Add support for cmdstanr backend (#1806)

* init

* add suggested packages

* use environment variables and align more with Py package

* remove additional testing logic, default to lbfgs

* Remove Newton specifier from test because cmdstanr expects newton

Co-authored-by: Ben Letham <bletham@gmail.com>

R/DESCRIPTION

@@ -35,14 +35,18 @@ Imports:
     tidyr (>= 0.6.1),
     xts
 Suggests:
+    cmdstanr,
+    posterior,
     knitr,
     testthat,
     readr,
     rmarkdown
+Additional_repositories:
+    https://mc-stan.org/r-packages/
 SystemRequirements: GNU make, C++11
 Biarch: true
 License: MIT + file LICENSE
-LinkingTo: 
+LinkingTo:
     BH (>= 1.66.0),
     Rcpp (>= 0.12.0),
     RcppParallel (>= 5.0.1),

R/R/prophet.R

@@ -16,54 +16,56 @@ globalVariables(c(
 #' Prophet forecaster.
 #'
 #' @param df (optional) Dataframe containing the history. Must have columns ds
-#'  (date type) and y, the time series. If growth is logistic, then df must
-#'  also have a column cap that specifies the capacity at each ds. If not
-#'  provided, then the model object will be instantiated but not fit; use
-#'  fit.prophet(m, df) to fit the model.
-#' @param growth String 'linear', 'logistic', or 'flat' to specify a linear, logistic
-#'  or flat trend.
+#'   (date type) and y, the time series. If growth is logistic, then df must
+#'   also have a column cap that specifies the capacity at each ds. If not
+#'   provided, then the model object will be instantiated but not fit; use
+#'   fit.prophet(m, df) to fit the model.
+#' @param growth String 'linear', 'logistic', or 'flat' to specify a linear,
+#'   logistic or flat trend.
 #' @param changepoints Vector of dates at which to include potential
-#'  changepoints. If not specified, potential changepoints are selected
-#'  automatically.
+#'   changepoints. If not specified, potential changepoints are selected
+#'   automatically.
 #' @param n.changepoints Number of potential changepoints to include. Not used
-#'  if input `changepoints` is supplied. If `changepoints` is not supplied,
-#'  then n.changepoints potential changepoints are selected uniformly from the
-#'  first `changepoint.range` proportion of df$ds.
+#'   if input `changepoints` is supplied. If `changepoints` is not supplied,
+#'   then n.changepoints potential changepoints are selected uniformly from the
+#'   first `changepoint.range` proportion of df$ds.
 #' @param changepoint.range Proportion of history in which trend changepoints
-#'  will be estimated. Defaults to 0.8 for the first 80%. Not used if
-#'  `changepoints` is specified.
-#' @param yearly.seasonality Fit yearly seasonality. Can be 'auto', TRUE,
-#'  FALSE, or a number of Fourier terms to generate.
-#' @param weekly.seasonality Fit weekly seasonality. Can be 'auto', TRUE,
-#'  FALSE, or a number of Fourier terms to generate.
-#' @param daily.seasonality Fit daily seasonality. Can be 'auto', TRUE,
-#' FALSE, or a number of Fourier terms to generate.
+#'   will be estimated. Defaults to 0.8 for the first 80%. Not used if
+#'   `changepoints` is specified.
+#' @param yearly.seasonality Fit yearly seasonality. Can be 'auto', TRUE, FALSE,
+#'   or a number of Fourier terms to generate.
+#' @param weekly.seasonality Fit weekly seasonality. Can be 'auto', TRUE, FALSE,
+#'   or a number of Fourier terms to generate.
+#' @param daily.seasonality Fit daily seasonality. Can be 'auto', TRUE, FALSE,
+#'   or a number of Fourier terms to generate.
 #' @param holidays data frame with columns holiday (character) and ds (date
-#'  type)and optionally columns lower_window and upper_window which specify a
