use cmdstanr generic

DESCRIPTION

@@ -123,6 +123,7 @@ Collate:
     'jmpost-package.R'
     'link_generics.R'
     'settings.R'
+    'standalone-s3-register.R'
     'zzz.R'
 VignetteBuilder: knitr
 RdMacros: Rdpack

NAMESPACE

@@ -1,6 +1,5 @@
 # Generated by roxygen2: do not edit by hand
 
-S3method(as.CmdStanMCMC,JointModelSamples)
 S3method(as.QuantityCollapser,GridEven)
 S3method(as.QuantityCollapser,GridEvent)
 S3method(as.QuantityCollapser,GridFixed)
@@ -220,7 +219,6 @@ export(SurvivalLogLogistic)
 export(SurvivalModel)
 export(SurvivalQuantities)
 export(SurvivalWeibullPH)
-export(as.CmdStanMCMC)
 export(as.QuantityCollapser)
 export(as.QuantityGenerator)
 export(as_formula)

R/JointModelSamples.R

@@ -98,7 +98,7 @@ as.StanModule.JointModelSamples <- function(object, generator, type, ...) {
 #' @export
 as_print_string.JointModelSamples <- function(object, indent = 1, ...) {
     sizes <- vapply(
-        as.CmdStanMCMC(object)$metadata()[["stan_variable_sizes"]],
+        cmdstanr::as.CmdStanMCMC(object)$metadata()[["stan_variable_sizes"]],
         \(x) {
             if (length(x) == 1 && x == 1) return("")
             paste0("[", paste(x, collapse = ", "), "]")
@@ -107,7 +107,7 @@ as_print_string.JointModelSamples <- function(object, indent = 1, ...) {
     )
     variable_string <- paste0(
         "        ",
-        as.CmdStanMCMC(object)$metadata()[["stan_variables"]],
+        cmdstanr::as.CmdStanMCMC(object)$metadata()[["stan_variables"]],
         sizes
     )
     template <- c(
@@ -123,8 +123,8 @@ as_print_string.JointModelSamples <- function(object, indent = 1, ...) {
     template_padded <- paste(pad, template)
     sprintf(
         paste(template_padded, collapse = "\n"),
-        as.CmdStanMCMC(object)$metadata()$iter_sampling,
-        as.CmdStanMCMC(object)$num_chains()
+        cmdstanr::as.CmdStanMCMC(object)$metadata()$iter_sampling,
+        cmdstanr::as.CmdStanMCMC(object)$num_chains()
     )
 }
 
@@ -141,7 +141,6 @@ setMethod(
 
 
 #' @rdname as.CmdStanMCMC
-#' @export
 as.CmdStanMCMC.JointModelSamples <- function(object, ...) {
     return(object@results)
 }

R/external-exports.R

@@ -30,3 +30,16 @@ NULL
 #' @export autoplot
 #' @family autoplot
 NULL
+
+
+#' Coerce to `CmdStanMCMC`
+#'
+#' @param object to be converted
+#' @param ... additional options
+#'
+#' @description
+#' Coerces an object to a [`cmdstanr::CmdStanMCMC`] object
+#'
+#' @name as.CmdStanMCMC
+#'
+NULL

R/generics.R

@@ -332,20 +332,6 @@ hazardWindows <- function(object, ...) {
     UseMethod("hazardWindows")
 }
 
-
-#' Coerce to `CmdStanMCMC`
-#'
-#' @param object to be converted
-#' @param ... additional options
-#'
-#' @description
-#' Coerces an object to a [`cmdstanr::CmdStanMCMC`] object
-#'
-#' @export
-as.CmdStanMCMC <- function(object, ...) {
-    UseMethod("as.CmdStanMCMC")
-}
-
 #' @rdname Quant-Dev
 #' @export
 as.QuantityGenerator <- function(object, ...) {

