due to different internal PRNG states. Furthermore, if is.na(seed), then the PRNG state for code downstream of such a cv_varsel() call will be different due to this change. (GitHub: #448)

  • The internal function .extract_model_data has been removed. As an alternative (with some differences compared to .extract_model_data), the new function y_wobs_offs() is exported.

  • Fixes/enhancements with respect to observation weights and offsets (GitHub: #449):

    • In case of an rstanarm reference model, the defaults for arguments weightsnew and offsetnew (see proj_linpred(), proj_predict(), and predict.refmodel()) now cause the original observation weights and offsets to be used if possible (instead of ones and zeros, respectively, which could even be considered to have been a bug—hence why this is mentioned under “Bug fixes” as well). For brms reference models, this behavior had already been implemented before.
    • An error is now thrown if a length-zero element weights or offset is returned by the function supplied to argument extract_model_data of init_refmodel() (before, a vector of ones or zeros was used silently for the observation weights and offsets, respectively).

  • Added the helper function force_search_terms() which allows to construct search_terms where certain predictor terms are forced to be included (i.e., they are forced to be selected first) whereas other predictor terms are optional (i.e., they are subject to the variable selection, but only after the inclusion of the “forced” terms). (GitHub: #346)

  • Reduced peak memory usage during performance evaluation (more precisely, during the re-projections done for the performance evaluation). This reduction is considerable especially for multilevel submodels, but possibly also for additive submodels. (GitHub: #440, #450)

  • A message is now thrown when cutting off the search at nterms_max’s internal default of (currently) 19. (GitHub: #452)

  • Added sub-section “Speed” to the main vignette’s “Troubleshooting” section. (GitHub: #455)

  • In case of K-fold CV, the list passed to argument cvfits of init_refmodel() should not have a sub-list called fits anymore. Instead, the content of this former sub-list called fits should be moved one level up, i.e., should be placed directly in the list passed to cvfits (the empty element fits should then be removed). For some time, the old structure will continue to work, but this possibility is deprecated and will be removed in the future. (GitHub: #456)

  • In case of K-fold CV, the K reference model fits (i.e., the elements of the return value of the function passed to argument cvfun of init_refmodel() or the elements of the list supplied to argument cvfits of init_refmodel()) do not need to be lists anymore (see the documentation for argument cvrefbuilder of init_refmodel()). (GitHub: #457)

Bug fixes

  • Fixed a bug in the printed number of projected draws for the performance evaluation when calling print.vselsummary() based on output from varsel() with refit_prj = FALSE.
  • Fixed a bug sometimes causing an error when predicting from a submodel that is a GLM and has interactions. (GitHub: #420)
  • Fixed a bug introduced in version 2.6.0, causing an incompatibility of K-fold CV with R versions < 4.2.0. (GitHub: #423, #427)
  • Fixed a bug for the augmented-data projection in combination with subsampled PSIS-LOO CV. (GitHub: #433)
  • cv_varsel() with validate_search = FALSE used to call loo::psis() (for the submodel performance evaluation PSIS-LOO CV) even in case of draws with different (i.e., nonconstant) weights. In such cases, loo::sis() is called now (with a warning). (GitHub: #438)
  • Fixed a bug for rstanarm (and custom) multilevel reference models with interactions (: syntax) between grouping variables, caused by missing columns in the reference model’s data.frame (for brms reference models, this was already done correctly). (GitHub: #445)
  • In case of an rstanarm reference model, the defaults for arguments weightsnew and offsetnew (see proj_linpred(), proj_predict(), and predict.refmodel()) now cause the original observation weights and offsets to be used if possible (instead of ones and zeros, respectively, which could be considered to have been a bug). For brms reference models, this behavior had already been implemented before. (GitHub: #449)
  • Fixed a bug causing PSIS-LOO CV with validate_search = FALSE to fail in case of a single projected draw. (GitHub: #454)

projpred 2.6.0

CRAN release: 2023-06-01

Major changes

  • In anticipation of a larger overhaul of the projpred user interface, this release comes with several new functions for accessing and investigating solution paths (which are now termed predictor rankings by these new functions, a term that is hopefully easier to grasp for new users):

    Because of these new functions, a message has been added to print.vselsummary(), mentioning how to access and investigate the fold-wise predictor rankings (if they exist). Furthermore, due to these changes, element pct_solution_terms_cv of vsel objects has been replaced with element solution_terms_cv which contains the fold-wise predictor rankings instead of the corresponding ranking proportions. However, elements of vsel objects are not meant to be accessed directly, so this replacement should not be a breaking change for most users. Finally, method solution_terms.vsel() (which—until now—was the only possibility to extract the full-data predictor ranking) has now been deprecated and will be removed in a future release. Please use the new function ranking() instead (more precisely, ranking()’s output element fulldata contains the full-data predictor ranking that is also extracted by solution_terms.vsel(); ranking()’s output element foldwise contains the fold-wise predictor rankings—if available—which were previously not accessible via a built-in function). (GitHub: #289, #406, #411)

  • Added function predictor_terms() which retrieves the predictor terms used in a project() run. Correspondingly, method solution_terms.projection() has now been deprecated and will be removed in a future release. Please use predictor_terms() instead. (GitHub: #411)

  • Renamed function cvfolds() to cv_folds() (more precisely, the former variant still exists, but is deprecated and will be removed in a future release). (GitHub: #411)