-#'  range of days around the date to be included as holidays. lower_window=-2
-#'  will include 2 days prior to the date as holidays. Also optionally can have
-#'  a column prior_scale specifying the prior scale for each holiday.
+#'   type)and optionally columns lower_window and upper_window which specify a
+#'   range of days around the date to be included as holidays. lower_window=-2
+#'   will include 2 days prior to the date as holidays. Also optionally can have
+#'   a column prior_scale specifying the prior scale for each holiday.
 #' @param seasonality.mode 'additive' (default) or 'multiplicative'.
 #' @param seasonality.prior.scale Parameter modulating the strength of the
-#'  seasonality model. Larger values allow the model to fit larger seasonal
-#'  fluctuations, smaller values dampen the seasonality. Can be specified for
-#'  individual seasonalities using add_seasonality.
+#'   seasonality model. Larger values allow the model to fit larger seasonal
+#'   fluctuations, smaller values dampen the seasonality. Can be specified for
+#'   individual seasonalities using add_seasonality.
 #' @param holidays.prior.scale Parameter modulating the strength of the holiday
-#'  components model, unless overridden in the holidays input.
+#'   components model, unless overridden in the holidays input.
 #' @param changepoint.prior.scale Parameter modulating the flexibility of the
-#'  automatic changepoint selection. Large values will allow many changepoints,
-#'  small values will allow few changepoints.
+#'   automatic changepoint selection. Large values will allow many changepoints,
+#'   small values will allow few changepoints.
 #' @param mcmc.samples Integer, if greater than 0, will do full Bayesian
-#'  inference with the specified number of MCMC samples. If 0, will do MAP
-#'  estimation.
+#'   inference with the specified number of MCMC samples. If 0, will do MAP
+#'   estimation.
 #' @param interval.width Numeric, width of the uncertainty intervals provided
-#'  for the forecast. If mcmc.samples=0, this will be only the uncertainty
-#'  in the trend using the MAP estimate of the extrapolated generative model.
-#'  If mcmc.samples>0, this will be integrated over all model parameters,
-#'  which will include uncertainty in seasonality.
+#'   for the forecast. If mcmc.samples=0, this will be only the uncertainty in
+#'   the trend using the MAP estimate of the extrapolated generative model. If
+#'   mcmc.samples>0, this will be integrated over all model parameters, which
+#'   will include uncertainty in seasonality.
 #' @param uncertainty.samples Number of simulated draws used to estimate
-#'  uncertainty intervals. Settings this value to 0 or False will disable
-#'  uncertainty estimation and speed up the calculation.
+#'   uncertainty intervals. Settings this value to 0 or False will disable
+#'   uncertainty estimation and speed up the calculation.
+#' @param backend Whether to use the "rstan" or "cmdstanr" backend to fit the
+#'   model. If not provided, uses the R_STAN_BACKEND environment variable.
 #' @param fit Boolean, if FALSE the model is initialized but not fit.
 #' @param ... Additional arguments, passed to \code{\link{fit.prophet}}
 #'
@@ -99,12 +101,15 @@ prophet <- function(df = NULL,
                     interval.width = 0.80,
                     uncertainty.samples = 1000,
                     fit = TRUE,
+                    backend = NULL,
                     ...
 ) {
   if (!is.null(changepoints)) {
     n.changepoints <- length(changepoints)
   }
 
