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R/projpred-package.R
In projpred: Projection Predictive Feature Selection
#' Projection predictive feature selection
#'
#' @useDynLib projpred
#' @importFrom Rcpp sourceCpp
#'
#' @import stats
#' @import ggplot2
#' @importFrom rstantools posterior_linpred
#' @importFrom loo kfold
#'
#' @description
#'
#' The \R package \pkg{projpred} performs the projection predictive variable (or
#' "feature") selection for various regression models. We recommend to read the
#' `README` file (available with enhanced formatting
#' [online](https://mc-stan.org/projpred/)) and the main vignette (`topic =
#' "projpred"`, but also available
#' [online](https://mc-stan.org/projpred/articles/projpred.html)) before
#' continuing here.
#'
#' @details
#'
#' # Terminology
#'
#' Throughout the whole package documentation, we use the term "submodel" for
#' all kinds of candidate models onto which the reference model is projected.
#' For custom reference models, the candidate models don't need to be actual
#' *sub*models of the reference model, but in any case (even for custom
#' reference models), the candidate models are always actual *sub*models of the
#' full [`formula`] used by the search procedure. In this regard, it is correct
#' to speak of *sub*models, even in case of a custom reference model.
#'
#' The following model type abbreviations will be used at multiple places
#' throughout the documentation: GLM (generalized linear model), GLMM
#' (generalized linear multilevel---or "mixed"---model), GAM (generalized
#' additive model), and GAMM (generalized additive multilevel---or
#' "mixed"---model). Note that the term "generalized" includes the Gaussian
#' family as well.
#'
#' # Draw-wise divergence minimizers
#'
#' For the projection of the reference model onto a submodel, \pkg{projpred}
#' currently relies on the following functions as draw-wise divergence
#' minimizers (in other words, these are the workhorse functions employed by
#' \pkg{projpred}'s internal default `div_minimizer` functions, see
#' [init_refmodel()]):
#' * Submodel without multilevel or additive terms:
#' + For the traditional (or latent) projection (or the augmented-data
#' projection in case of the [binomial()] or [brms::bernoulli()] family): An
#' internal C++ function which basically serves the same purpose as [lm()]
#' for the [gaussian()] family and [glm()] for all other families. The
#' returned object inherits from class `subfit`. Possible tuning parameters
#' for this internal C++ function are: `regul` (amount of ridge
#' regularization; default: `1e-4`), `thresh_conv` (convergence threshold;
#' default: `1e-7`), `qa_updates_max` (maximum number of quadratic
#' approximation updates; default: `100`, but fixed to `1` in case of the
#' Gaussian family with identity link), `ls_iter_max` (maximum number of
#' line search iterations; default: `30`, but fixed to `1` in case of the
#' Gaussian family with identity link), `normalize` (single logical value
#' indicating whether to scale the predictors internally with the returned
#' regression coefficient estimates being back-adjusted appropriately;
#' default: `TRUE`), `beta0_init` (single numeric value giving the starting
#' value for the intercept at centered predictors; default: `0`), and
#' `beta_init` (numeric vector giving the starting values for the regression
#' coefficients; default: vector of `0`s).
#' + For the augmented-data projection: [MASS::polr()] (the returned object
#' inherits from class `polr`) for the [brms::cumulative()] family or
#' [rstanarm::stan_polr()] fits, [nnet::multinom()] (the returned object
#' inherits from class `multinom`) for the [brms::categorical()] family.
#' * Submodel with multilevel but no additive terms:
#' + For the traditional (or latent) projection (or the augmented-data
#' projection in case of the [binomial()] or [brms::bernoulli()] family):
#' [lme4::lmer()] (the returned object inherits from class `lmerMod`) for
#' the [gaussian()] family, [lme4::glmer()] (the returned object inherits
#' from class `glmerMod`) for all other families.
#' + For the augmented-data projection: [ordinal::clmm()] (the returned
#' object inherits from class `clmm`) for the [brms::cumulative()] family,
#' [mclogit::mblogit()] (the returned object inherits from class `mmblogit`)
#' for the [brms::categorical()] family.
#' * Submodel without multilevel but additive terms: [mgcv::gam()] (the returned
#' object inherits from class `gam`).