  • seed (and .seed) arguments now have a default of NA instead of sample.int(.Machine$integer.max, 1) and the pseudorandom number generator (PRNG) state is reset only if the user-supplied seed is not NA. This allows setting a seed once at the beginning of any projpred-related code and then leaving all seed (and .seed) arguments at their default. Previously, such practice could lead to results which were “less random” than they should have been because the former default of sample.int(.Machine$integer.max, 1) caused projpred functions with a seed (or .seed) argument to reset the PRNG state upon exit, meaning that two repeated calls to cv_varsel() (for example) with no PRNG-using code between them would use the same seed internally. (GitHub: #412)

  • Added the main diagonal of the matrix returned by cv_proportions() to a new column called cv_proportions_diag of the summary table computed by summary.vsel(). The purpose of this new column is to give a basic sense for the (CV) variability in the ranking of the predictors. Argument cumulate of cv_proportions() has been added to summary.vsel() as well (to allow the ranking proportions in the newly added column to be cumulated ranking proportions, if desired). (GitHub: #289, #413)

  • Added the full-data predictor ranking and the main diagonal of the matrix returned by cv_proportions() to the plot created by plot.vsel(). These new elements can be omitted by setting plot.vsel()’s new argument ranking_nterms_max to NA (setting it to some specific submodel size causes the full-data predictor ranking and the corresponding ranking proportions to be omitted after that size). Argument cumulate of cv_proportions() has been added to plot.vsel() as well (to allow the ranking proportions to be cumulated ranking proportions, if desired). Other new arguments are ranking_abbreviate (together with ranking_abbreviate_args), ranking_repel (together with ranking_repel_args), and text_angle (see the plot.vsel() documentation for details). (GitHub: #289, #414, #416, #417)

Minor changes

  • Enhancements in the vignettes. In particular, the new functions ranking(), cv_proportions(), and plot.cv_proportions() (see “Major changes” above) are now illustrated in the main vignette. (GitHub: #407, #411)
  • Reduced the peak memory usage of cv_varsel() with cv_method = "kfold". This may slightly change results from such a cv_varsel() run compared to older projpred versions due to different pseudorandom number generator (PRNG) states when clustering posterior draws. (GitHub: #419)
  • The cvfits list (see init_refmodel()) does not need to have an attribute called K anymore.

Bug fixes

  • Fixed a bug causing L1 search to throw an error in case of some I() terms. (GitHub: #404, #408)
  • Fixed a bug causing L1 search to throw an error in case of poly() or polym() terms. Note that just like step() and MASS::stepAIC(), projpred’s search algorithms do not split up a poly() or polym() term into its lower-degree polynomial terms (which would be helpful, for example, if the linear part of a poly() term with degrees = 2 was relevant but the quadratic part not). Such a split-up of a poly() or polym() term needs to be performed manually (if desired). (GitHub: #183, #409)
  • Fixed a bug causing some non-smooth predictor terms to be treated as smooth terms. (GitHub: #182, #410)
  • See “Major changes” above: Fixed a bug causing projpred functions with a seed (or .seed) argument to use the same seed internally when users set a seed once at the beginning (via set.seed()) and then had two or more calls to such projpred functions with their seed (or .seed) argument being at its default and no PRNG-using code between those calls. (GitHub: #412)

projpred 2.5.0

CRAN release: 2023-04-05

Minor changes

  • Setting the new global option projpred.extra_verbose to TRUE will print out which submodel projpred is currently projecting onto. Furthermore, if method = "forward" and verbose = TRUE in varsel() or cv_varsel(), this new option will also make projpred print out which submodel has been selected at those steps of the forward search for which a percentage is printed (the percentage refers to the maximum submodel size that the search is run up to). In general, however, we cannot recommend setting this new global option to TRUE for cv_varsel() with validate_search = TRUE (simply due to the amount of information that will be printed, but also due to the progress bar which will not work anymore as intended). (GitHub: #363; thanks to @jtimonen)

  • Enhanced verbose output. In particular, varsel() is now more verbose, similarly to how cv_varsel() has already been for a long time. The verbose output for cv_varsel() has also been updated, with the aim to give users a better understanding of the methodology behind projpred. (GitHub: #382)

  • Slightly improved the calculation of predictive variances to make them less prone to numerical inaccuracies. (GitHub: #199)

  • Improved computational efficiency by avoiding an unnecessary final full-data performance evaluation (including costly re-projections if refit_prj = TRUE, which is the default for non-datafit reference models) in cv_varsel() with validate_search = TRUE. Due to this change, results from cv_varsel() (with validate_search = TRUE) may slightly change due to a different pseudorandom number generator (PRNG) state when clustering posterior draws. The different PRNG state was necessary to make the PRNG state for the full-data search in the validate_search = TRUE case consistent to the PRNG state for the full-data search in the validate_search = FALSE case. (GitHub: #385)

  • Reduced dependencies. (GitHub: #388)

  • Argument digits of print.vselsummary() which used to be passed to an internal round() call was removed. Instead, digits can now be passed to print.data.frame() via ..., thereby determining the minimum number of significant digits to be printed. (GitHub: #389)

