From 278442b4f5187a321cc255e1eed6453570306f3c Mon Sep 17 00:00:00 2001
From: Santtu Tikka <santtu.tikka@jyu.fi>
Date: Mon, 13 May 2024 13:33:44 +0300
Subject: [PATCH] mcmc_diagnostics, hmc_diagnostics and lfo as methods,
 cutpoints to cutpoint, run-extended

---
 NAMESPACE                        |  3 +++
 R/as_data_frame.R                |  2 +-
 R/as_data_table.R                | 16 ++++++++--------
 R/lfo.R                          | 18 ++++++++++++------
 R/mcmc_diagnostics.R             | 22 +++++++++++++++++++---
 R/plot.R                         |  4 ++--
 R/predict_helpers.R              | 28 ++++++++++++++--------------
 R/prepare_stan_input.R           |  8 ++++----
 R/stanblocks_families.R          | 32 +++++++++++++++++---------------
 man/as.data.frame.dynamitefit.Rd |  2 +-
 man/hmc_diagnostics.Rd           |  7 ++++++-
 man/lfo.Rd                       | 13 ++++++++-----
 man/mcmc_diagnostics.Rd          |  9 +++++++--
 man/plot.lfo.Rd                  |  4 ++--
 man/print.lfo.Rd                 |  2 +-
 tests/testthat/test-errors.R     | 27 +++++++--------------------
 tests/testthat/test-extended.R   |  4 ++--
 17 files changed, 114 insertions(+), 87 deletions(-)

diff --git a/NAMESPACE b/NAMESPACE
index f5dcc16..131f8da 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -19,7 +19,10 @@ S3method(get_parameter_names,dynamitefit)
 S3method(get_parameter_types,dynamitefit)
 S3method(get_priors,dynamitefit)
 S3method(get_priors,dynamiteformula)
+S3method(hmc_diagnostics,dynamitefit)
+S3method(lfo,dynamitefit)
 S3method(loo,dynamitefit)
+S3method(mcmc_diagnostics,dynamitefit)
 S3method(ndraws,dynamitefit)
 S3method(nobs,dynamitefit)
 S3method(plot,dynamitefit)
diff --git a/R/as_data_frame.R b/R/as_data_frame.R
index 2c51ac6..8dc7b0b 100644
--- a/R/as_data_frame.R
+++ b/R/as_data_frame.R
@@ -11,7 +11,7 @@
 #'
 #'  * `alpha`\cr Intercept terms (time-invariant or time-varying).
 #'  * `beta`\cr Time-invariant regression coefficients.
-#'  * `cutpoints`\cr Cutpoints for ordinal regression.
+#'  * `cutpoint`\cr Cutpoints for ordinal regression.
 #'  * `delta`\cr Time-varying regression coefficients.
 #'  * `nu`\cr Group-level random effects.
 #'  * `lambda`\cr Factor loadings.