#' * Submodel with multilevel and additive terms: [gamm4::gamm4()] (within
#' \pkg{projpred}, the returned object inherits from class `gamm4`).
#'
#' # Verbosity, messages, warnings, errors
#'
#' Global option `projpred.verbose` may be used for specifying the value passed
#' to argument `verbose` of [project()], [varsel()], and [cv_varsel()].
#'
#' By default, \pkg{projpred} 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_proj_drawwise` to
#' `FALSE`.
#'
#' Furthermore, by default, \pkg{projpred} checks the convergence of the
#' draw-wise divergence minimizers and throws a warning if any seem to have not
#' converged. This warning is thrown after the warning message from global
#' option `projpred.warn_proj_drawwise` (see above) and can be deactivated by
#' setting global option `projpred.check_convergence` to `FALSE`.
#'
#' # Parallelization
#'
#' The projection of the reference model onto a submodel can be run in parallel
#' (across the projected draws). This is powered by the \pkg{foreach} package.
#' Thus, any parallel (or sequential) backend compatible with \pkg{foreach} can
#' be used, e.g., the backends from packages \pkg{doParallel}, \pkg{doMPI}, or
#' \pkg{doFuture}. Using the global option `projpred.parallel_proj_trigger`, the
#' number of projected draws below which no parallelization is applied (even if
#' a parallel backend is registered) can be modified. Such a "trigger" threshold
#' exists because of the computational overhead of a parallelization which makes
#' the projection parallelization only useful for a sufficiently large number of
#' projected draws. By default, the projection parallelization is turned off,
#' which can also be achieved by supplying `Inf` (or `NULL`) to option
#' `projpred.parallel_proj_trigger`. Note that we cannot recommend the
#' projection parallelization on Windows because in our experience, the
#' parallelization overhead is larger there, causing a parallel run to take
#' longer than a sequential run. Also note that the projection parallelization
#' works well for submodels which are GLMs (and hence also for the latent
#' projection if the submodel has no multilevel or additive predictor terms),
#' but for all other types of submodels, the fitted submodel objects are quite
#' big, which---when running in parallel---may lead to excessive memory usage
#' which in turn may crash the R session (on Unix systems, setting an
#' appropriate memory limit via [unix::rlimit_as()] may avoid crashing the whole
#' machine). Thus, we currently cannot recommend parallelizing projections onto
#' submodels which are GLMs (in this context, the latent projection onto a
#' submodel without multilevel and without additive terms may be regarded as a
#' projection onto a submodel which is a GLM). However, for [cv_varsel()], there
#' is also a *CV* parallelization (i.e., a parallelization of \pkg{projpred}'s
#' cross-validation) which can be activated via argument `parallel` (which in
#' turn can be controlled via global option `projpred.parallel_cv`).
#'
#' For the CV parallelization, 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.
#'
#' During parallelization (either of the projection or the CV), progression
#' updates can be received via the \pkg{progressr} package. This only works if
#' the \pkg{doFuture} backend is used for parallelization, e.g., via
#' `doFuture::registerDoFuture()` and `future::plan(future::multisession,
#' workers = 4)`. In that case, the \pkg{progressr} package can be used, e.g.,
#' by calling `progressr::handlers(global = TRUE)` before running the projection
#' or the CV in parallel. The \pkg{projpred} package also offers the global
#' option `projpred.use_progressr` for controlling whether to use the
#' \pkg{progressr} package (`TRUE` or `FALSE`), but since that global option
#' defaults to `requireNamespace("progressr", quietly = TRUE) && interactive()
#' && identical(foreach::getDoParName(), "doFuture")`, it usually does not need
#' to be set by the user.