  • Although bad practice (in general), a reference model lacking an intercept can now be used within projpred. However, it will always be projected onto submodels which include an intercept. The reason is that even if the true intercept in the reference model is zero, this does not need to hold for the submodels. An informational message mentioning the projection onto intercept-including submodels is thrown when projpred encounters a reference model lacking an intercept. (GitHub: #96, #391)

  • In case of non-predictor arguments of s() or t2(), projpred now throws an error. (This had already been documented before, but a suitable error message was missing.) (GitHub: #393, based on #156 and #269)

  • In case of the brms::categorical() family (supported since version 2.4.0), projpred now strips underscores from response category names in as.matrix.projection() output, as done by brms. (GitHub: #394)

  • L1 search now throws a warning if an interaction term is selected before all involved main-effect terms have been selected. (GitHub: #395)

  • Documented that in multilevel (group-level) terms, function calls on the right-hand side of the | character (e.g., (1 | gr(group_variable)), which is possible in brms) are currently not allowed in projpred. A corresponding error message has also been added. (GitHub: #319)

  • Due to internal refactoring:

    • project()’s output elements submodl and weights have been renamed to outdmin and wdraws_prj, respectively.
    • varsel()’s and cv_varsel()’s output element d_test has been replaced with new output elements type_test and y_wobs_test.

    Apart from project()’s output element wdraws_prj, these elements are not meant to be accessed manually, so changes are mentioned here only for the sake of completeness. Output element wdraws_prj of project() is only needed if project() was used for a clustered projection, which is not the default (and discouraged in most applied cases, at least with a small number of clusters). Thus, these renamings are breaking changes only in very rare cases.

  • print.vselsummary() now also prints K in case of K-fold CV.

  • The print.vselsummary() output has been slightly improved, e.g., adding a remark what “search included” or “search not included” means.

  • print.vselsummary() now also prints whether deltas = TRUE or deltas = FALSE was used.

  • Output element pct_solution_terms_cv has now also been added to vsel objects returned by varsel(), but in that case, it is simply NULL. This (pct_solution_terms_cv being NULL) is now also the case if validate_search = FALSE was used in cv_varsel().

  • Minor enhancements in the documentation.

  • Enhancements in the vignettes. In particular, section “Troubleshooting” of the main vignette has been revised.

  • If proj_predict() is used with observation weights that are not all equal to 1, a warning is now thrown. (GitHub: starts to address #402)

Bug fixes

  • Fixed a long-standing bug (existing at least from version 2.0.2 on) causing predict.refmodel() to require newdata to contain the response variable in case of a brms reference model. This is similar to paul-buerkner/brms#1457, but concerns predict.refmodel() (paul-buerkner/brms#1457 referred to predictions from the submodels). In order to make this predict.refmodel() fix work, brms version 2.19.0 or later is needed. (GitHub: #381)
  • Fixed a long-standing bug (existing from version 2.1.0 on) causing output element p_type of project() to be incorrect in case of refit_prj = FALSE, !is.null(nclusters), and an object of class vsel that was created with a non-clustered (thinned) projection during the search phase. The fix comes with a slightly different behavior of proj_predict() for datafits: It will not draw nresample_clusters times from the posterior-projection predictive distribution (which is based on the same single projected draw), but only once. (GitHub: #211, #401)
  • When performing predictions from submodels which are GLMs (or from submodels which are L1-penalized GLMs, which is only possible in case of refit_prj = FALSE after an L1 search), a new dataset containing a character predictor variable with only a single unique value (or a new dataset containing a factor predictor variable with a single level) used to cause an error. The case of a character (not factor) predictor variable with only a single unique value occurred, e.g., during the performance evaluation in a LOO CV if a character predictor got selected into a fold’s solution path. The character issue existed from version 2.1.0 on (in earlier versions, however, there were other issues which caused character predictors to throw an error). Now, all issues with respect to character predictor variables should be resolved. The issue with single-level factor predictor variables is resolved now as well. (GitHub: #403)
  • When performing predictions from submodels which are GLMs (or from submodels which are L1-penalized GLMs, which is only possible in case of refit_prj = FALSE after an L1 search), a new dataset containing a factor predictor with re-ordered levels (compared to this same factor in the original dataset) used to lead to incorrect predictions. This bug existed at least from version 2.0.2 on (possibly even in earlier versions), but has been resolved now. (GitHub: #403)
  • Fixed an error thrown by projpred’s internal GLM submodel fitter in case of unused levels of a factor. This issue existed at least from version 2.0.2 on (possibly even in earlier versions), but should have only affected rstanarm reference model fits (brms reference model fits were only affected in case of a brms::brm() call with drop_unused_levels = FALSE, which is not the default). (GitHub: #403)
  • Fixed a bug that caused an L1 search combined with refit_prj = FALSE (which is the default only for datafits, not for the reference model objects of class refmodel that are usually employed in practice) to lead to incorrect predictions from the L1-searched submodels (which are L1-penalized GLMs) if the solution path had a main effect ranked after an interaction term. This bug existed at least from version 2.0.2 on (possibly even in earlier versions). The mentioned submodel predictions did not only affect the performance evaluation, but also the projected dispersion parameter and the returned Kullback-Leibler divergence (and the corresponding cross-entropy). (GitHub: #403)

projpred 2.4.0

CRAN release: 2023-02-12

Major changes

Minor changes

  • Improvements in the numerical stability of internal link and inverse-link functions. (GitHub: #376)