+  if (is.null(backend)) backend <- get_stan_backend()
+
   m <- list(
     growth = growth,
     changepoints = changepoints,
@@ -121,6 +126,7 @@ prophet <- function(df = NULL,
     mcmc.samples = mcmc.samples,
     interval.width = interval.width,
     uncertainty.samples = uncertainty.samples,
+    backend = backend,
     specified.changepoints = !is.null(changepoints),
     start = NULL,  # This and following attributes are set during fitting
     y.scale = NULL,
@@ -1068,7 +1074,7 @@ set_auto_seasonalities <- function(m) {
 #' Initialize flat growth.
 #'
 #' Provides a strong initialization for flat growth by setting the
-#' growth to 0 and calculates the offset parameter that pass the 
+#' growth to 0 and calculates the offset parameter that pass the
 #' function through the mean of the the y_scaled values.
 #'
 #' @param df Data frame with columns ds (date), y_scaled (scaled time series),
@@ -1227,11 +1233,7 @@ fit.prophet <- function(m, df, ...) {
     kinit <- logistic_growth_init(history)
   }
 
-  if (exists(".prophet.stan.model", where = prophet_model_env)) {
-    model <- get('.prophet.stan.model', envir = prophet_model_env)
-  } else {
-    model <- stanmodels$prophet
-  }
+  model <- .load_model(m$backend)
 
   stan_init <- function() {
     list(k = kinit[1],
@@ -1242,45 +1244,24 @@ fit.prophet <- function(m, df, ...) {
     )
   }
 
-  if (min(history$y) == max(history$y) & 
+  if (min(history$y) == max(history$y) &
         (m$growth %in% c('linear', 'flat'))) {
     # Nothing to fit.
     m$params <- stan_init()
     m$params$sigma_obs <- 0.
     n.iteration <- 1.
-  } else if (m$mcmc.samples > 0) {
-    args <- list(
-      object = model,
-      data = dat,
-      init = stan_init,
-      iter = m$mcmc.samples
-    )
-    args <- utils::modifyList(args, list(...))
-    m$stan.fit <- do.call(rstan::sampling, args)
-    m$params <- rstan::extract(m$stan.fit)
-    n.iteration <- length(m$params$k)
   } else {
-    args <- list(
-      object = model,
-      data = dat,
-      init = stan_init,
-      algorithm = if(dat$T < 100) {'Newton'} else {'LBFGS'},
-      iter = 1e4,
-      as_vector = FALSE
-    )
-    args <- utils::modifyList(args, list(...))
-    m$stan.fit <- do.call(rstan::optimizing, args)
-    if (m$stan.fit$return_code != 0) {
-      message(
-        'Optimization terminated abnormally. Falling back to Newton optimizer.'
-      )
-      args$algorithm = 'Newton'
-      m$stan.fit <- do.call(rstan::optimizing, args)
+    if (m$mcmc.samples > 0) {
+      args <- .stan_args(model, dat, stan_init, m$backend, type = "mcmc", m$mcmc.samples, ...)
+      model_output <- .sampling(args, m$backend)
+    } else {
+      args <- .stan_args(model, dat, stan_init, m$backend, type = "optimize", ...)
+      model_output <- .fit(args, m$backend)
     }
-    m$params <- m$stan.fit$par
-    n.iteration <- 1
+    m$stan.fit <- model_output$stan_fit
+    m$params <- model_output$params
+    n.iteration <- model_output$n_iteration
   }
-
   # Cast the parameters to have consistent form, whether full bayes or MAP
   for (name in c('delta', 'beta')){
     m$params[[name]] <- matrix(m$params[[name]], nrow = n.iteration)

