% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/classic.R
\name{Classic_Fit}
\alias{Classic_Fit}
\title{Classic fit object}
\description{
An R6 class representing the results of a classical (frequentist) estimation.
}
\section{Super class}{
\code{\link[BayesRTMB:RTMB_Fit_Base]{BayesRTMB::RTMB_Fit_Base}} -> \code{Classic_Fit}
}
\section{Public fields}{
\if{html}{\out{<div class="r6-fields">}}
\describe{
\item{\code{model}}{The `RTMB_Model` object used for estimation.}

\item{\code{fit}}{The result of the estimation (dataframe or lm object).}

\item{\code{par}}{a named list of parameter estimates.}

\item{\code{vcov}}{Variance-covariance matrix of fixed effects.}

\item{\code{se_method}}{Character string specifying the method used for standard errors.}

\item{\code{cluster}}{Character string specifying the cluster variable name, if any.}

\item{\code{robust_type}}{Character string specifying the robust standard error correction type, if used.}

\item{\code{bootstrap_results}}{A matrix containing bootstrap samples, if applicable.}

\item{\code{test_results}}{List of additional test results (e.g., chisq.test).}

\item{\code{view}}{Character vector of parameter names to prioritize in summary.}

\item{\code{par_vec}}{Numeric vector of parameter estimates.}

\item{\code{objective}}{Final objective value.}

\item{\code{log_lik}}{Log-likelihood.}

\item{\code{restricted_log_lik}}{Restricted log-likelihood, if classical estimation used REML-style marginalization.}

\item{\code{rss}}{Residual sum of squares for classical linear models, if available.}

\item{\code{df_residual}}{Residual degrees of freedom for classical linear models, if available.}

\item{\code{convergence}}{Convergence code.}

\item{\code{sd_rep}}{TMB sdreport object.}

\item{\code{df_fixed}}{Dataframe of fixed effects results.}

\item{\code{random_effects}}{Dataframe of random effects results.}

\item{\code{df_transform}}{Dataframe of transformed parameters.}

\item{\code{df_generate}}{Dataframe of generated quantities.}

\item{\code{opt_history}}{Dataframe of optimization history.}

\item{\code{transform}}{List of transformed parameters.}

\item{\code{generate}}{List of generated quantities.}

\item{\code{se_samples}}{List of simulated samples for SE estimation.}

\item{\code{par_unc}}{Numeric vector of unconstrained parameter estimates.}

\item{\code{vcov_unc}}{Variance-covariance matrix of parameters in unconstrained space.}

\item{\code{ci_method}}{Method used for CI estimation.}

\item{\code{laplace}}{Whether Laplace approximation was used.}

\item{\code{map}}{Parameter mapping used.}

\item{\code{df_method}}{Character string specifying the degrees of freedom calculation method.}

\item{\code{idx_fix_active}}{Numeric vector; mapping between active parameters and full unconstrained vector.}