Bug fixes

  • Fix a bug for offsets in cases where family (see init_refmodel()) has a non-identity link function: After clustering the reference model’s posterior draws, we need to aggregate (within a given cluster) the reference model’s fitted values which already take the offsets into account instead of taking the offsets into account after aggregating the fitted values which do not take the offsets into account. This fix should affect results only in a very slight manner. Due to projpred’s internal adjustment for numerical stability when averaging a quantity across the draws within a given cluster, this also changes the projected residual standard deviations in Gaussian models in the order of 1e-10. (GitHub: #374)

projpred 2.3.0

CRAN release: 2023-01-10

Major changes

  • In plot.vsel() and summary.vsel(), the default of alpha = 0.32 is replaced by alpha = 2 * pnorm(-1) (= 1 - diff(pnorm(c(-1, 1))), which is only approximately 0.32) so that now, a normal-approximation confidence interval with default alpha stretches by exactly one standard error on either side of the point estimate. Typically, this changes results only slightly. In some cases, however, the new default may lead to a different suggested size, explaining why this is regarded as a major change. (GitHub: #371)

Minor changes

  • The deprecated function ggplot2::aes_string() is not used anymore, thereby avoiding an occasional soft-deprecation warning thrown by ggplot2 3.4.0. (GitHub: #367)
  • The KL divergence from the reference model to a submodel is simplified to the corresponding cross-entropy (i.e., the reference model’s entropy is dropped), with some caveats described in the documentation for output element ce of project(). The reason for this change is that the former KL divergence assumed the reference model’s family to be the same as the submodel’s family, which does not need to be the case for custom reference models. This should not be a user-facing change as users are discouraged to make use of specific output elements (like the former element kl of objects of class projection or vsel) directly. (GitHub: #369)
  • Improvements in the documentation (especially for argument family of init_refmodel() and get_refmodel.default()).

projpred 2.2.2

CRAN release: 2022-11-09

Major changes

  • Several important bug fixes (see below).

Minor changes

Bug fixes

  • Fix a bug causing offsets not to be taken into account appropriately when calculating the PSIS weights (those used for the submodels) in the validate_search = FALSE case of cv_varsel(). This bug was introduced in v2.2.0 (and existed up to—including—v2.2.1).
  • Fix a (long-standing) bug causing offsets not to be taken into account appropriately when calculating the predictive variances for a reference model that has a dispersion parameter and a non-identity link function. (GitHub: #186 (partly), #355)
  • Fix a (long-standing) bug causing offsets not to be taken into account appropriately when calculating the reference model’s summary statistics in case of cv_varsel() with cv_method = "LOO" (more precisely, only the LOO posterior predictive expected values <vsel_object>$summaries$ref$mu were affected, not the (pointwise) LOO log posterior predictive density values <vsel_object>$summaries$ref$lppd). (GitHub: #186 (partly), #356)
  • Fix a (long-standing) bug leading to an error when trying to use cv_varsel() with custom search_terms (in some instances). (GitHub: #345, #360; thanks to @sor16)

projpred 2.2.1

CRAN release: 2022-09-20

Minor changes

  • Several improvements in the documentation.
  • For the RMSE as well as the AUC (see argument stats of summary.vsel()), the bootstrapping results are now also used for inferring the lower and upper confidence interval bounds. (GitHub: #318, #347; thanks to @awd97 and @VisionResearchBlog)
  • For datafits, offsets are not supported anymore. (GitHub: #186 (partly), #351)

Bug fixes

  • Fix GitHub issue #348 (L1 search in the presence of interaction terms). This bug was introduced in v2.1.0 (and existed up to—including—v2.2.0).
  • Fix incorrectly thrown messages in case of datafits (and other—unlikely—cases where nclusters == S and S <= 20, with S denoting the number of draws in the reference model).
  • Fix GitHub issue #349 (only concerned datafits). (GitHub: #350)

projpred 2.2.0

CRAN release: 2022-08-19

Major changes

  • In the validate_search = FALSE case of cv_varsel() (with cv_method = "LOO"), the PSIS weights are now calculated based on the reference model (they used to be calculated based on the submodels which is incorrect). (GitHub: #325)
  • Some long-standing severe bugs (GitHub issues #329, #330, and #342) have been fixed, concerning the performance evaluation of models with nontrivial observation weights (i.e., models where at least one observation had a weight differing from 1). Concerned performance statistics were "mse", "rmse", "acc" (= "pctcorr"), and "auc" (i.e., all performance statistics except for "elpd" and "mlpd").
  • plot.vsel() and suggest_size() gain a new argument thres_elpd. By default, this argument doesn’t have any impact, but a non-NA value can be used for a customized model size selection rule (see ?suggest_size for details). (GitHub: #335)

Minor changes

  • Several improvements in the documentation (especially in the explanation of the suggest_size() heuristic).
  • Improvement of the numerical stability for some link functions, achieved by avoiding unnecessary back-and-forth transformations between latent space and response space. (GitHub: #337, #338)
  • All arguments seed and .seed are now allowed to be NA for not calling set.seed() internally at all.
  • Argument d_test of varsel() is not considered as an internal feature anymore. This was possible after fixing a bug for d_test (see below). (GitHub: #341)
  • The order of the observations in the sub-elements of <vsel_object>$summaries and <vsel_object>$d_test now corresponds to the order of the observations in the original dataset if <vsel_object> was created by a call to cv_varsel(<...>, cv_method = "kfold") (formerly, in that case, the observations in those sub-elements were ordered by fold). Thereby, the order of the observations in those sub-elements now always corresponds to the order of the observations in the original dataset, except if <vsel_object> was created by a call to varsel(<...>, d_test = <non-NULL_d_test_object>), in which case the order of the observations in those sub-elements corresponds to the order of the observations in <non-NULL_d_test_object>. (GitHub: #341)