R/R/stan_backends.R

@@ -0,0 +1,256 @@
+#' Get the stan backend defined in the environment variables.
+#'
+#' @return 'rstan' or 'cmdstanr'. 'rstan' if variable is not set.
+#' @keywords internal
+get_stan_backend <- function() {
+  backend_setting <- Sys.getenv("R_STAN_BACKEND", "RSTAN")
+  if (backend_setting %in% c("RSTAN", "CMDSTANR")) {
+    backend <- switch(
+      backend_setting,
+      "RSTAN" = "rstan",
+      "CMDSTANR" = "cmdstanr"
+    )
+    if (backend == "cmdstanr") check_cmdstanr()
+    return(backend)
+  } else {
+    return("rstan")
+  }
+}
+
+#' Check that the required packages for using the cmdstanr backend are installed.
+#'
+#' @return NULL if successful, and prints the current version of cmdstan being used.
+#' @keywords internal
+check_cmdstanr <- function() {
+  if (!requireNamespace("cmdstanr", quietly = TRUE)) {
+    stop(
+      "Package \"cmdstanr\" needed to use cmdstanr backend. See installation instructions: https://mc-stan.org/cmdstanr/.",
+      call. = FALSE
+    )
+  }
+  if (!requireNamespace("posterior", quietly = TRUE)) {
+    stop(
+      "Package \"posterior\" needed to use cmdstanr backend. See installation instructions: https://mc-stan.org/posterior/.",
+      call. = FALSE
+    )
+  }
+  cmdstanr_version <- cmdstanr::cmdstan_version()
+  return(invisible(TRUE))
+}
+
+#' Load the Prophet Stan model.
+#'
+#' @param backend "rstan" or "cmdstanr".
+#'
+#' @return stanmodel object if backend = "rstan", CmdStanModel object if backend = "cmdstanr"
+#' @keywords internal
+.load_model <- function(backend) {
+  switch(
+    backend,
+    "rstan" = .load_model_rstan(),
+    "cmdstanr" = .load_model_cmdstanr()
+  )
+}
+
+#' @rdname .load_model
+.load_model_rstan <- function() {
+  if (exists(".prophet.stan.model", where = prophet_model_env)) {
+    model <- get('.prophet.stan.model', envir = prophet_model_env)
+  } else {
+    model <- stanmodels$prophet
+  }
+
+  return(model)
+}
+
+#' @rdname .load_model
+.load_model_cmdstanr <- function() {
+  model_file <- system.file(
+    "stan",
+    "prophet.stan",
+    package = "prophet",
+    mustWork = TRUE
+  )
+  model <- cmdstanr::cmdstan_model(model_file)
+
+  return(model)
+}
+
+#' Gives Stan arguments the appropriate names depending on the chosen Stan backend.
+#'
+#' @param model Model object.
+#' @param dat List containing data to use in fitting.
+#' @param stan_init Function to initialize parameters for stan fit.
+#' @param backend "rstan" or "cmdstanr".
+#' @param type "mcmc" or "optimize".
+#' @param mcmc_samples Integer, if greater than 0, will do full Bayesian
+#'  inference with the specified number of MCMC samples. If 0, will do MAP
+#'  estimation.
+#'
+#' @return Named list of arguments.
+#' @keywords internal
+.stan_args <- function(model, dat, stan_init, backend, type, mcmc_samples = 0, ...) {
+  args <- switch(
+    backend,
+    "rstan" = .stan_args_rstan(model, dat, stan_init, type, mcmc_samples),
+    "cmdstanr" = .stan_args_cmdstanr(model, dat, stan_init, type, mcmc_samples)
+  )
+  args <- utils::modifyList(args, list(...))
+
+  return(args)
+}
+
+#' @rdname .stan_args
+.stan_args_rstan <- function(model, dat, stan_init, type, mcmc_samples = NULL) {
+  if (type == "mcmc") {
+    args <- list(
+      object = model,
+      data = dat,
+      init = stan_init,
+      iter = mcmc_samples,
+      chains = 4
+    )
+  } else if (type == "optimize") {
+    args <- list(
+      object = model,
+      data = dat,
+      init = stan_init,
+      algorithm = if(dat$T < 100) {'Newton'} else {'LBFGS'},
+      iter = 1e4,
+      as_vector = FALSE
+    )
+  }
+
+  return(args)
+}
+
+#' @rdname .stan_args
+.stan_args_cmdstanr <- function(model, dat, stan_init, type, mcmc_samples = NULL) {
+  if (type == "mcmc") {