diff --git a/R/as_data_table.R b/R/as_data_table.R
index 078eab1..0907aec 100644
--- a/R/as_data_table.R
+++ b/R/as_data_table.R
@@ -706,9 +706,9 @@ as_data_table_corr <- function(x, draws, n_draws, resps, ...) {
   )
 }
 
-#' @describeIn as_data_table_default Data Table for a "cutpoints" Parameter
+#' @describeIn as_data_table_default Data Table for a "cutpoint" Parameter
 #' @noRd
-as_data_table_cutpoints <- function(x, draws, response,
+as_data_table_cutpoint <- function(x, draws, response,
                                     n_draws, include_fixed, ...) {
   channel <- get_channel(x, response)
   S <- channel$S
@@ -726,7 +726,7 @@ as_data_table_cutpoints <- function(x, draws, response,
     data.table::rbindlist(lapply(seq_len(S - 1L), function(i) {
       idx <- (i - 1L) * n_time2 + seq_len(n_time2)
       data.table::data.table(
-        parameter = paste0("cutpoints_", response),
+        parameter = paste0("cutpoint_", response),
         value = c(
           rep(NA, n_na),
           c(draws[, , idx])
@@ -737,7 +737,7 @@ as_data_table_cutpoints <- function(x, draws, response,
     }))
   } else {
     data.table::data.table(
-      parameter = paste0("cutpoints_", response),
+      parameter = paste0("cutpoint_", response),
       category = rep(seq_len(S - 1L), each = n_draws),
       value = c(draws)
     )
@@ -752,7 +752,7 @@ all_types <- c(
   "corr",
   "corr_nu",
   "corr_psi",
-  "cutpoints",
+  "cutpoint",
   "delta",
   "lambda",
   "nu",
@@ -776,7 +776,7 @@ fixed_types <- c(
   "corr",
   "corr_nu",
   "corr_psi",
-  "cutpoints",
+  "cutpoint",
   "lambda",
   "nu",
   "omega",
@@ -794,7 +794,7 @@ fixed_types <- c(
 
 varying_types <- c(
   "alpha",
-  "cutpoints",
+  "cutpoint",
   "delta",
   "psi"
 )
@@ -802,7 +802,7 @@ varying_types <- c(
 default_types <- c(
   "alpha",
   "beta",
-  "cutpoints",
+  "cutpoint",
   "delta",
   "lambda",
   "nu",
diff --git a/R/lfo.R b/R/lfo.R
index a279d20..39dc844 100644
--- a/R/lfo.R
+++ b/R/lfo.R
@@ -9,9 +9,8 @@
 #' is triggered if pareto k values of any group exceeds the threshold.
 #'
 #' @export
-#' @export lfo
 #' @family diagnostics
-#' @aliases lfo
+#' @rdname lfo
 #' @param x \[`dynamitefit`]\cr The model fit object.
 #' @param L  \[`integer(1)`]\cr Positive integer defining how many time points
 #'   should be used for the initial fit.
@@ -51,7 +50,13 @@
 #' }
 #' }
 #'
-lfo <- function(x, L, verbose = TRUE, k_threshold = 0.7, ...) {
+lfo <- function(x, ...) {
+  UseMethod("lfo", x)
+}
+
+#' @export
+#' @rdname lfo
+lfo.dynamitefit <- function(x, L, verbose = TRUE, k_threshold = 0.7, ...) {
   stopifnot_(
     !missing(x),
     "Argument {.arg x} is missing."
@@ -272,7 +277,8 @@ lfo <- function(x, L, verbose = TRUE, k_threshold = 0.7, ...) {
 #' Print the results from the LFO
 #'
 #' Prints the summary of the leave-future-out cross-validation.
-#' @param x x \[`lfo`]\cr Output of the `lfo` method.
+#'
+#' @param x \[`lfo`]\cr Output of the `lfo` method.
 #' @param ... Ignored.
 #' @return Returns `x` invisibly.
 #' @export
@@ -305,9 +311,9 @@ print.lfo <- function(x, ...) {
 #' Plots Pareto k values per each time point (with one point per group),
 #' together with a horizontal line representing the used threshold.
 #'
-#' @param x \[`lfo`]\cr Output from the `lfo` function.
+#' @param x \[`lfo`]\cr Output of the `lfo` method.
 #' @param ... Ignored.
-#' @return A ggplot object.
+#' @return A `ggplot` object.
 #' @export
 #' @examples
 #' data.table::setDTthreads(1) # For CRAN
diff --git a/R/mcmc_diagnostics.R b/R/mcmc_diagnostics.R
index e9a7434..2d9201c 100644
--- a/R/mcmc_diagnostics.R
+++ b/R/mcmc_diagnostics.R
@@ -1,21 +1,29 @@
 #' Diagnostic Values of a Dynamite Model
 #'
-#' Prints HMC diagnostics, and lists parameters with smallest effective sample
+#' Prints HMC diagnostics and lists parameters with smallest effective sample
 #' sizes and largest Rhat values. See [hmc_diagnostics()] and
 #' [posterior::default_convergence_measures()] for details.