\item{\code{show_df}}{Logical; whether to display degrees of freedom in the summary output.}
}
\if{html}{\out{</div>}}
}
\section{Methods}{
\subsection{Public methods}{
\itemize{
\item \href{#method-Classic_Fit-get_point_estimate}{\code{Classic_Fit$get_point_estimate()}}
\item \href{#method-Classic_Fit-new}{\code{Classic_Fit$new()}}
\item \href{#method-Classic_Fit-robust_se}{\code{Classic_Fit$robust_se()}}
\item \href{#method-Classic_Fit-compute_robust}{\code{Classic_Fit$compute_robust()}}
\item \href{#method-Classic_Fit-bootstrap}{\code{Classic_Fit$bootstrap()}}
\item \href{#method-Classic_Fit-compute_bootstrap}{\code{Classic_Fit$compute_bootstrap()}}
\item \href{#method-Classic_Fit-AIC}{\code{Classic_Fit$AIC()}}
\item \href{#method-Classic_Fit-BIC}{\code{Classic_Fit$BIC()}}
\item \href{#method-Classic_Fit-WAIC}{\code{Classic_Fit$WAIC()}}
\item \href{#method-Classic_Fit-diagnose}{\code{Classic_Fit$diagnose()}}
\item \href{#method-Classic_Fit-print}{\code{Classic_Fit$print()}}
\item \href{#method-Classic_Fit-logLik}{\code{Classic_Fit$logLik()}}
\item \href{#method-Classic_Fit-EAP}{\code{Classic_Fit$EAP()}}
\item \href{#method-Classic_Fit-MAP}{\code{Classic_Fit$MAP()}}
\item \href{#method-Classic_Fit-summary}{\code{Classic_Fit$summary()}}
\item \href{#method-Classic_Fit-anova}{\code{Classic_Fit$anova()}}
\item \href{#method-Classic_Fit-lsmeans}{\code{Classic_Fit$lsmeans()}}
\item \href{#method-Classic_Fit-.calc_contrast}{\code{Classic_Fit$.calc_contrast()}}
\item \href{#method-Classic_Fit-.get_lsmeans_df}{\code{Classic_Fit$.get_lsmeans_df()}}
\item \href{#method-Classic_Fit-.construct_par_list}{\code{Classic_Fit$.construct_par_list()}}
\item \href{#method-Classic_Fit-clone}{\code{Classic_Fit$clone()}}
}
}
\if{html}{\out{
<details open><summary>Inherited methods</summary>
<ul>
<li><span class="pkg-link" data-pkg="BayesRTMB" data-topic="RTMB_Fit_Base" data-id="estimate"><a href='../../BayesRTMB/html/RTMB_Fit_Base.html#method-RTMB_Fit_Base-estimate'><code>BayesRTMB::RTMB_Fit_Base$estimate()</code></a></span></li>
<li><span class="pkg-link" data-pkg="BayesRTMB" data-topic="RTMB_Fit_Base" data-id="fa_rotate"><a href='../../BayesRTMB/html/RTMB_Fit_Base.html#method-RTMB_Fit_Base-fa_rotate'><code>BayesRTMB::RTMB_Fit_Base$fa_rotate()</code></a></span></li>
<li><span class="pkg-link" data-pkg="BayesRTMB" data-topic="RTMB_Fit_Base" data-id="rotate"><a href='../../BayesRTMB/html/RTMB_Fit_Base.html#method-RTMB_Fit_Base-rotate'><code>BayesRTMB::RTMB_Fit_Base$rotate()</code></a></span></li>
</ul>
</details>
}}
\if{html}{\out{<hr>}}
\if{html}{\out{<a id="method-Classic_Fit-get_point_estimate"></a>}}
\if{latex}{\out{\hypertarget{method-Classic_Fit-get_point_estimate}{}}}
\subsection{Method \code{get_point_estimate()}}{
Get point estimate for a target parameter.
\subsection{Usage}{
\if{html}{\out{<div class="r">}}\preformatted{Classic_Fit$get_point_estimate(target, ...)}\if{html}{\out{</div>}}
}

\subsection{Arguments}{
\if{html}{\out{<div class="arguments">}}
\describe{
\item{\code{target}}{Target parameter name.}

\item{\code{...}}{Additional arguments, ignored for classic fits.}
}
\if{html}{\out{</div>}}
}
\subsection{Returns}{
Matrix, array, vector, or scalar point estimate.
}
}
\if{html}{\out{<hr>}}
\if{html}{\out{<a id="method-Classic_Fit-new"></a>}}
\if{latex}{\out{\hypertarget{method-Classic_Fit-new}{}}}
\subsection{Method \code{new()}}{
Create a new `Classic_Fit` object.
\subsection{Usage}{
\if{html}{\out{<div class="r">}}\preformatted{Classic_Fit$new(
  model,
  par_vec = NULL,
  par = NULL,
  objective = NULL,
  log_lik = NULL,
  restricted_log_lik = NULL,
  convergence = NULL,
  sd_rep = NULL,
  df_fixed = NULL,
  random_effects = NULL,
  df_transform = NULL,
  df_generate = NULL,
  opt_history = NULL,
  transform = NULL,
  generate = NULL,
  se_samples = NULL,
  par_unc = NULL,
  vcov_unc = NULL,
  ci_method = "wald",
  laplace = TRUE,
  map = NULL,
  test_results = list(),
  view = NULL,
  fit = NULL,
  vcov = NULL,
  df_method = "auto",
  idx_fix_active = NULL,
  show_df = TRUE,
  rss = NULL,
  df_residual = NULL,
  ...
)}\if{html}{\out{</div>}}
}

\subsection{Arguments}{
\if{html}{\out{<div class="arguments">}}
\describe{
\item{\code{model}}{The `RTMB_Model` object.}