+    args <- list(
+      object = model,
+      data = dat,
+      init = stan_init,
+      iter_warmup = mcmc_samples / 2,
+      iter_sampling = mcmc_samples / 2,
+      chains = 4,
+      refresh = 0,
+      show_messages = FALSE
+    )
+  } else if (type == "optimize") {
+    args <- list(
+      object = model,
+      data = dat,
+      init = stan_init,
+      algorithm = if(dat$T < 100) {'newton'} else {'lbfgs'},
+      iter = 1e4,
+      refresh = 0
+    )
+  }
+
+  return(args)
+}
+
+#' Obtain the point estimates of the parameters of the Prophet model using
+#' stan's optimization algorithms.
+#'
+#' @param args Named list of arguments suitable for the chosen backend. Must
+#'   include arguments required for optimization.
+#' @param backend "rstan" or "cmdstanr".
+#'
+#' @return A named list containing "stan_fit" (the fitted stan object),
+#'   "params", and "n_iteration"
+#' @keywords internal
+.fit <- function(args, backend) {
+  switch(
+    backend,
+    "rstan" = .fit_rstan(args),
+    "cmdstanr" = .fit_cmdstanr(args)
+  )
+}
+
+#' Obtain the joint posterior distribution of the parameters of the Prophet
+#' model using MCMC sampling.
+#'
+#' @param args Named list of arguments suitable for the chosen backend. Must
+#'   include arguments required for MCMC sampling.
+#' @param backend "rstan" or "cmdstanr".
+#'
+#' @return A named list containing "stan_fit" (the fitted stan object),
+#'   "params", and "n_iteration"
+#' @keywords internal
+.sampling <- function(args, backend) {
+  switch(
+    backend,
+    "rstan" = .sampling_rstan(args),
+    "cmdstanr" = .sampling_cmdstanr(args)
+  )
+}
+
+#' @rdname .fit
+.fit_rstan <- function(args) {
+  model_output <- list()
+  model_output$stan_fit <- do.call(rstan::optimizing, args)
+  if (model_output$stan_fit$return_code != 0) {
+    message(
+      'Optimization terminated abnormally. Falling back to Newton optimizer.'
+    )
+    args$algorithm = 'Newton'
+    model_output$stan_fit <- do.call(rstan::optimizing, args)
+  }
+  model_output$params <- model_output$stan_fit$par
+  model_output$n_iteration <- 1
+
+  return(model_output)
+}
+
+#' @rdname .sampling
+.sampling_rstan <- function(args) {
+  model_output <- list()
+  model_output$stan_fit <- do.call(rstan::sampling, args)
+  model_output$params <- rstan::extract(model_output$stan_fit)
+  model_output$n_iteration <- length(model_output$params$k)
+
+  return(model_output)
+}
+
+#' @rdname .fit
+.fit_cmdstanr <- function(args) {
+  # TODO: Replace with method to extract parameter names once implemented in cmdstanr
+  param_names <- c("k", "m", "delta", "sigma_obs", "beta", "trend")
+  model_output <- list()
+  model <- args$object
+  args$object <- NULL
+  model_output$stan_fit <- do.call(model$optimize, args)
+  if (model_output$stan_fit$return_codes()[1] != 0) {
+    message(
+      'Optimization terminated abnormally. Falling back to Newton optimizer.'
+    )
+    args$algorithm = 'newton'
+    model_output$stan_fit <- do.call(model$optimize, args)
+  }
+  model_output$params <- list()
+  for (name in param_names) {
+    model_output$params[[name]] <- unname(model_output$stan_fit$mle(name))
+  }
+  model_output$n_iteration <- 1
+
+  return(model_output)
+}
+
+#' @rdname .sampling
+.sampling_cmdstanr <- function(args) {
+  param_names <- c("k", "m", "delta", "sigma_obs", "beta", "trend")
+  model_output <- list()
+  model <- args$object
+  args$object <- NULL
+  param_names <- c(param_names, "lp__")
+  model_output$stan_fit <- do.call(model$sample, args)
+  model_output$params <- list()
+  for (name in param_names) {
+    model_output$params[[name]] <- posterior::as_draws_matrix(model_output$stan_fit$draws(name))
+  }
+  model_output$n_iteration <- nrow(model_output$params$k)
+
+  return(model_output)
+}