R/standalone-s3-register.R

@@ -0,0 +1,162 @@
+
+
+# Original source repo = r-lib/rlang
+# file: standalone-s3-register.R
+# last-updated: 2024-05-14
+# license: https://unlicense.org
+# ---
+#
+# ## Changelog
+#
+# 2024-05-14:
+#
+# * Mentioned `usethis::use_standalone()`.
+#
+# nocov start
+
+#' Register a method for a suggested dependency
+#'
+#' Generally, the recommended way to register an S3 method is to use the
+#' `S3Method()` namespace directive (often generated automatically by the
+#' `@export` roxygen2 tag). However, this technique requires that the generic
+#' be in an imported package, and sometimes you want to suggest a package,
+#' and only provide a method when that package is loaded. `s3_register()`
+#' can be called from your package's `.onLoad()` to dynamically register
+#' a method only if the generic's package is loaded.
+#'
+#' For R 3.5.0 and later, `s3_register()` is also useful when demonstrating
+#' class creation in a vignette, since method lookup no longer always involves
+#' the lexical scope. For R 3.6.0 and later, you can achieve a similar effect
+#' by using "delayed method registration", i.e. placing the following in your
+#' `NAMESPACE` file:
+#'
+#' ```
+#' if (getRversion() >= "3.6.0") {
+#'   S3method(package::generic, class)
+#' }
+#' ```
+#'
+#' @section Usage in other packages:
+#' To avoid taking a dependency on rlang, you copy the source of
+#' [`s3_register()`](https://github.com/r-lib/rlang/blob/main/R/standalone-s3-register.R)
+#' into your own package or with
+#' `usethis::use_standalone("r-lib/rlang", "s3-register")`. It is licensed under
+#' the permissive [unlicense](https://choosealicense.com/licenses/unlicense/) to
+#' make it crystal clear that we're happy for you to do this. There's no need to
+#' include the license or even credit us when using this function.
+#'
+#' @param generic Name of the generic in the form `"pkg::generic"`.
+#' @param class Name of the class
+#' @param method Optionally, the implementation of the method. By default,
+#'   this will be found by looking for a function called `generic.class`
+#'   in the package environment.
+#' @examples
+#' # A typical use case is to dynamically register tibble/pillar methods
+#' # for your class. That way you avoid creating a hard dependency on packages
+#' # that are not essential, while still providing finer control over
+#' # printing when they are used.
+#'
+#' .onLoad <- function(...) {
+#'   s3_register("pillar::pillar_shaft", "vctrs_vctr")
+#'   s3_register("tibble::type_sum", "vctrs_vctr")
+#' }
+#' @keywords internal
+#' @noRd
+s3_register <- function(generic, class, method = NULL) {
+    stopifnot(is.character(generic), length(generic) == 1)
+    stopifnot(is.character(class), length(class) == 1)
+
+    pieces <- strsplit(generic, "::")[[1]]
+    stopifnot(length(pieces) == 2)
+    package <- pieces[[1]]
+    generic <- pieces[[2]]
+
+    caller <- parent.frame()
+
+    get_method_env <- function() {
+        top <- topenv(caller)
+        if (isNamespace(top)) {
+            asNamespace(environmentName(top))
+        } else {
+            caller
+        }
+    }
+    get_method <- function(method) {
+        if (is.null(method)) {
+            get(paste0(generic, ".", class), envir = get_method_env())
+        } else {
+            method
+        }
+    }
+
+    register <- function(...) {
+        envir <- asNamespace(package)
+
+        # Refresh the method each time, it might have been updated by devtools
+        method_fn <- get_method(method)
+        stopifnot(is.function(method_fn))
+
+
+        # Only register if generic can be accessed
+        if (exists(generic, envir)) {
+            registerS3method(generic, class, method_fn, envir = envir)
+        } else if (identical(Sys.getenv("NOT_CRAN"), "true")) {
+            warn <- .rlang_s3_register_compat("warn")
+
+            warn(c(
+                sprintf(
+                    "Can't find generic `%s` in package %s to register S3 method.",
+                    generic,
+                    package
+                ),
+                "i" = "This message is only shown to developers using devtools.",
+                "i" = sprintf("Do you need to update %s to the latest version?", package)
+            ))
+        }
+    }
+
+    # Always register hook in case package is later unloaded & reloaded
+    setHook(packageEvent(package, "onLoad"), function(...) {
+        register()
+    })
+
+    # For compatibility with R < 4.1.0 where base isn't locked
+    is_sealed <- function(pkg) {
+        identical(pkg, "base") || environmentIsLocked(asNamespace(pkg))
+    }
+
+    # Avoid registration failures during loading (pkgload or regular).
+    # Check that environment is locked because the registering package
+    # might be a dependency of the package that exports the generic. In
+    # that case, the exports (and the generic) might not be populated
+    # yet (#1225).
+    if (isNamespaceLoaded(package) && is_sealed(package)) {
+        register()
+    }
+
+    invisible()
+}
+
+.rlang_s3_register_compat <- function(fn) {
+    # Compats that behave the same independently of rlang's presence
+    out <- switch(fn,
+        is_installed = return(function(pkg) requireNamespace(pkg, quietly = TRUE))
+    )
+
+    is_interactive_compat <- function() {
+        interactive()
+    }
+
+    format_msg <- function(x) paste(x, collapse = "\n")
+    switch(
+        fn,
+        is_interactive = return(is_interactive_compat),
+        abort = return(function(msg) stop(format_msg(msg), call. = FALSE)),
+        warn = return(function(msg) warning(format_msg(msg), call. = FALSE)),
+        inform = return(function(msg) message(format_msg(msg)))
+    )
+
+    stop(sprintf("Internal error in rlang shims: Unknown function `%s()`.", fn))
+}
+
+# nocov end

R/zzz.R

@@ -1,8 +1,4 @@
 
-.onLoad <- function(libname, pkgname) {
-    set_options()
-}
-
 .onAttach <- function(libname, pkgname) {
     if (!is_cmdstanr_available()) {
         packageStartupMessage(
@@ -38,6 +34,10 @@
     return(invisible(NULL))
 }
 