 #'
 #' @export
 #' @family diagnostics
+#' @rdname mcmc_diagnostics
 #' @param x \[`dynamitefit`]\cr The model fit object.
 #' @param n \[`integer(1)`]\cr How many rows to print in
 #'   parameter-specific convergence measures. The default is 3. Should be a
 #'   positive (unrestricted) integer.
+#' @param ... Ignored.
 #' @return Returns `x` (invisibly).
 #' @examples
 #' data.table::setDTthreads(1) # For CRAN
 #' mcmc_diagnostics(gaussian_example_fit)
 #'
-mcmc_diagnostics <- function(x, n = 3L) {
+mcmc_diagnostics <- function(x, ...) {
+  UseMethod("mcmc_diagnostics", x)
+}
+
+#' @export
+#' @rdname mcmc_diagnostics
+mcmc_diagnostics.dynamitefit <- function(x, n = 3L, ...) {
   stopifnot_(
     !missing(x),
     "Argument {.arg x} is missing."
@@ -71,12 +79,20 @@ mcmc_diagnostics <- function(x, n = 3L) {
 #'
 #' @export
 #' @family diagnostics
+#' @rdname hmc_diagnostics
 #' @param x \[`dynamitefit`]\cr The model fit object.
+#' @param ... Ignored.
 #' @return Returns `x` (invisibly).
 #' data.table::setDTthreads(1) # For CRAN
 #' hmc_diagnostics(gaussian_example_fit)
 #'
-hmc_diagnostics <- function(x) {
+hmc_diagnostics <- function(x, ...) {
+  UseMethod("hmc_diagnostics", x)
+}
+
+#' @export
+#' @rdname hmc_diagnostics
+hmc_diagnostics.dynamitefit <- function(x, ...) {
   stopifnot_(
     !missing(x),
     "Argument {.arg x} is missing."
diff --git a/R/plot.R b/R/plot.R
index caac8d5..2db1eb5 100644
--- a/R/plot.R
+++ b/R/plot.R
@@ -501,7 +501,7 @@ plot_fixed <- function(coefs, level, alpha, facet, scales, n_params) {
       coefs$type[1L],
       alpha = "time-invariant intercepts",
       beta = "time-invariant regression coefficients",
-      cutpoints = "time-invariant cutpoints",
+      cutpoint = "time-invariant cutpoints",
       nu = "random intercepts",
       lambda = "latent factor loadings",
       "time-invariant parameters"
@@ -559,7 +559,7 @@ plot_varying <- function(coefs, level, alpha, scales, n_params) {
     title_spec <- switch(
       coefs$type[1L],
       alpha = "time-varying intercepts",
-      cutpoints = "time-invariant cutpoints",
+      cutpoint = "time-invariant cutpoints",
       delta = "time-invariant regression coefficients",
       psi = "latent factors",
       "time-varying parameters"
diff --git a/R/predict_helpers.R b/R/predict_helpers.R
index 72ede1c..f290b10 100644
--- a/R/predict_helpers.R
+++ b/R/predict_helpers.R
@@ -644,7 +644,7 @@ prepare_eval_env_univariate <- function(e, resp, resp_levels, cvars,
                                         idx, type, eval_type) {
   alpha <- paste0("alpha_", resp)
   beta <- paste0("beta_", resp)
-  cutpoints <- paste0("cutpoints_", resp)
+  cutpoint <- paste0("cutpoint_", resp)
   delta <- paste0("delta_", resp)
   phi <- paste0("phi_", resp)
   sigma <- paste0("sigma_", resp)
@@ -681,14 +681,14 @@ prepare_eval_env_univariate <- function(e, resp, resp_levels, cvars,