Bug fixes

  • Fix GitHub issue #324 (large search_terms caused the R session to crash).
  • Fix GitHub issue #204. (GitHub: #325)
  • Fix the validate_search = FALSE bug described above in “Major changes”: The PSIS weights are now calculated based on the reference model (they used to be calculated based on the submodels which is incorrect). (GitHub: #325)
  • Fix \mbox{} commands displayed incorrectly in the HTML help from R version 4.2.0 on. (GitHub: #326)
  • Fix GitHub issue #329 (see also “Major changes” above).
  • Fix GitHub issue #331.
  • plot.vsel() now draws the dashed red horizontal line for the reference model (and—if present—the dotted black horizontal line for the baseline model) first (i.e., before the submodel-specific graphical elements), to avoid overplotting.
  • Fix GitHub issue #339. (GitHub: #340)
  • Fix argument d_test of varsel(): Not only the predictive performance of the reference model needs to be evaluated on the test data, but also the predictive performance of the submodels. (GitHub: #341)
  • Fix GitHub issue #342 (see also “Major changes” above).
  • Fix GitHub issue #330 (see also “Major changes” above). (GitHub: #344, commit 23e7101)

projpred 2.1.2

CRAN release: 2022-05-13

Minor changes

  • Account for changes concerning the handling of offsets in rstanarm version 2.21.3. In particular, issue stan-dev/rstanarm#542 was fixed in rstanarm 2.21.3.
  • Show the output of the vignette on CRAN.
  • In the vignette, use cv_varsel() with LOO CV and validate_search = FALSE instead of K-fold CV. (GitHub: #305)
  • Improve the documentation for argument search_terms of varsel() and cv_varsel(). (GitHub: #155, #308)
  • In case of user-specified (non-NULL) search_terms, method = NULL is internally changed to method = "forward" and method = "L1" throws a warning. This is done because search_terms only takes effect in case of a forward search. (GitHub: #155, #308)
  • Internally, the intercept is now always included in search_terms. This is necessary to prevent a bug described below. (GitHub: #308)
  • When fitting multilevel submodels via lme4, projpred now tries to handle PIRLS loop resulted in NaN value errors automatically. (GitHub: #314)
  • The fix for GitHub issue #320 (see below) required to rename argument b of projpred:::bootstrap() to B.

Bug fixes

  • Throw a more informative error message in case of special group-level terms which are currently not supported (in particular, nested ones).
  • Previously, using a search_terms vector which excluded the intercept in conjunction with refit_prj = FALSE (the latter in project(), varsel(), or cv_varsel()) led to incorrect submodels being fetched from the search or to an error while doing so. This has been fixed now by internally forcing the inclusion of the intercept in search_terms. (GitHub: #308)
  • Fix GitHub issues #147 and #202. (GitHub: #312)
  • Fix GitHub issue #320. (GitHub: #321)

projpred 2.1.1

CRAN release: 2022-04-03

Bug fixes

  • Fix the order of the package authors.
  • Fix failing CRAN checks.
  • Add an input check for argument solution_terms of project() to fix a test failure in R versions >= 4.2.