R/man/check_cmdstanr.Rd

@@ -0,0 +1,15 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/stan_backends.R
+\name{check_cmdstanr}
+\alias{check_cmdstanr}
+\title{Check that the required packages for using the cmdstanr backend are installed.}
+\usage{
+check_cmdstanr()
+}
+\value{
+NULL if successful, and prints the current version of cmdstan being used.
+}
+\description{
+Check that the required packages for using the cmdstanr backend are installed.
+}
+\keyword{internal}

R/man/dot-fit.Rd

@@ -0,0 +1,24 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/stan_backends.R
+\name{.fit}
+\alias{.fit}
+\title{Obtain the point estimates of the parameters of the Prophet model using
+stan's optimization algorithms.}
+\usage{
+.fit(args, backend)
+}
+\arguments{
+\item{args}{Named list of arguments suitable for the chosen backend. Must
+include arguments required for optimization.}
+
+\item{backend}{"rstan" or "cmdstanr".}
+}
+\value{
+A named list containing "stan_fit" (the fitted stan object),
+  "params", and "n_iteration"
+}
+\description{
+Obtain the point estimates of the parameters of the Prophet model using
+stan's optimization algorithms.
+}
+\keyword{internal}

R/man/dot-load_model.Rd

@@ -0,0 +1,18 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/stan_backends.R
+\name{.load_model}
+\alias{.load_model}
+\title{Load the Prophet Stan model.}
+\usage{
+.load_model(backend)
+}
+\arguments{
+\item{backend}{"rstan" or "cmdstanr".}
+}
+\value{
+stanmodel object if backend = "rstan", CmdStanModel object if backend = "cmdstanr"
+}
+\description{
+Load the Prophet Stan model.
+}
+\keyword{internal}

R/man/dot-sampling.Rd

@@ -0,0 +1,24 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/stan_backends.R
+\name{.sampling}
+\alias{.sampling}
+\title{Obtain the joint posterior distribution of the parameters of the Prophet
+model using MCMC sampling.}
+\usage{
+.sampling(args, backend)
+}
+\arguments{
+\item{args}{Named list of arguments suitable for the chosen backend. Must
+include arguments required for MCMC sampling.}
+
+\item{backend}{"rstan" or "cmdstanr".}
+}
+\value{
+A named list containing "stan_fit" (the fitted stan object),
+  "params", and "n_iteration"
+}
+\description{
+Obtain the joint posterior distribution of the parameters of the Prophet
+model using MCMC sampling.
+}
+\keyword{internal}

R/man/dot-stan_args.Rd

@@ -0,0 +1,30 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/stan_backends.R
+\name{.stan_args}
+\alias{.stan_args}
+\title{Gives Stan arguments the appropriate names depending on the chosen Stan backend.}
+\usage{
+.stan_args(model, dat, stan_init, backend, type, mcmc_samples = 0, ...)
+}
+\arguments{
+\item{model}{Model object.}
+
+\item{dat}{List containing data to use in fitting.}
+
+\item{stan_init}{Function to initialize parameters for stan fit.}
+
+\item{backend}{"rstan" or "cmdstanr".}
+
+\item{type}{"mcmc" or "optimize".}
+
+\item{mcmc_samples}{Integer, if greater than 0, will do full Bayesian
+inference with the specified number of MCMC samples. If 0, will do MAP
+estimation.}
+}
+\value{
+Named list of arguments.
+}
+\description{
+Gives Stan arguments the appropriate names depending on the chosen Stan backend.
+}
+\keyword{internal}

R/man/flat_growth_init.Rd

@@ -16,7 +16,7 @@ A vector (k, m) with the rate (k) and offset (m) of the flat
 }
 \description{
 Provides a strong initialization for flat growth by setting the
-growth to 0 and calculates the offset parameter that pass the 
+growth to 0 and calculates the offset parameter that pass the
 function through the mean of the the y_scaled values.
 }
 \keyword{internal}

R/man/get_stan_backend.Rd

@@ -0,0 +1,15 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/stan_backends.R
+\name{get_stan_backend}
+\alias{get_stan_backend}
+\title{Get the stan backend defined in the environment variables.}
+\usage{
+get_stan_backend()
+}
+\value{
+'rstan' or 'cmdstanr'. 'rstan' if variable is not set.
+}
+\description{
+Get the stan backend defined in the environment variables.
+}
+\keyword{internal}

R/man/prophet.Rd

@@ -22,6 +22,7 @@ prophet(
   interval.width = 0.8,
   uncertainty.samples = 1000,
   fit = TRUE,
+  backend = NULL,
   ...
 )
 }
@@ -32,8 +33,8 @@ also have a column cap that specifies the capacity at each ds. If not
 provided, then the model object will be instantiated but not fit; use
 fit.prophet(m, df) to fit the model.}
 
-\item{growth}{String 'linear', 'logistic', or 'flat' to specify a linear, logistic
-or flat trend.}
+\item{growth}{String 'linear', 'logistic', or 'flat' to specify a linear,
+logistic or flat trend.}
 
 \item{changepoints}{Vector of dates at which to include potential
 changepoints. If not specified, potential changepoints are selected
@@ -48,14 +49,14 @@ first `changepoint.range` proportion of df$ds.}
 will be estimated. Defaults to 0.8 for the first 80%. Not used if
 `changepoints` is specified.}
 