       stats::pnorm
     )
     if (cvars$has_fixed_intercept) {
-      e$cutpoints <- samples[[cutpoints]][idx, , drop = FALSE]
-      e$cutpoints <- e$cutpoints[rep_len(e$n_draws, e$k), , drop = FALSE]
+      e$cutpoint <- samples[[cutpoint]][idx, , drop = FALSE]
+      e$cutpoint <- e$cutpoint[rep_len(e$n_draws, e$k), , drop = FALSE]
       e$alpha <- matrix(0.0, e$n_draws, 1L)
     }
     if (cvars$has_varying_intercept) {
-      e$cutpoints <- samples[[cutpoints]][idx, , , drop = FALSE]
-      e$cutpoints <- e$cutpoints[rep_len(e$n_draws, e$k), , , drop = FALSE]
-      e$alpha <- matrix(0.0, e$n_draws, dim(e$cutpoints)[2L])
+      e$cutpoint <- samples[[cutpoint]][idx, , , drop = FALSE]
+      e$cutpoint <- e$cutpoint[rep_len(e$n_draws, e$k), , , drop = FALSE]
+      e$alpha <- matrix(0.0, e$n_draws, dim(e$cutpoint)[2L])
     }
   } else {
     if (cvars$has_fixed_intercept) {
@@ -1063,8 +1063,8 @@ predict_expr$fitted$categorical <- "
 "
 
 predict_expr$fitted$cumulative <- "
-  prob <- cbind(1, invlink(xbeta - cutpoints{idx_cuts})) -
-    cbind(invlink(xbeta - cutpoints{idx_cuts}), 0)
+  prob <- cbind(1, invlink(xbeta - cutpoint{idx_cuts})) -
+    cbind(invlink(xbeta - cutpoint{idx_cuts}), 0)
   for (s in 1:d) {{
     data.table::set(
       x = out,
@@ -1173,8 +1173,8 @@ predict_expr$predicted$categorical <- "
 "
 
 predict_expr$predicted$cumulative <- "
-  prob <- cbind(1, invlink(xbeta - cutpoints{idx_cuts})) -
-    cbind(invlink(xbeta - cutpoints{idx_cuts}), 0)
+  prob <- cbind(1, invlink(xbeta - cutpoint{idx_cuts})) -
+    cbind(invlink(xbeta - cutpoint{idx_cuts}), 0)
   data.table::set(
     x = out,
     i = idx_data,
@@ -1322,8 +1322,8 @@ predict_expr$mean$categorical <- "
 "
 
 predict_expr$mean$cumulative <- "
-  prob <- cbind(1, invlink(xbeta - cutpoints{idx_cuts})) -
-    cbind(invlink(xbeta - cutpoints{idx_cuts}), 0)
+  prob <- cbind(1, invlink(xbeta - cutpoint{idx_cuts})) -
+    cbind(invlink(xbeta - cutpoint{idx_cuts}), 0)
   for (s in 1:d) {{
     data.table::set(
       x = out,
@@ -1462,8 +1462,8 @@ predict_expr$loglik$categorical <- "
 "
 
 predict_expr$loglik$cumulative <- "
-  prob <- cbind(1, invlink(xbeta - cutpoints{idx_cuts})) -
-    cbind(invlink(xbeta - cutpoints{idx_cuts}), 0)
+  prob <- cbind(1, invlink(xbeta - cutpoint{idx_cuts})) -
+    cbind(invlink(xbeta - cutpoint{idx_cuts}), 0)
   data.table::set(
     x = out,
     i = idx,
diff --git a/R/prepare_stan_input.R b/R/prepare_stan_input.R
index 85374a5..762533e 100644
--- a/R/prepare_stan_input.R
+++ b/R/prepare_stan_input.R
@@ -770,7 +770,7 @@ prepare_channel_cumulative <- function(y, Y, channel, sampling,
   fixed_cutpoints <- channel$has_fixed_intercept
   if (fixed_cutpoints) {
     cutpoint_priors <- data.frame(
-      parameter = paste0("cutpoints_", y, "_", seq_len(S_y - 1)),