+.onLoad <- function(...) {
+    set_options()
+    s3_register("cmdstanr::as.CmdStanMCMC", "JointModelSamples")
+}
 
 # This only exists to silence the false positive R CMD CHECK warning about
 # importing but not using the posterior package. posterior is a dependency

tests/testthat/test-Grid.R

@@ -471,7 +471,7 @@ test_that("GridObservered + Constructs correct quantities", {
 
 
 
-    pred_mat <- as.CmdStanMCMC(fixtures_gsf$mp)$draws("Ypred", format = "draws_matrix")
+    pred_mat <- cmdstanr::as.CmdStanMCMC(fixtures_gsf$mp)$draws("Ypred", format = "draws_matrix")
 
     fdat <- fixtures_gsf$dat_lm |>
         dplyr::arrange(subject, time, sld) |>
@@ -512,7 +512,7 @@ test_that("GridObservered + Constructs correct quantities", {
     #
     design <- model.matrix(~ cov_cat + cov_cont, data = fixtures_gsf$dat_os)
 
-    beta_coefs <- as.CmdStanMCMC(fixtures_gsf$mp)$draws(
+    beta_coefs <- cmdstanr::as.CmdStanMCMC(fixtures_gsf$mp)$draws(
         c("sm_exp_lambda", "beta_os_cov"),
         format = "draws_matrix"
     )
@@ -636,7 +636,7 @@ test_that("GridPopulation() works as expected for GSF models", {
         b * (phi * exp(-s * time) + (1 - phi) * exp(g * time))
     }
 
-    samples_df <- as.CmdStanMCMC(fixtures_gsf$mp)$draws(
+    samples_df <- cmdstanr::as.CmdStanMCMC(fixtures_gsf$mp)$draws(
         c("lm_gsf_mu_ks", "lm_gsf_mu_kg", "lm_gsf_mu_bsld", "lm_gsf_mu_phi"),
         format = "draws_df"
     ) |>
@@ -705,7 +705,7 @@ test_that("GridPopulation() works as expected for Longitudinal models", {
     #
     # Derive values by hand
     #
-    samples_df <- as.CmdStanMCMC(fixtures_rs$mp)$draws(
+    samples_df <- cmdstanr::as.CmdStanMCMC(fixtures_rs$mp)$draws(
         c("lm_rs_intercept", "lm_rs_slope_mu"),
         format = "draws_df"
     ) |>

tests/testthat/test-GridPrediction.R

@@ -225,7 +225,7 @@ test_that("GridPrediction() works as expected for Survival models", {
     #
     # Derive values by hand
     #
-    samples_df <- as.CmdStanMCMC(fixtures_gsf_link$mp)$draws(
+    samples_df <- cmdstanr::as.CmdStanMCMC(fixtures_gsf_link$mp)$draws(
         c("beta_os_cov", "link_dsld", "link_ttg", "sm_exp_lambda"),
         format = "draws_df"
     ) |>
@@ -399,7 +399,7 @@ test_that("GridPrediction() works for survival only models", {
         dplyr::as_tibble()
 
     # Calculate expected values by hand
-    pars_dat <- as.CmdStanMCMC(fixtures_weibull_only$mp)$draws(
+    pars_dat <- cmdstanr::as.CmdStanMCMC(fixtures_weibull_only$mp)$draws(
         c("beta_os_cov", "sm_weibull_ph_lambda", "sm_weibull_ph_gamma"),
         format = "draws_df"
     ) |>

tests/testthat/test-LongitudinalClaretBruno.R

@@ -242,7 +242,7 @@ test_that("Can recover known distributional parameters from a SF joint model", {
     }
 
     dat <- summary_post(
-        as.CmdStanMCMC(mp),
+        cmdstanr::as.CmdStanMCMC(mp),
         c("lm_clbr_mu_b", "lm_clbr_mu_g", "lm_clbr_mu_c", "lm_clbr_mu_p"),
         TRUE
     )
@@ -257,7 +257,7 @@ test_that("Can recover known distributional parameters from a SF joint model", {
 
 
     dat <- summary_post(
-        as.CmdStanMCMC(mp),
+        cmdstanr::as.CmdStanMCMC(mp),
         c("beta_os_cov", "sm_exp_lambda", "link_dsld", "link_growth", "link_ttg")
     )
     true_values <- c(
@@ -496,7 +496,7 @@ test_that("Can recover known distributional parameters from unscaled variance Cl
     }
 
     dat <- summary_post(
-        as.CmdStanMCMC(mp),
+        cmdstanr::as.CmdStanMCMC(mp),
         c(
             "lm_clbr_mu_b", "lm_clbr_mu_g", "lm_clbr_mu_c", "lm_clbr_mu_p",
             "lm_clbr_omega_b", "lm_clbr_omega_g", "lm_clbr_omega_c", "lm_clbr_omega_p",