#'
#' # Multilevel models: "Integrating out" group-level effects
#'
#' In case of multilevel models, \pkg{projpred} offers two global options for
#' "integrating out" group-level effects: `projpred.mlvl_pred_new` and
#' `projpred.mlvl_proj_ref_new`. When setting `projpred.mlvl_pred_new` to `TRUE`
#' (default is `FALSE`), then at
#' *prediction* time, \pkg{projpred} will treat group levels existing in the
#' training data as *new* group levels, implying that their group-level effects
#' are drawn randomly from a (multivariate) Gaussian distribution. This concerns
#' both, the reference model and the (i.e., any) submodel. Furthermore, setting
#' `projpred.mlvl_pred_new` to `TRUE` causes [as.matrix.projection()] and
#' [as_draws_matrix.projection()] to omit the projected group-level effects (for
#' the group levels from the original dataset). When setting
#' `projpred.mlvl_proj_ref_new` to `TRUE` (default is `FALSE`), then at
#' *projection* time, the reference model's fitted values (that the submodels
#' fit to) will be computed by treating the group levels from the original
#' dataset as *new* group levels, implying that their group-level effects will
#' be drawn randomly from a (multivariate) Gaussian distribution (as long as the
#' reference model is a multilevel model, which---for custom reference
#' models---does not need to be the case). This also affects the latent response
#' values for a latent projection correspondingly. Setting
#' `projpred.mlvl_pred_new` to `TRUE` makes sense, e.g., when the prediction
#' task is such that any group level will be treated as a new one. Typically,
#' setting `projpred.mlvl_proj_ref_new` to `TRUE` only makes sense when
#' `projpred.mlvl_pred_new` is already set to `TRUE`. In that case, the default
#' of `FALSE` for `projpred.mlvl_proj_ref_new` ensures that at projection time,
#' the submodels fit to the best possible fitted values from the reference
#' model, and setting `projpred.mlvl_proj_ref_new` to `TRUE` would make sense if
#' the group-level effects should be integrated out completely.
#'
#' # Memory usage
#'
#' By setting the global option `projpred.run_gc` to `TRUE`, \pkg{projpred} will
#' call [gc()] at some places (e.g., after each size that the forward search
#' passes through) to free up some memory. These [gc()] calls are not always
#' necessary to reduce the peak memory usage, but they add runtime (hence the
#' default of `FALSE` for that global option).
#'
#' # Other notes
#'
#' Global option `projpred.digits` controls arguments `digits` of
#' [print.vselsummary()] and [print.vsel()].
#'
#' There are several global options to control arguments of [plot.vsel()] and
#' [plot.cv_proportions()] globally, see section "Usage" of the help pages of
#' these two functions.
#'
#' Global option `projpred.warn_L1_interactions` may be set to `FALSE` to
#' deactivate a warning that an L1 search selected an interaction term before
#' all involved lower-order interaction terms (including main-effect terms) were
#' selected (in which case the predictor ranking is automatically modified by
#' \pkg{projpred} so that the lower-order interaction terms come before this
#' interaction term).
#'
#' Most examples are not executed when called via [example()]. To execute them,
#' their code has to be copied and pasted manually to the console.
#'
#' # Functions
#'
#' \describe{
#' \item{[init_refmodel()], [get_refmodel()]}{For setting up an object
#' containing information about the reference model, the submodels, and how
#' the projection should be carried out. Explicit calls to [init_refmodel()]
#' and [get_refmodel()] are only rarely needed.}
#' \item{[varsel()], [cv_varsel()]}{For running the *search* part and the
#' *evaluation* part for a projection predictive variable selection, possibly
#' with cross-validation (CV).}
#' \item{[summary.vsel()], [print.vsel()], [plot.vsel()],
#' [suggest_size.vsel()], [ranking()], [cv_proportions()],
#' [plot.cv_proportions()], [performances()]}{For post-processing the results
#' from [varsel()] and [cv_varsel()].}
#' \item{[project()]}{For projecting the reference model onto submodel(s).
#' Typically, this follows the variable selection, but it can also be applied
#' directly (without a variable selection).}
#' \item{[as.matrix.projection()] and [as_draws_matrix.projection()]}{For
#' extracting projected parameter draws.}
#' \item{[proj_linpred()], [proj_predict()]}{For making predictions from a
#' submodel (after projecting the reference model onto it).}
#' }
#'
"_PACKAGE"
#' Internal global options
#'
#' The following global options are for internal use:
#' * `projpred.mssg_ndraws`, `projpred.mssg_cut_search`, `projpred.mssg_time`,
#' `projpred.warn_wobs_ppd`, `projpred.warn_additive_experimental`,
#' `projpred.warn_allrandom_dis`, `projpred.warn_instable_projections`,
#' `projpred.warn_cvrefbuilder_NULL`, `projpred.warn_kfold_refits`: A single
#' logical value indicating whether to throw certain messages or warnings
#' (depending on the midfix `mssg` or `warn`, respectively). For the exact
#' meaning of these global options, see their occurrences in the codebase. With
#' the exception of `projpred.warn_allrandom_dis`, these global options are
#' currently used in the unit tests to deactivate these messages and warnings.