+      parameter = paste0("cutpoint_", y, "_", seq_len(S_y - 1)),
       response = y,
       prior = "std_normal()",
       type = "cutpoint",
@@ -793,13 +793,13 @@ prepare_channel_cumulative <- function(y, Y, channel, sampling,
     out$priors <- out$priors[out$priors$type != "alpha", ]
     out$channel$prior_distr$alpha_prior_distr <- NULL
     if (is.null(priors)) {
-      out$channel$prior_distr$cutpoints_prior_distr <- cutpoint_priors$prior
-      names(out$channel$prior_distr$cutpoints_prior_distr) <- cutpoint_priors$category
+      out$channel$prior_distr$cutpoint_prior_distr <- cutpoint_priors$prior
+      names(out$channel$prior_distr$cutpoint_prior_distr) <- cutpoint_priors$category
       out$priors <- rbind(cutpoint_priors, out$priors)
     } else {
       priors <- priors[priors$response == y, ]
       pdef <- priors[priors$type == "cutpoint", ]
-      out$channel$prior_distr$cutpoints_prior_distr <- pdef$prior
+      out$channel$prior_distr$cutpoint_prior_distr <- pdef$prior
       defaults <- rbind(
         cutpoint_priors,
         default_priors(y, channel, mean_gamma, sd_gamma, mean_y, sd_y)$priors,
diff --git a/R/stanblocks_families.R b/R/stanblocks_families.R
index 9a10f35..1d2e83e 100644
--- a/R/stanblocks_families.R
+++ b/R/stanblocks_families.R
@@ -632,20 +632,20 @@ loglik_lines_cumulative <- function(y, obs, idt, default, family,
   u <- default$u
   is_logit <- identical("logit", family$link)
   link <- ifelse_(is_logit, "logistic", "probit")
-  cutpoints <- ifelse_(
+  cutpoint <- ifelse_(
     has_varying_intercept,
-    glue::glue("cutpoints_{y}[t, ]"),
-    glue::glue("cutpoints_{y}")
+    glue::glue("cutpoint_{y}[t, ]"),
+    glue::glue("cutpoint_{y}")
   )
   likelihood <- ifelse_(
     default$use_glm && is_logit,
     glue::glue(
       "ll += ordered_{link}_glm_l{u}pmf(y_{y}[t, {obs}] | X[t][{obs}, J_{y}], ",
-      "gamma__{y}, {cutpoints});"
+      "gamma__{y}, {cutpoint});"
     ),
     glue::glue(
       "ll += ordered_{link}_l{u}pmf(y_{y}[t, {obs}] | ",
-      "intercept_{y}, {cutpoints});"
+      "intercept_{y}, {cutpoint});"
     )
   )
   if (default$threading) {
@@ -653,11 +653,11 @@ loglik_lines_cumulative <- function(y, obs, idt, default, family,
       default$fun_args,
       onlyif(
         has_fixed_intercept,
-        glue::glue("vector cutpoints_{y}")
+        glue::glue("vector cutpoint_{y}")
       ),
       onlyif(
         has_varying_intercept,
-        glue::glue("array[] vector cutpoints_{y}")
+        glue::glue("array[] vector cutpoint_{y}")
       )
     )
   }
@@ -1459,7 +1459,7 @@ parameters_lines_cumulative <- function(y, idt, default,
     default,
     onlyif(
       has_fixed_intercept,
-      "ordered[S_{y} - 1] cutpoints_{y}; // Cutpoints"
+      "ordered[S_{y} - 1] cutpoint_{y}; // Cutpoints"
     ),
     .indent = idt(c(0, 1))
   )