projpred 2.1.0

CRAN release: 2022-04-01

Major changes

  • Added support for weighted LOO proportional-to-size subsampling based on Magnusson et al. (2019). However, subsampled PSIS-LOO CV is currently regarded as experimental. Therefore, a corresponding warning is thrown when calling cv_varsel() with nloo < n where n denotes the number of observations. (GitHub: #94, #252, commit feea39e)
  • Automatically explore both linear and smooths components in GAM models. This allows the user to gauge the impact of the smooth term against its linear counterpart.
  • Fast approximate LOO computation for validate_search = FALSE in cv_varsel().
  • Formerly, the defaults for arguments nclusters (= 1) and nclusters_pred (= 5) of varsel() and cv_varsel() were set internally (the user-visible defaults were NULL). Now, nclusters and ndraws_pred (note the ndraws_pred, not nclusters_pred) have non-NULL user-visible defaults of 20 and 400, respectively. In general, this increases the runtime of these functions a lot. With respect to cv_varsel(), the new vignette (see vignettes) mentions two ways to quickly obtain some rough preliminary results which in general should not be used as final results, though: (i) varsel() and (ii) cv_varsel() with validate_search = FALSE (which only takes effect for cv_method = "LOO"). (GitHub: #291 and several commits beforehand, in particular bbd0f0a, babe031, 4ef95d3, and ce7d1e0)
  • For proj_linpred() and proj_predict(), arguments nterms, ndraws, and seed have been removed to allow the user to pass them to project(). New arguments filter_nterms, nresample_clusters, and .seed have been introduced (see the documentation for details). (GitHub: #92, #135)
  • Reference models lacking an intercept are not supported anymore (actually, the previous implementation for such models was incomplete). Support might be re-introduced in the future (when fixed), but for now it is withdrawn as it requires some larger changes. (GitHub: #124, but see also #96 and #100)
  • In the output of proj_linpred(), dimensions are not dropped anymore (i.e., output elements pred and lpd are always S x N matrices now). (GitHub: #143)
  • In case of integrated = TRUE, proj_linpred() now averages the LPD (across the projected posterior draws) instead of taking the LPD at the averaged linear predictors. (GitHub: #143)
  • If newdata does not contain the response variable, proj_linpred() now returns NULL for output element lpd. (GitHub: #143)
  • The fix for the offset issues (listed below under “Bug fixes”) requires reference model fits of class stanreg (from package rstanarm) with offsets to have these offsets specified via an offset() term in the model formula (and not via argument offset).
  • Improved handling of errors when fitting multilevel submodels. (GitHub: #201)
  • Some defaults have been changed from NULL to a user-visible value (and NULL is not allowed anymore).
  • Argument data of get_refmodel.stanreg() has been removed. (GitHub: #219)
  • The function passed to argument div_minimizer of init_refmodel() now always needs to return a list of submodels (see the documentation for details). Correspondingly, the function passed to argument proj_predfun of init_refmodel() can now always expect a list as input for argument fits (see the documentation for details). (GitHub: #230)
  • The function passed to argument proj_predfun of init_refmodel() now always needs to return a matrix (see the documentation for details). (GitHub: #230)
  • The projection can be run in parallel now. However, we cannot recommend this for all kinds of platforms and all kinds of models. For more information, see the general package documentation available at ?`projpred-package`. (GitHub: #235)
  • Support for the Student_t() family is regarded as experimental. Therefore, a corresponding warning is thrown when creating the reference model. (GitHub: #233, #252)
  • Support for additive models (i.e., GAMs and GAMMs) is regarded as experimental. Therefore, a corresponding warning is thrown when creating the reference model. (GitHub: #237, #252)
  • Support for the Gamma() family is regarded as experimental. Therefore, a corresponding warning is thrown when creating the reference model. (GitHub: paul-buerkner/brms#1255, #240, #252)
  • The previous behavior of init_refmodel() in case of argument dis being NULL (the default) was dangerous for custom reference models with a family having a dispersion parameter (in that case, dis values of all-zeros were used silently). The new behavior now requires a non-NULL argument dis in that case. (GitHub: #254)
  • Argument cv_search has been renamed to refit_prj. (GitHub: #154, #265)
  • as.matrix.projection() has gained a new argument nm_scheme which allows to choose the naming scheme for the column names of the returned matrix. The default ("auto") follows the naming scheme of the reference model fit (and uses the "rstanarm" naming scheme if the reference model fit is of an unknown class). (GitHub: #82, #279)
  • seed (and .seed) arguments now have a default of sample.int(.Machine$integer.max, 1) instead of NULL. Furthermore, the value supplied to these arguments is now used to generate new seeds internally on-the-fly. In many cases, this will change results compared to older projpred versions. Also note that now, the internal seeds are never fixed to a specific value if seed (and .seed) arguments are set to NULL. (GitHub: #84, #286)

Minor changes

  • Improved summary output with important details.
  • For group-level effects, the as.matrix.projection() method now also returns the estimated group-level effects themselves. (GitHub: #75)
  • For group-level effects, the as.matrix.projection() method now returns the variance components (population SD(s) and population correlation(s)) instead of the empirical SD(s) of the group-level effects. (GitHub: #74)
  • Improved documentation. (GitHub: especially #233)
  • Replaced the two vignettes by a single one which also has new content. (GitHub: #237)
  • Updated the README file. (GitHub: #245)
  • Some error and warning messages have been improved and added. (GitHub: especially #219, #221, #223, #252, #263)
  • For K-fold cross-validation, an internally hard-coded value of 5 for nclusters_pred was removed. (GitHub: commit 5062f2f)
  • Throw a proper error message for unsupported families. (GitHub: #140)
  • Show the README also on the CRAN website. (GitHub: #140)
  • project(): Warn if elements of solution_terms are not found in the reference model (and therefore ignored). (GitHub: #140)
  • get_refmodel.default() now passes arguments via the ellipsis (...) to init_refmodel(). (GitHub: #153, commit dd3716e)
  • Remove dependency on package rngtools (version 2.0.0 of projpred re-introduced this dependency after it was already removed in version 1.1.2). (GitHub: #189)
  • init_refmodel(): The default (NULL) for argument extract_model_data has been removed as it wasn’t meaningful anyway. (GitHub: #219)
  • Argument folds of init_refmodel() has been removed as it was effectively unused. (GitHub: #220)
  • Use the S3 system for solution_terms(). This allowed the introduction of a solution_terms.projection() method. (GitHub: #223)
  • predict.refmodel() now uses a default of newdata = NULL. (GitHub: #223)
  • Argument weights of init_refmodel()’s argument proj_predfun has been removed. (GitHub: #163, #224)
  • projpred’s internal div_minimizer functions have been unified into a single div_minimizer which chooses an appropriate submodel fitter based on the formula of the submodel, not based on that of the reference model. Furthermore, the automatic handling of errors in the submodel fitters has been improved. (GitHub: #230)
  • Improve the axis labels in plot.vsel(). (GitHub: #234, #270)
  • Handle rstanarm’s GitHub issue #551. This implies that projpred’s default cvfun for stanreg fits will now always use inner parallelization in rstanarm::kfold.stanreg() (i.e., across chains, not across CV folds), with getOption("mc.cores", 1) cores. We do so on all systems (not only Windows). (GitHub: #249)
  • Argument fit of init_refmodel()’s argument proj_predfun was renamed to fits. This is a non-breaking change since all calls to proj_predfun in projpred have that argument unnamed. However, this cannot be guaranteed in the future, so we strongly encourage users with a custom proj_predfun to rename argument fit to fits. (GitHub: #263)
  • init_refmodel() has gained argument cvrefbuilder which may be a custom function for constructing the K reference models in a K-fold CV. (GitHub: #271)
  • Allow arguments to be passed from project(), varsel(), and cv_varsel() to the divergence minimizer. (GitHub: #278)
  • In init_refmodel(), any contrasts attributes of the dataset’s columns are silently removed. (GitHub: #284)
  • NAs in data supplied to newdata arguments now trigger an error. (GitHub: #285)