\item{\code{par_vec}}{Numeric vector of parameter estimates.}

\item{\code{par}}{List of parameter estimates.}

\item{\code{objective}}{Final objective value.}

\item{\code{log_lik}}{Full log-likelihood used for information criteria.}

\item{\code{restricted_log_lik}}{Restricted log-likelihood, if classical estimation used REML-style marginalization.}

\item{\code{convergence}}{Convergence code.}

\item{\code{sd_rep}}{TMB sdreport object.}

\item{\code{df_fixed}}{Dataframe of fixed effects results.}

\item{\code{random_effects}}{Dataframe of random effects results.}

\item{\code{df_transform}}{Dataframe of transformed parameters.}

\item{\code{df_generate}}{Dataframe of generated quantities.}

\item{\code{opt_history}}{Dataframe of optimization history.}

\item{\code{transform}}{List of transformed parameters.}

\item{\code{generate}}{List of generated quantities.}

\item{\code{se_samples}}{List of simulated samples for SE estimation.}

\item{\code{par_unc}}{Parameter vector on unconstrained scale.}

\item{\code{vcov_unc}}{Covariance matrix on unconstrained scale.}

\item{\code{ci_method}}{Method used for CI estimation.}

\item{\code{laplace}}{Whether Laplace approximation was used.}

\item{\code{map}}{Parameter mapping used.}

\item{\code{test_results}}{List of additional test results (e.g., chisq.test).}

\item{\code{view}}{Character vector of parameter names to prioritize in summary.}

\item{\code{fit}}{Legacy argument for backward compatibility (maps to df_fixed).}

\item{\code{vcov}}{Variance-covariance matrix of parameters.}

\item{\code{df_method}}{Method for degrees of freedom calculation.}

\item{\code{idx_fix_active}}{Numeric vector; mapping between active parameters and full unconstrained vector.}

\item{\code{show_df}}{Logical; whether to display degrees of freedom in the summary output.}

\item{\code{rss}}{Residual sum of squares for classical linear models, if available.}

\item{\code{df_residual}}{Residual degrees of freedom for classical linear models, if available.}

\item{\code{...}}{Additional arguments passed to the constructor.}
}
\if{html}{\out{</div>}}
}
}
\if{html}{\out{<hr>}}
\if{html}{\out{<a id="method-Classic_Fit-robust_se"></a>}}
\if{latex}{\out{\hypertarget{method-Classic_Fit-robust_se}{}}}
\subsection{Method \code{robust_se()}}{
Compute robust standard errors (sandwich estimator).
\subsection{Usage}{
\if{html}{\out{<div class="r">}}\preformatted{Classic_Fit$robust_se(
  cluster = NULL,
  type = c("HC3", "HC0", "HC1", "CR1", "CR0"),
  inplace = FALSE,
  ...
)}\if{html}{\out{</div>}}
}

\subsection{Arguments}{
\if{html}{\out{<div class="arguments">}}
\describe{
\item{\code{cluster}}{Character; variable name for clustering.}

\item{\code{type}}{Character; "HC3" (default), "HC0", or "HC1" for non-clustered
robust SE; "CR1" or "CR0" for clustered robust SE.}

\item{\code{inplace}}{Logical; if FALSE, return a robust-SE copy without modifying the original fit.
If TRUE, update the object in place.}

\item{\code{...}}{Additional arguments.}
}
\if{html}{\out{</div>}}
}
\subsection{Returns}{
Self.
}
}
\if{html}{\out{<hr>}}
\if{html}{\out{<a id="method-Classic_Fit-compute_robust"></a>}}
\if{latex}{\out{\hypertarget{method-Classic_Fit-compute_robust}{}}}
\subsection{Method \code{compute_robust()}}{
(Deprecated) Use robust_se() instead.
\subsection{Usage}{
\if{html}{\out{<div class="r">}}\preformatted{Classic_Fit$compute_robust(...)}\if{html}{\out{</div>}}
}

\subsection{Arguments}{
\if{html}{\out{<div class="arguments">}}
\describe{
\item{\code{...}}{Arguments passed to robust_se(), including inplace.}
}
\if{html}{\out{</div>}}
}
\subsection{Returns}{
Self.
}
}
\if{html}{\out{<hr>}}
\if{html}{\out{<a id="method-Classic_Fit-bootstrap"></a>}}
\if{latex}{\out{\hypertarget{method-Classic_Fit-bootstrap}{}}}
\subsection{Method \code{bootstrap()}}{
Compute nonparametric bootstrap standard errors and confidence intervals.
Currently implemented only for mediation models.
Bootstrap refits mediation equations with base R lm.fit()/glm.fit() when possible,
and falls back to RTMB refits for unsupported families.
\subsection{Usage}{
\if{html}{\out{<div class="r">}}\preformatted{Classic_Fit$bootstrap(n_boot = 1000, seed = NULL, inplace = FALSE, ...)}\if{html}{\out{</div>}}
}