-\item{yearly.seasonality}{Fit yearly seasonality. Can be 'auto', TRUE,
-FALSE, or a number of Fourier terms to generate.}
+\item{yearly.seasonality}{Fit yearly seasonality. Can be 'auto', TRUE, FALSE,
+or a number of Fourier terms to generate.}
 
-\item{weekly.seasonality}{Fit weekly seasonality. Can be 'auto', TRUE,
-FALSE, or a number of Fourier terms to generate.}
+\item{weekly.seasonality}{Fit weekly seasonality. Can be 'auto', TRUE, FALSE,
+or a number of Fourier terms to generate.}
 
-\item{daily.seasonality}{Fit daily seasonality. Can be 'auto', TRUE,
-FALSE, or a number of Fourier terms to generate.}
+\item{daily.seasonality}{Fit daily seasonality. Can be 'auto', TRUE, FALSE,
+or a number of Fourier terms to generate.}
 
 \item{holidays}{data frame with columns holiday (character) and ds (date
 type)and optionally columns lower_window and upper_window which specify a
@@ -82,10 +83,10 @@ inference with the specified number of MCMC samples. If 0, will do MAP
 estimation.}
 
 \item{interval.width}{Numeric, width of the uncertainty intervals provided
-for the forecast. If mcmc.samples=0, this will be only the uncertainty
-in the trend using the MAP estimate of the extrapolated generative model.
-If mcmc.samples>0, this will be integrated over all model parameters,
-which will include uncertainty in seasonality.}
+for the forecast. If mcmc.samples=0, this will be only the uncertainty in
+the trend using the MAP estimate of the extrapolated generative model. If
+mcmc.samples>0, this will be integrated over all model parameters, which
+will include uncertainty in seasonality.}
 
 \item{uncertainty.samples}{Number of simulated draws used to estimate
 uncertainty intervals. Settings this value to 0 or False will disable
@@ -93,6 +94,9 @@ uncertainty estimation and speed up the calculation.}
 
 \item{fit}{Boolean, if FALSE the model is initialized but not fit.}
 
+\item{backend}{Whether to use the "rstan" or "cmdstanr" backend to fit the
+model. If not provided, uses the R_STAN_BACKEND environment variable.}
+
 \item{...}{Additional arguments, passed to \code{\link{fit.prophet}}}
 }
 \value{

R/tests/testthat/test_diagnostics.R

@@ -121,7 +121,7 @@ test_that("cross_validation_uncertainty_disabled", {
   skip_if_not(Sys.getenv('R_ARCH') != '/i386')
   for (uncertainty in c(0, FALSE)) {
     m <- prophet(uncertainty.samples = uncertainty)
-    m <- fit.prophet(m = m, df = DATA, algorithm = "Newton")
+    m <- fit.prophet(m = m, df = DATA)
     df.cv <- cross_validation(
       m, horizon = 4, units = "days", period = 4, initial = 115)
     expected.cols <- c('y', 'ds', 'yhat', 'cutoff')

R/tests/testthat/test_prophet.R

@@ -125,11 +125,10 @@ test_that("logistic_floor", {
   future1 <- future
   future1$cap <- 80.
   future1$floor <- 10.
-  m <- fit.prophet(m, history, algorithm = 'Newton')
+  m <- fit.prophet(m, history)
   expect_true(m$logistic.floor)
   expect_true('floor' %in% colnames(m$history))
   expect_equal(m$history$y_scaled[1], 1., tolerance = 1e-6)
-  expect_equal(m$fit.kwargs, list(algorithm = 'Newton'))
   fcst1 <- predict(m, future1)
 
   m2 <- prophet(growth = 'logistic')
@@ -139,7 +138,7 @@ test_that("logistic_floor", {
   history2$cap <- history2$cap + 10.
   future1$cap <- future1$cap + 10.
   future1$floor <- future1$floor + 10.
-  m2 <- fit.prophet(m2, history2, algorithm = 'Newton')
+  m2 <- fit.prophet(m2, history2)
   expect_equal(m2$history$y_scaled[1], 1., tolerance = 1e-6)
 })