Bug fixes

  • Fixed a bug in as.matrix.projection() (causing incorrect column names for the returned matrix). (GitHub: #72, #73)
  • Fixed a bug raising an error when not projecting from a vsel object. (GitHub: #79, #80)
  • Fixed a bug in the calculation of the Gaussian deviance. (GitHub: #81)
  • Fixed a bug in the calculation of the predictive statistics of the reference model on test data in varsel(). (GitHub #90)
  • Fixed a bug in an input check for argument nloo of cv_varsel(). (GitHub: #93)
  • Fixed a bug in cv_varsel(), causing an error in case of !validate_search && cv_method != "LOO". (GitHub: #95)
  • Fixed bugs related to the setting of the seed. (GitHub: commit 02cd50d)
  • Fixed a bug causing proj_linpred() to raise an error if argument newdata was NULL. (GitHub: #97)
  • Fixed an incorrect usage of the dispersion parameter values when calculating output element lpd in proj_linpred() (for integrated = TRUE as well as for integrated = FALSE). (GitHub: #105)
  • Fixed bugs in proj_linpred()’s calculation of output element lpd (for integrated = TRUE). (GitHub: #106, #112)
  • Fixed an inconsistency in the dimensions of proj_linpred()’s output elements pred and lpd (for integrated = FALSE): Now, they are both S x N matrices, with S denoting the number of (possibly clustered) posterior draws and N denoting the number of observations. (GitHub: #107, #112)
  • Fixed a bug causing proj_predict()’s output matrix to be transposed in case of nrow(newdata) == 1. (GitHub: #112)
  • Fixed a bug when using weights or offsets e.g. in proj_linpred(). (GitHub: #114)
  • Fixed a bug causing varsel()/make_formula to fail with multidimensional interaction terms. (GitHub: #102, #103)
  • Fixed an indexing bug in cv_varsel() for models with a single predictor. (GitHub: #115)
  • Fixed bugs for argument nterms of proj_linpred() and proj_predict(). (GitHub: #110)
  • Fixed an inconsistency for some intercept-only submodels. (GitHub: #119)
  • Fix a bug for as.matrix.projection() in case of 1 (clustered) draw after projection. (GitHub: #130)
  • For submodels of class subfit, make the column names of as.matrix.projection()’s output matrix consistent with other classes of submodels. (GitHub: #132)
  • Fix a bug for argument nterms_max of plot.vsel() if there is just the intercept-only submodel. (GitHub: #138)
  • Throw an appropriate error message when trying to apply an L1 search to an empty (i.e. intercept-only) reference model. (GitHub: #139)
  • Fix the list names of element search_path in, e.g., varsel()’s output. (GitHub: #140)
  • Fix a bug (error unused argument) when initializing the K reference models in a K-fold CV with CV fits not of class brmsfit or stanreg. (GitHub: #140)
  • In get_refmodel.default(), remove old defunct arguments fetch_data, wobs, and offset. (GitHub: #140)
  • Fix a bug in get_refmodel.stanreg(). (GitHub: #142, #184)
  • Fix a possible bug related to extract_model_data()’s argument extract_y in get_refmodel.default(). (GitHub: #153, commit 39fece8)
  • Fix a possible bug related to extract_model_data() in K-fold CV. (GitHub: #153, commit 4f32195)
  • Fix GitHub issue #161.
  • Fix GitHub issue #162.
  • Fix GitHub issue #164.
  • Fix GitHub issue #160.
  • Fix GitHub issue #159.
  • Fix GitHub issue #158.
  • Fix GitHub issue #157.
  • Fix GitHub issue #144.
  • Fix GitHub issue #146.
  • Fix GitHub issue #169.
  • Fix GitHub issue #167.
  • Fix a bug in the default proj_predfun() for GLMMs. (GitHub: #174)
  • Fix GitHub issue #171.
  • Fix GitHub issue #172.
  • Fix a bug in the default proj_predfun() for datafits. (GitHub: #177)
  • Fix the names of summary.vsel()$selection for objects of class vsel created by varsel(). (GitHub: #179)
  • Fix forward search when search_terms are not consecutive in size. (GitHub: commit 34e24de)
  • Fix a bug in cv_varsel()$pct_solution_terms_cv. (GitHub: #188, commit e529ec1)
  • Fix GitHub issue #185. (GitHub: #193, #194)
  • Fix a bug in forward searches with interaction terms. (GitHub: #191)
  • Fix offset issues. (GitHub: #196, #203, #228)
  • Fix a bug in glm_elnet() (the workhorse for L1 search), causing the grid for lambda to be constructed without taking observation weights into account. (GitHub: #198; note that the second part of #198 did not have any consequences for users)
  • Fix GitHub issue #136. (GitHub: #221)
  • Fix a bug in print.vsel() causing argument digits to be ignored. (GitHub: #222)
  • Fix a bug causing the default of argument cv_search in varsel() and cv_varsel() to be TRUE for datafits, although it should be FALSE in that case. (GitHub: #223)
  • Fix a bug (Error: Levels '<...>' of grouping factor '<...>' cannot be found in the fitted model. Consider setting argument 'allow_new_levels' to TRUE.) when predicting from submodels which are GLMMs for newdata containing new levels for grouping factors. (GitHub: #223)
  • predict.refmodel(): Fix a bug for integer ynew. (GitHub: #223)
  • predict.refmodel(): Fix input checks for offsetnew and weightsnew. (GitHub: #223)
  • After all calls to extract_model_data(), the weights and offsets are now checked if they are of length 0 (and if yes, then they are set to vectors of ones and zeros, respectively). This is important for extract_model_data() functions which return weights and offsets of length 0 (see, e.g., brms version <= 2.16.1). (GitHub: #223)
  • Handle rstanarm’s GitHub issue #546. (GitHub: #227)
  • Fix a bug causing the internal submodel fitter for GLMMs to not pass arguments var (the predictive variances) and regul (amount of ridge regularization) to the internal submodel fitter for GLMs. (GitHub: #230)
  • Fix GitHub issue #210. (GitHub: #234)
  • Fix GitHub issue #242. (GitHub: #253)
  • Fix GitHub issue #244. (GitHub: #255)
  • Fix GitHub issue #243. (GitHub: #262)
  • Fix GitHub issue #213. (GitHub: #264)
  • Fix GitHub issue #215. (GitHub: #266)
  • Fix GitHub issue #212. (GitHub: #267)
  • Fix GitHub issue #156. (GitHub: #269)
  • If the data used for the reference model contains NAs, an appropriate error is now thrown. Previously, the reference model was created successfully, but this caused opaque errors in downstream code such as project(). (GitHub: #274)
  • Fix GitHub issue #268. (GitHub: #287)
  • Fix GitHub issue #149. (GitHub: #288)