\subsection{Arguments}{
\if{html}{\out{<div class="arguments">}}
\describe{
\item{\code{n_boot}}{Integer; number of bootstrap samples.}

\item{\code{seed}}{Optional integer random seed.}

\item{\code{inplace}}{Logical; if FALSE, return a bootstrap-SE copy without modifying the original fit.
If TRUE, update the object in place.}

\item{\code{...}}{Additional arguments.}
}
\if{html}{\out{</div>}}
}
\subsection{Returns}{
A Classic_Fit object.
}
}
\if{html}{\out{<hr>}}
\if{html}{\out{<a id="method-Classic_Fit-compute_bootstrap"></a>}}
\if{latex}{\out{\hypertarget{method-Classic_Fit-compute_bootstrap}{}}}
\subsection{Method \code{compute_bootstrap()}}{
Compute nonparametric bootstrap standard errors and confidence intervals.
Currently implemented only for mediation models.
Bootstrap refits mediation equations with base R lm.fit()/glm.fit() when possible,
and falls back to RTMB refits for unsupported families.
\subsection{Usage}{
\if{html}{\out{<div class="r">}}\preformatted{Classic_Fit$compute_bootstrap(n_boot = 1000, seed = NULL, inplace = FALSE, ...)}\if{html}{\out{</div>}}
}

\subsection{Arguments}{
\if{html}{\out{<div class="arguments">}}
\describe{
\item{\code{n_boot}}{Integer; number of bootstrap samples.}

\item{\code{seed}}{Optional integer random seed.}

\item{\code{inplace}}{Logical; if FALSE, return a bootstrap-SE copy without modifying the original fit.
If TRUE, update the object in place.}

\item{\code{...}}{Additional arguments.}
}
\if{html}{\out{</div>}}
}
\subsection{Returns}{
A Classic_Fit object.
}
}
\if{html}{\out{<hr>}}
\if{html}{\out{<a id="method-Classic_Fit-AIC"></a>}}
\if{latex}{\out{\hypertarget{method-Classic_Fit-AIC}{}}}
\subsection{Method \code{AIC()}}{
Get the AIC of the fitted model.
\subsection{Usage}{
\if{html}{\out{<div class="r">}}\preformatted{Classic_Fit$AIC()}\if{html}{\out{</div>}}
}

}
\if{html}{\out{<hr>}}
\if{html}{\out{<a id="method-Classic_Fit-BIC"></a>}}
\if{latex}{\out{\hypertarget{method-Classic_Fit-BIC}{}}}
\subsection{Method \code{BIC()}}{
Get the BIC of the fitted model.
\subsection{Usage}{
\if{html}{\out{<div class="r">}}\preformatted{Classic_Fit$BIC()}\if{html}{\out{</div>}}
}

}
\if{html}{\out{<hr>}}
\if{html}{\out{<a id="method-Classic_Fit-WAIC"></a>}}
\if{latex}{\out{\hypertarget{method-Classic_Fit-WAIC}{}}}
\subsection{Method \code{WAIC()}}{
WAIC is not defined for classical fits.
\subsection{Usage}{
\if{html}{\out{<div class="r">}}\preformatted{Classic_Fit$WAIC()}\if{html}{\out{</div>}}
}

}
\if{html}{\out{<hr>}}
\if{html}{\out{<a id="method-Classic_Fit-diagnose"></a>}}
\if{latex}{\out{\hypertarget{method-Classic_Fit-diagnose}{}}}
\subsection{Method \code{diagnose()}}{
Run basic diagnostics for the classical fit.
\subsection{Usage}{
\if{html}{\out{<div class="r">}}\preformatted{Classic_Fit$diagnose(...)}\if{html}{\out{</div>}}
}