@@ -1761,14 +1761,14 @@ transformed_parameters_lines_cumulative <- function(y, categories,
   if (has_varying_intercept) {
     declare_cutpoints <- glue::glue(
       stan_array(
-        backend, "vector", "cutpoints_{y}", "T", "", "S_{y} - 1"
+        backend, "vector", "cutpoint_{y}", "T", "", "S_{y} - 1"
       )
     )
     assign_cutpoints <- vapply(
       seq_along(categories),
       function(s) {
         glue::glue(
-          "cutpoints_{y}[, {s}] = alpha_{y}_{s};"
+          "cutpoint_{y}[, {s}] = alpha_{y}_{s};"
         )
       },
       character(1L)
@@ -1776,9 +1776,9 @@ transformed_parameters_lines_cumulative <- function(y, categories,
     state_cutpoints <- paste_rows(
       assign_cutpoints,
       "for (t in 1:T) {{",
-      "vector[S_{y}] tmp = exp(append_row(0, cutpoints_{y}[t, ]));",
+      "vector[S_{y}] tmp = exp(append_row(0, cutpoint_{y}[t, ]));",
       "for (s in 1:(S_{y} - 1)) {{",
-      "cutpoints_{y}[t, s] = log(sum(tmp[1:s]) / sum(tmp[(s + 1):S_{y}]));",
+      "cutpoint_{y}[t, s] = log(sum(tmp[1:s]) / sum(tmp[(s + 1):S_{y}]));",
       "}}",
       "}}",
       .indent = idt(c(1, 1, 2, 2, 3, 2, 1))
@@ -2158,12 +2158,14 @@ model_lines_cumulative <- function(y, obs, idt, priors,
   if (threading) {
     default$fun_call_args <- cs(
       default$fun_call_args,
-      glue::glue("cutpoints_{y}")
+      glue::glue("cutpoint_{y}")
     )
   }
-  S <- length(prior_distr$cutpoints_prior_distr)
+  S <- length(prior_distr$cutpoint_prior_distr)
   paste_rows(
-    glue::glue("cutpoints_{y}[{seq_len(S)}] ~ {prior_distr$cutpoints_prior_distr};"),
+    glue::glue(
+      "cutpoint_{y}[{seq_len(S)}] ~ {prior_distr$cutpoint_prior_distr};"
+    ),
     priors,
     model_lines_default(y, obs, idt, threading, default, ...),
     .parse = FALSE
diff --git a/man/as.data.frame.dynamitefit.Rd b/man/as.data.frame.dynamitefit.Rd
index 691bebc..7f74835 100644
--- a/man/as.data.frame.dynamitefit.Rd
+++ b/man/as.data.frame.dynamitefit.Rd
@@ -81,7 +81,7 @@ Potential values for the \code{types} argument are:
 \itemize{
 \item \code{alpha}\cr Intercept terms (time-invariant or time-varying).
 \item \code{beta}\cr Time-invariant regression coefficients.
-\item \code{cutpoints}\cr Cutpoints for ordinal regression.
+\item \code{cutpoint}\cr Cutpoints for ordinal regression.
 \item \code{delta}\cr Time-varying regression coefficients.
 \item \code{nu}\cr Group-level random effects.
 \item \code{lambda}\cr Factor loadings.
diff --git a/man/hmc_diagnostics.Rd b/man/hmc_diagnostics.Rd
index f1de3c5..8e6f1df 100644
--- a/man/hmc_diagnostics.Rd
+++ b/man/hmc_diagnostics.Rd
@@ -2,12 +2,17 @@
 % Please edit documentation in R/mcmc_diagnostics.R
 \name{hmc_diagnostics}
 \alias{hmc_diagnostics}
+\alias{hmc_diagnostics.dynamitefit}
 \title{HMC Diagnostics for a Dynamite Model}
 \usage{
-hmc_diagnostics(x)
+hmc_diagnostics(x, ...)