projpred 2.0.2

CRAN release: 2020-10-28

We have fully rewritten the internals in several ways. Most importantly, we now leverage maximum likelihood estimation to third parties depending on the reference model’s family. This allows a lot of flexibility and extensibility for various models. Functionality wise, the major updates since the last release are:

  • Added support for GLMMs and GAMMs via lme4 and gamm4.
  • Formula syntax support internally that allows for easier building upon projections.
  • Thanks to the above point, we save some computation by only considering sensible projections during forward search instead of fitting every possible submodel.
  • We have added a new argument search_terms that allows the user to specify custom unit building blocks of the projections. New vignette coming up.
  • We have fully changed the way to define custom reference models. The user now provides projection fitting and prediction functions (more information in a new upcoming vignette).

projpred 1.1.4

CRAN release: 2019-10-02

Better validation of function arguments.

projpred 1.1.3

Added print methods for vsel and cvsel objects. Added AUC statistics for binomial family. A few additional minor patches.

projpred 1.1.2

CRAN release: 2019-05-31

Removed the dependency on the rngtools package.

projpred 1.1.1

CRAN release: 2019-03-12

This version contains only a few patches, no new features to the user.

projpred 1.1.0

CRAN release: 2018-10-23

New features

  • Added support for brms models.

Bug fixes

  • The program crashed with rstanarm models fitted with syntax like stan_glm(log(y) ~ log(x), ...), that is, it did not allow transformation for y.

projpred 1.0.0

CRAN release: 2018-09-18

New features and improvements

  • Changed the internals so that now all fit objects (such as rstanarm fits) are converted to refmodel-objects using the generic get_refmodel-function, and all the functions use only this object. This makes it much easier to use projpred with other reference models by writing them a new get_refmodel-function. The syntax is now changed so that varsel and cv_varsel both return an object that has similar structure always, and the reference model is stored into this object.
  • Added more examples to the vignette.
  • Added possibility to change the baseline in plot/summary. Now it is possible to compare also to the best submodel found, not only to the reference model.
  • Bug fix: RMSE was previously computed wrong, this is now fixed.
  • Small changes: nloo = n by default in cv_varsel. regul=1e-4 now by default in all functions.

projpred 0.9.0

New features and improvements

  • Added the cv_search argument for the main functions (varsel,cv_varsel,project and the prediction functions). Now it is possible to make predictions also with those parameter estimates that were computed during the L1-penalized search. This change also allows the user to compute the Lasso-solution by providing the observed data as the ‘reference fit’ for init_refmodel. An example will be added to the vignette.

Bug fixes

  • The projection with a nonzero regularization parameter value did not produce exactly correct result, although the difference to the correct result was often so small that user would not see the difference. Fixed this.

projpred 0.8.0 and earlier

CRAN release: 2018-04-16

Until this version, we did not keep record of the changes between different versions. Started to do this from version 0.9.0 onwards.