\subsection{Arguments}{
\if{html}{\out{<div class="arguments">}}
\describe{
\item{\code{...}}{Additional arguments passed to `diagnose_classic_fit()`.}
}
\if{html}{\out{</div>}}
}
\subsection{Returns}{
A `diagnose_BayesRTMB` object.
}
}
\if{html}{\out{<hr>}}
\if{html}{\out{<a id="method-Classic_Fit-print"></a>}}
\if{latex}{\out{\hypertarget{method-Classic_Fit-print}{}}}
\subsection{Method \code{print()}}{
Print the fit results.
\subsection{Usage}{
\if{html}{\out{<div class="r">}}\preformatted{Classic_Fit$print(...)}\if{html}{\out{</div>}}
}

\subsection{Arguments}{
\if{html}{\out{<div class="arguments">}}
\describe{
\item{\code{...}}{Additional arguments passed to `summary()`.}
}
\if{html}{\out{</div>}}
}
}
\if{html}{\out{<hr>}}
\if{html}{\out{<a id="method-Classic_Fit-logLik"></a>}}
\if{latex}{\out{\hypertarget{method-Classic_Fit-logLik}{}}}
\subsection{Method \code{logLik()}}{
Get the Log-Likelihood of the fitted model.
\subsection{Usage}{
\if{html}{\out{<div class="r">}}\preformatted{Classic_Fit$logLik()}\if{html}{\out{</div>}}
}

}
\if{html}{\out{<hr>}}
\if{html}{\out{<a id="method-Classic_Fit-EAP"></a>}}
\if{latex}{\out{\hypertarget{method-Classic_Fit-EAP}{}}}
\subsection{Method \code{EAP()}}{
EAP estimates are not available for Classic_Fit.
\subsection{Usage}{
\if{html}{\out{<div class="r">}}\preformatted{Classic_Fit$EAP(...)}\if{html}{\out{</div>}}
}

\subsection{Arguments}{
\if{html}{\out{<div class="arguments">}}
\describe{
\item{\code{...}}{Ignored.}
}
\if{html}{\out{</div>}}
}
}
\if{html}{\out{<hr>}}
\if{html}{\out{<a id="method-Classic_Fit-MAP"></a>}}
\if{latex}{\out{\hypertarget{method-Classic_Fit-MAP}{}}}
\subsection{Method \code{MAP()}}{
MAP estimates are not available for Classic_Fit.
\subsection{Usage}{
\if{html}{\out{<div class="r">}}\preformatted{Classic_Fit$MAP(...)}\if{html}{\out{</div>}}
}

\subsection{Arguments}{
\if{html}{\out{<div class="arguments">}}
\describe{
\item{\code{...}}{Ignored.}
}
\if{html}{\out{</div>}}
}
}
\if{html}{\out{<hr>}}
\if{html}{\out{<a id="method-Classic_Fit-summary"></a>}}
\if{latex}{\out{\hypertarget{method-Classic_Fit-summary}{}}}
\subsection{Method \code{summary()}}{
Display a summary of the estimation results.
\subsection{Usage}{
\if{html}{\out{<div class="r">}}\preformatted{Classic_Fit$summary(view = NULL, digits = 5, max_rows = 10, ranef = FALSE)}\if{html}{\out{</div>}}
}

\subsection{Arguments}{
\if{html}{\out{<div class="arguments">}}
\describe{
\item{\code{view}}{Character vector of parameter names to prioritize or filter by.}

\item{\code{digits}}{Number of digits to print for estimates.}

\item{\code{max_rows}}{Maximum number of rows to display in the coefficient table.}

\item{\code{ranef}}{Logical; whether to show random effects in the summary.}
}
\if{html}{\out{</div>}}
}
}
\if{html}{\out{<hr>}}
\if{html}{\out{<a id="method-Classic_Fit-anova"></a>}}
\if{latex}{\out{\hypertarget{method-Classic_Fit-anova}{}}}
\subsection{Method \code{anova()}}{
Perform ANOVA (Wald F-tests / Chisq-tests) on the fitted model.
\subsection{Usage}{
\if{html}{\out{<div class="r">}}\preformatted{Classic_Fit$anova(method = c("reml", "ls"), type = 3)}\if{html}{\out{</div>}}
}

\subsection{Arguments}{
\if{html}{\out{<div class="arguments">}}
\describe{
\item{\code{method}}{Character; "reml" (standard) or "ls" (experimental).}