#' Global option `projpred.warn_instable_projections` is also used (invisibly)
#' in the latent vignette to suppress the corresponding warnings while
#' illustrating the underlying issue (instable projections).
#' * `projpred.additional_checks`: A single logical value indicating whether to
#' run some additional checks that are not necessary to be run when users call
#' the corresponding \pkg{projpred} functions. Currently, these checks are
#' activated during the unit tests.
#' * `projpred.glm_fitter`: A character string naming the function to be used as
#' the submodel fitter for non-multilevel, non-additive projections. Currently,
#' this is an experimental feature and allowed values are
#' `"fit_glm_ridge_callback"` (the default) and `"fit_glm_callback"`.
#' * `projpred.gaussian_not_as_generalized`: A single logical value indicating
#' whether to treat the [gaussian()] family not as a family for a *generalized
#' linear* model (i.e., for which [glm()] would typically be used as a model
#' fitting function outside of \pkg{projpred}), but as the family for an
#' explicit *linear* model (i.e., for which [lm()] would typically be used as a
#' model fitting function outside of \pkg{projpred}). This also holds for models
#' with multilevel terms (because \pkg{lme4} offers both [lme4::glmer()] and
#' [lme4::lmer()]). Currently, this is an experimental feature.
#' * `projpred.PQL`: A single logical value indicating whether to use
#' [MASS::glmmPQL()] as the submodel fitter for multilevel (non-additive)
#' projections (see GitHub issue
#' [#207](https://github.com/stan-dev/projpred/issues/207) and GitHub pull
#' request [#353](https://github.com/stan-dev/projpred/pull/353)). Currently,
#' this is an experimental feature.
#'
#' @name internal-global-options
#' @keywords internal
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#' Projection predictive feature selection
#'
#' @useDynLib projpred
#' @importFrom Rcpp sourceCpp
#'
#' @import stats
#' @import ggplot2
#' @importFrom rstantools posterior_linpred
#' @importFrom loo kfold
#'
#' @description
#'
#' The \R package \pkg{projpred} performs the projection predictive variable (or
#' "feature") selection for various regression models. We recommend to read the
#' `README` file (available with enhanced formatting
#' [online](https://mc-stan.org/projpred/)) and the main vignette (`topic =
#' "projpred"`, but also available
#' [online](https://mc-stan.org/projpred/articles/projpred.html)) before
#' continuing here.
#'
#' @details
#'
#' # Terminology
#'
#' Throughout the whole package documentation, we use the term "submodel" for
#' all kinds of candidate models onto which the reference model is projected.
#' For custom reference models, the candidate models don't need to be actual
#' *sub*models of the reference model, but in any case (even for custom
#' reference models), the candidate models are always actual *sub*models of the
#' full [`formula`] used by the search procedure. In this regard, it is correct
#' to speak of *sub*models, even in case of a custom reference model.
#'
#' The following model type abbreviations will be used at multiple places
#' throughout the documentation: GLM (generalized linear model), GLMM
#' (generalized linear multilevel---or "mixed"---model), GAM (generalized
#' additive model), and GAMM (generalized additive multilevel---or
#' "mixed"---model). Note that the term "generalized" includes the Gaussian
#' family as well.