+
+\method{hmc_diagnostics}{dynamitefit}(x, ...)
 }
 \arguments{
 \item{x}{[\code{dynamitefit}]\cr The model fit object.}
+
+\item{...}{Ignored.}
 }
 \value{
 Returns \code{x} (invisibly).
diff --git a/man/lfo.Rd b/man/lfo.Rd
index 11296d9..a9162ab 100644
--- a/man/lfo.Rd
+++ b/man/lfo.Rd
@@ -2,13 +2,20 @@
 % Please edit documentation in R/lfo.R
 \name{lfo}
 \alias{lfo}
+\alias{lfo.dynamitefit}
 \title{Approximate Leave-Future-Out (LFO) Cross-validation}
 \usage{
-lfo(x, L, verbose = TRUE, k_threshold = 0.7, ...)
+lfo(x, ...)
+
+\method{lfo}{dynamitefit}(x, L, verbose = TRUE, k_threshold = 0.7, ...)
 }
 \arguments{
 \item{x}{[\code{dynamitefit}]\cr The model fit object.}
 
+\item{...}{Additional arguments passed to \code{\link[rstan:stanmodel-method-sampling]{rstan::sampling()}} or
+\code{\link[cmdstanr:model-method-sample]{cmdstanr::sample()}}, such as \code{chains} and \code{cores} (\code{parallel_chains} in
+\code{cmdstanr}).}
+
 \item{L}{[\code{integer(1)}]\cr Positive integer defining how many time points
 should be used for the initial fit.}
 
@@ -17,10 +24,6 @@ the LFO computations to the console.}
 
 \item{k_threshold}{[\code{numeric(1)}]\cr Threshold for the Pareto k estimate
 triggering refit. Default is 0.7.}
-
-\item{...}{Additional arguments passed to \code{\link[rstan:stanmodel-method-sampling]{rstan::sampling()}} or
-\code{\link[cmdstanr:model-method-sample]{cmdstanr::sample()}}, such as \code{chains} and \code{cores} (\code{parallel_chains} in
-\code{cmdstanr}).}
 }
 \value{
 An \code{lfo} object which is a \code{list} with the following components:
diff --git a/man/mcmc_diagnostics.Rd b/man/mcmc_diagnostics.Rd
index 04db359..5cce21a 100644
--- a/man/mcmc_diagnostics.Rd
+++ b/man/mcmc_diagnostics.Rd
@@ -2,13 +2,18 @@
 % Please edit documentation in R/mcmc_diagnostics.R
 \name{mcmc_diagnostics}
 \alias{mcmc_diagnostics}
+\alias{mcmc_diagnostics.dynamitefit}
 \title{Diagnostic Values of a Dynamite Model}
 \usage{
-mcmc_diagnostics(x, n = 3L)
+mcmc_diagnostics(x, ...)
+
+\method{mcmc_diagnostics}{dynamitefit}(x, n = 3L, ...)
 }
 \arguments{
 \item{x}{[\code{dynamitefit}]\cr The model fit object.}
 
+\item{...}{Ignored.}
+
 \item{n}{[\code{integer(1)}]\cr How many rows to print in
 parameter-specific convergence measures. The default is 3. Should be a
 positive (unrestricted) integer.}
@@ -17,7 +22,7 @@ positive (unrestricted) integer.}
 Returns \code{x} (invisibly).
 }
 \description{
-Prints HMC diagnostics, and lists parameters with smallest effective sample
+Prints HMC diagnostics and lists parameters with smallest effective sample
 sizes and largest Rhat values. See \code{\link[=hmc_diagnostics]{hmc_diagnostics()}} and
 \code{\link[posterior:draws_summary]{posterior::default_convergence_measures()}} for details.
 }
diff --git a/man/plot.lfo.Rd b/man/plot.lfo.Rd
index 34bc6d4..37cd8e8 100644
--- a/man/plot.lfo.Rd
+++ b/man/plot.lfo.Rd
@@ -7,12 +7,12 @@
 \method{plot}{lfo}(x, ...)