\item{\code{type}}{Integer; Type of Sum of Squares (only Type III supported currently).}
}
\if{html}{\out{</div>}}
}
\subsection{Returns}{
A data frame containing the ANOVA table.
}
}
\if{html}{\out{<hr>}}
\if{html}{\out{<a id="method-Classic_Fit-lsmeans"></a>}}
\if{latex}{\out{\hypertarget{method-Classic_Fit-lsmeans}{}}}
\subsection{Method \code{lsmeans()}}{
Calculate Least Squares Means (Marginal Means) and contrasts.
\subsection{Usage}{
\if{html}{\out{<div class="r">}}\preformatted{Classic_Fit$lsmeans(
  specs = NULL,
  pairwise = FALSE,
  simple = NULL,
  adjust = "holm",
  protect = FALSE
)}\if{html}{\out{</div>}}
}

\subsection{Arguments}{
\if{html}{\out{<div class="arguments">}}
\describe{
\item{\code{specs}}{Character vector of factors to calculate means for.}

\item{\code{pairwise}}{Logical; whether to perform pairwise comparisons.}

\item{\code{simple}}{Character vector of factors to hold constant for simple main effects.}

\item{\code{adjust}}{Character; p-value adjustment method (e.g., "bonferroni", "holm", "none").}

\item{\code{protect}}{Logical; whether to use hierarchical (protected) testing.}
}
\if{html}{\out{</div>}}
}
\subsection{Returns}{
A data frame containing the marginal means or contrasts.
}
}
\if{html}{\out{<hr>}}
\if{html}{\out{<a id="method-Classic_Fit-.calc_contrast"></a>}}
\if{latex}{\out{\hypertarget{method-Classic_Fit-.calc_contrast}{}}}
\subsection{Method \code{.calc_contrast()}}{
(Internal) Calculate metrics for a contrast.
\subsection{Usage}{
\if{html}{\out{<div class="r">}}\preformatted{Classic_Fit$.calc_contrast(L, specs)}\if{html}{\out{</div>}}
}

\subsection{Arguments}{
\if{html}{\out{<div class="arguments">}}
\describe{
\item{\code{L}}{Contrast matrix.}

\item{\code{specs}}{Variable names for lsmeans.}
}
\if{html}{\out{</div>}}
}
\subsection{Returns}{
A data frame with estimate, SE, df, t-value, and p-value.
}
}
\if{html}{\out{<hr>}}
\if{html}{\out{<a id="method-Classic_Fit-.get_lsmeans_df"></a>}}
\if{latex}{\out{\hypertarget{method-Classic_Fit-.get_lsmeans_df}{}}}
\subsection{Method \code{.get_lsmeans_df()}}{
(Internal) Get representative DF for lsmeans.
\subsection{Usage}{
\if{html}{\out{<div class="r">}}\preformatted{Classic_Fit$.get_lsmeans_df(specs)}\if{html}{\out{</div>}}
}

\subsection{Arguments}{
\if{html}{\out{<div class="arguments">}}
\describe{
\item{\code{specs}}{Variable names for lsmeans.}
}
\if{html}{\out{</div>}}
}
\subsection{Returns}{
Degrees of freedom.
}
}
\if{html}{\out{<hr>}}
\if{html}{\out{<a id="method-Classic_Fit-.construct_par_list"></a>}}
\if{latex}{\out{\hypertarget{method-Classic_Fit-.construct_par_list}{}}}
\subsection{Method \code{.construct_par_list()}}{
(Internal) Construct a list of parameters from the fit.
\subsection{Usage}{
\if{html}{\out{<div class="r">}}\preformatted{Classic_Fit$.construct_par_list(fit)}\if{html}{\out{</div>}}
}

\subsection{Arguments}{
\if{html}{\out{<div class="arguments">}}
\describe{
\item{\code{fit}}{The fit result (dataframe or lm object).}
}
\if{html}{\out{</div>}}
}
\subsection{Returns}{
A named list of parameters.
}
}
\if{html}{\out{<hr>}}
\if{html}{\out{<a id="method-Classic_Fit-clone"></a>}}
\if{latex}{\out{\hypertarget{method-Classic_Fit-clone}{}}}
\subsection{Method \code{clone()}}{
The objects of this class are cloneable with this method.
\subsection{Usage}{
\if{html}{\out{<div class="r">}}\preformatted{Classic_Fit$clone(deep = FALSE)}\if{html}{\out{</div>}}
}

\subsection{Arguments}{
\if{html}{\out{<div class="arguments">}}
\describe{
\item{\code{deep}}{Whether to make a deep clone.}
}
\if{html}{\out{</div>}}
}
}
}