#'
#' # Draw-wise divergence minimizers
#'
#' For the projection of the reference model onto a submodel, \pkg{projpred}
#' currently relies on the following functions as draw-wise divergence
#' minimizers (in other words, these are the workhorse functions employed by
#' \pkg{projpred}'s internal default `div_minimizer` functions, see
#' [init_refmodel()]):
#' * Submodel without multilevel or additive terms:
#' + For the traditional (or latent) projection (or the augmented-data
#' projection in case of the [binomial()] or [brms::bernoulli()] family): An
#' internal C++ function which basically serves the same purpose as [lm()]
#' for the [gaussian()] family and [glm()] for all other families. The
#' returned object inherits from class `subfit`. Possible tuning parameters
#' for this internal C++ function are: `regul` (amount of ridge
#' regularization; default: `1e-4`), `thresh_conv` (convergence threshold;
#' default: `1e-7`), `qa_updates_max` (maximum number of quadratic
#' approximation updates; default: `100`, but fixed to `1` in case of the
#' Gaussian family with identity link), `ls_iter_max` (maximum number of
#' line search iterations; default: `30`, but fixed to `1` in case of the
#' Gaussian family with identity link), `normalize` (single logical value
#' indicating whether to scale the predictors internally with the returned
#' regression coefficient estimates being back-adjusted appropriately;
#' default: `TRUE`), `beta0_init` (single numeric value giving the starting
#' value for the intercept at centered predictors; default: `0`), and
#' `beta_init` (numeric vector giving the starting values for the regression
#' coefficients; default: vector of `0`s).
#' + For the augmented-data projection: [MASS::polr()] (the returned object
#' inherits from class `polr`) for the [brms::cumulative()] family or
#' [rstanarm::stan_polr()] fits, [nnet::multinom()] (the returned object
#' inherits from class `multinom`) for the [brms::categorical()] family.
#' * Submodel with multilevel but no additive terms:
#' + For the traditional (or latent) projection (or the augmented-data
#' projection in case of the [binomial()] or [brms::bernoulli()] family):
#' [lme4::lmer()] (the returned object inherits from class `lmerMod`) for
#' the [gaussian()] family, [lme4::glmer()] (the returned object inherits
#' from class `glmerMod`) for all other families.
#' + For the augmented-data projection: [ordinal::clmm()] (the returned
#' object inherits from class `clmm`) for the [brms::cumulative()] family,
#' [mclogit::mblogit()] (the returned object inherits from class `mmblogit`)
#' for the [brms::categorical()] family.
#' * Submodel without multilevel but additive terms: [mgcv::gam()] (the returned
#' object inherits from class `gam`).
#' * Submodel with multilevel and additive terms: [gamm4::gamm4()] (within
#' \pkg{projpred}, the returned object inherits from class `gamm4`).
#'
#' # Verbosity, messages, warnings, errors
#'
#' Global option `projpred.verbose` may be used for specifying the value passed
#' to argument `verbose` of [project()], [varsel()], and [cv_varsel()].
#'
#' By default, \pkg{projpred} 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_proj_drawwise` to
#' `FALSE`.
#'
#' Furthermore, by default, \pkg{projpred} checks the convergence of the
#' draw-wise divergence minimizers and throws a warning if any seem to have not
#' converged. This warning is thrown after the warning message from global
#' option `projpred.warn_proj_drawwise` (see above) and can be deactivated by
#' setting global option `projpred.check_convergence` to `FALSE`.
#'
#' # Parallelization
#'
#' The projection of the reference model onto a submodel can be run in parallel
#' (across the projected draws). This is powered by the \pkg{foreach} package.
#' Thus, any parallel (or sequential) backend compatible with \pkg{foreach} can
#' be used, e.g., the backends from packages \pkg{doParallel}, \pkg{doMPI}, or
#' \pkg{doFuture}. Using the global option `projpred.parallel_proj_trigger`, the
#' number of projected draws below which no parallelization is applied (even if
#' a parallel backend is registered) can be modified. Such a "trigger" threshold
#' exists because of the computational overhead of a parallelization which makes
#' the projection parallelization only useful for a sufficiently large number of
#' projected draws. By default, the projection parallelization is turned off,
#' which can also be achieved by supplying `Inf` (or `NULL`) to option
#' `projpred.parallel_proj_trigger`. Note that we cannot recommend the
#' projection parallelization on Windows because in our experience, the
#' parallelization overhead is larger there, causing a parallel run to take
#' longer than a sequential run. Also note that the projection parallelization
#' works well for submodels which are GLMs (and hence also for the latent
#' projection if the submodel has no multilevel or additive predictor terms),
#' but for all other types of submodels, the fitted submodel objects are quite
#' big, which---when running in parallel---may lead to excessive memory usage
#' which in turn may crash the R session (on Unix systems, setting an
#' appropriate memory limit via [unix::rlimit_as()] may avoid crashing the whole
#' machine). Thus, we currently cannot recommend parallelizing projections onto
#' submodels which are GLMs (in this context, the latent projection onto a
#' submodel without multilevel and without additive terms may be regarded as a
#' projection onto a submodel which is a GLM). However, for [cv_varsel()], there
#' is also a *CV* parallelization (i.e., a parallelization of \pkg{projpred}'s
#' cross-validation) which can be activated via argument `parallel` (which in
#' turn can be controlled via global option `projpred.parallel_cv`).