 }
 \arguments{
-\item{x}{[\code{lfo}]\cr Output from the \code{lfo} function.}
+\item{x}{[\code{lfo}]\cr Output of the \code{lfo} method.}
 
 \item{...}{Ignored.}
 }
 \value{
-A ggplot object.
+A \code{ggplot} object.
 }
 \description{
 Plots Pareto k values per each time point (with one point per group),
diff --git a/man/print.lfo.Rd b/man/print.lfo.Rd
index 2c69d61..166a30d 100644
--- a/man/print.lfo.Rd
+++ b/man/print.lfo.Rd
@@ -7,7 +7,7 @@
 \method{print}{lfo}(x, ...)
 }
 \arguments{
-\item{x}{x [\code{lfo}]\cr Output of the \code{lfo} method.}
+\item{x}{[\code{lfo}]\cr Output of the \code{lfo} method.}
 
 \item{...}{Ignored.}
 }
diff --git a/tests/testthat/test-errors.R b/tests/testthat/test-errors.R
index 1b4a983..b37915e 100644
--- a/tests/testthat/test-errors.R
+++ b/tests/testthat/test-errors.R
@@ -989,17 +989,8 @@ test_that("output for missing argument fails", {
     "summary",
     "update"
   )
-  non_s3_methods <- c(
-    "hmc_diagnostics",
-    "lfo",
-    "mcmc_diagnostics"
-  )
   for (m in methods) {
-    call_fun <- ifelse_(
-      m %in% non_s3_methods,
-      m,
-      paste0(m, ".dynamitefit")
-    )
+    call_fun <- paste0(m, ".dynamitefit")
     expect_error(
       do.call(call_fun, args = list()),
       "Argument `.+` is missing"
@@ -1015,7 +1006,10 @@ test_that("output for non dynamitefit objects fails", {
     "coef",
     "fitted",
     "formula",
+    "hmc_diagnostics",
+    "lfo",
     "loo",
+    "mcmc_diagnostics",
     "ndraws",
     "nobs",
     "plot",
@@ -1033,22 +1027,13 @@ test_that("output for non dynamitefit objects fails", {
     "summary",
     "update"
   )
-  non_s3_methods <- c(
-    "hmc_diagnostics",
-    "lfo",
-    "mcmc_diagnostics"
-  )
   for (m in methods) {
     args <- ifelse_(
       m %in% object_arg_methods,
       list(object = 1L),
       list(x = 1L)
     )
-    call_fun <- ifelse_(
-      m %in% non_s3_methods,
-      m,
-      paste0(m, ".dynamitefit")
-    )
+    call_fun <- paste0(m, ".dynamitefit")
     expect_error(
       do.call(call_fun, args = args),
       "Argument `.+` must be a <dynamitefit> object\\."
@@ -1061,6 +1046,8 @@ test_that("output without Stan fit fails", {
     "as.data.frame",
     "as_draws_df",
     "fitted",
+    "lfo",
+    "loo",
     "predict",
     "ndraws"
   )
diff --git a/tests/testthat/test-extended.R b/tests/testthat/test-extended.R
index 3c2e514..008fea6 100644
--- a/tests/testthat/test-extended.R
+++ b/tests/testthat/test-extended.R
@@ -235,7 +235,7 @@ test_that("time-invariant cumulative fit and predict work", {
     NA
   )
   expect_error(
-    as.data.table(fit, types = "cutpoints"),
+    as.data.table(fit, types = "cutpoint"),
     NA
   )
   expect_error(
@@ -317,7 +317,7 @@ test_that("time-varying cutpoints for cumulative works", {
     NA
   )
   expect_error(
-    as.data.table(fit, types = "cutpoints"),
+    as.data.table(fit, types = "cutpoint"),
     NA
   )
   expect_error(