#'
#' For the CV parallelization, 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.
#'
#' During parallelization (either of the projection or the CV), progression
#' updates can be received via the \pkg{progressr} package. This only works if
#' the \pkg{doFuture} backend is used for parallelization, e.g., via
#' `doFuture::registerDoFuture()` and `future::plan(future::multisession,
#' workers = 4)`. In that case, the \pkg{progressr} package can be used, e.g.,
#' by calling `progressr::handlers(global = TRUE)` before running the projection
#' or the CV in parallel. The \pkg{projpred} package also offers the global
#' option `projpred.use_progressr` for controlling whether to use the
#' \pkg{progressr} package (`TRUE` or `FALSE`), but since that global option
#' defaults to `requireNamespace("progressr", quietly = TRUE) && interactive()
#' && identical(foreach::getDoParName(), "doFuture")`, it usually does not need
#' to be set by the user.
#'
#' # Multilevel models: "Integrating out" group-level effects
#'
#' In case of multilevel models, \pkg{projpred} offers two global options for
#' "integrating out" group-level effects: `projpred.mlvl_pred_new` and
#' `projpred.mlvl_proj_ref_new`. When setting `projpred.mlvl_pred_new` to `TRUE`
#' (default is `FALSE`), then at
#' *prediction* time, \pkg{projpred} will treat group levels existing in the
#' training data as *new* group levels, implying that their group-level effects
#' are drawn randomly from a (multivariate) Gaussian distribution. This concerns
#' both, the reference model and the (i.e., any) submodel. Furthermore, setting
#' `projpred.mlvl_pred_new` to `TRUE` causes [as.matrix.projection()] and
#' [as_draws_matrix.projection()] to omit the projected group-level effects (for
#' the group levels from the original dataset). When setting
#' `projpred.mlvl_proj_ref_new` to `TRUE` (default is `FALSE`), then at
#' *projection* time, the reference model's fitted values (that the submodels
#' fit to) will be computed by treating the group levels from the original
#' dataset as *new* group levels, implying that their group-level effects will
#' be drawn randomly from a (multivariate) Gaussian distribution (as long as the
#' reference model is a multilevel model, which---for custom reference
#' models---does not need to be the case). This also affects the latent response
#' values for a latent projection correspondingly. Setting
#' `projpred.mlvl_pred_new` to `TRUE` makes sense, e.g., when the prediction
#' task is such that any group level will be treated as a new one. Typically,
#' setting `projpred.mlvl_proj_ref_new` to `TRUE` only makes sense when
#' `projpred.mlvl_pred_new` is already set to `TRUE`. In that case, the default
#' of `FALSE` for `projpred.mlvl_proj_ref_new` ensures that at projection time,
#' the submodels fit to the best possible fitted values from the reference
#' model, and setting `projpred.mlvl_proj_ref_new` to `TRUE` would make sense if
#' the group-level effects should be integrated out completely.
#'
#' # Memory usage
#'
#' By setting the global option `projpred.run_gc` to `TRUE`, \pkg{projpred} will
#' call [gc()] at some places (e.g., after each size that the forward search
#' passes through) to free up some memory. These [gc()] calls are not always
#' necessary to reduce the peak memory usage, but they add runtime (hence the
#' default of `FALSE` for that global option).
#'
#' # Other notes
#'
#' Global option `projpred.digits` controls arguments `digits` of
#' [print.vselsummary()] and [print.vsel()].
#'
#' There are several global options to control arguments of [plot.vsel()] and
#' [plot.cv_proportions()] globally, see section "Usage" of the help pages of
#' these two functions.
#'
#' Global option `projpred.warn_L1_interactions` may be set to `FALSE` to
#' deactivate a warning that an L1 search selected an interaction term before
#' all involved lower-order interaction terms (including main-effect terms) were
#' selected (in which case the predictor ranking is automatically modified by
#' \pkg{projpred} so that the lower-order interaction terms come before this
#' interaction term).
#'
#' Most examples are not executed when called via [example()]. To execute them,
#' their code has to be copied and pasted manually to the console.
#'
#' # Functions
#'
#' \describe{
#' \item{[init_refmodel()], [get_refmodel()]}{For setting up an object
#' containing information about the reference model, the submodels, and how
#' the projection should be carried out. Explicit calls to [init_refmodel()]
#' and [get_refmodel()] are only rarely needed.}
#' \item{[varsel()], [cv_varsel()]}{For running the *search* part and the
#' *evaluation* part for a projection predictive variable selection, possibly
#' with cross-validation (CV).}
#' \item{[summary.vsel()], [print.vsel()], [plot.vsel()],
#' [suggest_size.vsel()], [ranking()], [cv_proportions()],
#' [plot.cv_proportions()], [performances()]}{For post-processing the results
#' from [varsel()] and [cv_varsel()].}
#' \item{[project()]}{For projecting the reference model onto submodel(s).
#' Typically, this follows the variable selection, but it can also be applied
#' directly (without a variable selection).}
#' \item{[as.matrix.projection()] and [as_draws_matrix.projection()]}{For
#' extracting projected parameter draws.}
#' \item{[proj_linpred()], [proj_predict()]}{For making predictions from a
#' submodel (after projecting the reference model onto it).}
#' }
#'
"_PACKAGE"
#' Internal global options
#'
#' The following global options are for internal use:
#' * `projpred.mssg_ndraws`, `projpred.mssg_cut_search`, `projpred.mssg_time`,
#' `projpred.warn_wobs_ppd`, `projpred.warn_additive_experimental`,
#' `projpred.warn_allrandom_dis`, `projpred.warn_instable_projections`,
#' `projpred.warn_cvrefbuilder_NULL`, `projpred.warn_kfold_refits`: A single
#' logical value indicating whether to throw certain messages or warnings
#' (depending on the midfix `mssg` or `warn`, respectively). For the exact
#' meaning of these global options, see their occurrences in the codebase. With
#' the exception of `projpred.warn_allrandom_dis`, these global options are
#' currently used in the unit tests to deactivate these messages and warnings.
#' Global option `projpred.warn_instable_projections` is also used (invisibly)
#' in the latent vignette to suppress the corresponding warnings while
#' illustrating the underlying issue (instable projections).
#' * `projpred.additional_checks`: A single logical value indicating whether to
#' run some additional checks that are not necessary to be run when users call
#' the corresponding \pkg{projpred} functions. Currently, these checks are
#' activated during the unit tests.
#' * `projpred.glm_fitter`: A character string naming the function to be used as
#' the submodel fitter for non-multilevel, non-additive projections. Currently,
#' this is an experimental feature and allowed values are
#' `"fit_glm_ridge_callback"` (the default) and `"fit_glm_callback"`.
#' * `projpred.gaussian_not_as_generalized`: A single logical value indicating
#' whether to treat the [gaussian()] family not as a family for a *generalized
#' linear* model (i.e., for which [glm()] would typically be used as a model
#' fitting function outside of \pkg{projpred}), but as the family for an
#' explicit *linear* model (i.e., for which [lm()] would typically be used as a
#' model fitting function outside of \pkg{projpred}). This also holds for models
#' with multilevel terms (because \pkg{lme4} offers both [lme4::glmer()] and
#' [lme4::lmer()]). Currently, this is an experimental feature.
#' * `projpred.PQL`: A single logical value indicating whether to use
#' [MASS::glmmPQL()] as the submodel fitter for multilevel (non-additive)
#' projections (see GitHub issue
#' [#207](https://github.com/stan-dev/projpred/issues/207) and GitHub pull
#' request [#353](https://github.com/stan-dev/projpred/pull/353)). Currently,
#' this is an experimental feature.
#'
#' @name internal-global-options
#' @keywords internal
NULL
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