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R Interface#

Installation#

Note

Mac users have reported issues when using a copy of R installed from conda-forge. If you encounter an issue, you may need to use R from the official R project website or a system package manager like brew.

From inside R#

While BridgeStan is not available on CRAN, you can install the R package from the source code using the remotes package:

remotes::install_github("https://github.com/roualdes/bridgestan", subdir="R")

To install a specific version of BridgeStan you can use the argument ref, for example, ref="v2.4.1".

The first time you compile a model, the BridgeStan source code for your current version will be downloaded and placed in :file:~/.bridgestan/. If you prefer to use a source distribution of BridgeStan, consult the following section.

Note that the system pre-requisites from the Getting Started guide are still required and will not be automatically installed by this method.

From Source#

This assumes you have followed the Getting Started guide to install BridgeStan’s pre-requisites and downloaded a copy of the BridgeStan source code.

To install the R package from the source code, run:

install.packages(file.path(getwd(),"R"), repos=NULL, type="source")

from the BridgeStan folder.

To use the BridgeStan source you’ve manually downloaded instead of one the package will download for you, you must use set_bridgestan_path() or the $BRIDGESTAN environment variable.

Note that the R package depends on R 3+ and R6, and will install R6 if it is not already installed.

Example Program#

An example program is provided alongside the R interface code in example.R:

Show example.R 
# Before running this, make sure you are in the directory bridgestan/R

library(bridgestan)

model <- StanModel$new("../test_models/bernoulli/bernoulli.stan", "../test_models/bernoulli/bernoulli.data.json", 1234)

print(paste0("This model's name is ", model$name(), "."))
print(paste0("This model has ", model$param_num(), " parameters."))


x <- runif(model$param_unc_num())
q <- log(x / (1 - x))

res <- model$log_density_gradient(q, jacobian = FALSE)

print(paste0("log_density and gradient of Bernoulli model: ",
             res$val, ", ", res$gradient))

API Reference#

StanModel#

R6 Class representing a compiled BridgeStan model.

This model exposes log density, gradient, and Hessian information as well as constraining and unconstraining transforms.

Methods#

Method new()#

Create a Stan Model instance.

Usage#
StanModel$new(
  lib,
  data,
  seed,
  stanc_args = NULL,
  make_args = NULL,
  warn = TRUE
)
Arguments#

  • lib: A path to a compiled BridgeStan Shared Object file or a .stan file (will be compiled).

  • data: Either a JSON string literal, a path to a data file in JSON format ending in “.json”, or the empty string.

  • seed: Seed for the RNG used in constructing the model.

  • stanc_args: A list of arguments to pass to stanc3 if the model is not already compiled.

  • make_args: A list of additional arguments to pass to Make if the model is not already compiled.

  • warn: If false, the warning about re-loading the same shared object is suppressed.

Returns#

A new StanModel.

Method name()#

Get the name of this StanModel.

Usage#
StanModel$name()
Returns#

A character vector of the name.

Method model_info()#

Get compile information about this Stan model.

Usage#
StanModel$model_info()
Returns#

A character vector of the Stan version and important flags.

Method model_version()#

Get the version of BridgeStan used in the compiled model.

Usage#
StanModel$model_version()
Method param_names()#

Return the indexed names of the (constrained) parameters. For containers, indexes are separated by periods (.).

For example, the scalar a has indexed name “a”, the vector entry a[1] has indexed name “a.1” and the matrix entry a[2, 3] has indexed name “a.2.3”. Parameter order of the output is column major and more generally last-index major for containers.

Usage#
StanModel$param_names(include_tp = FALSE, include_gq = FALSE)
Arguments#

  • include_tp: Whether to include variables from transformed parameters.

  • include_gq: Whether to include variables from generated quantities.

Returns#

A list of character vectors of the names.

Method param_unc_names()#

Return the indexed names of the unconstrained parameters. For containers, indexes are separated by periods (.).

For example, the scalar a has indexed name “a”, the vector entry a[1] has indexed name “a.1” and the matrix entry a[2, 3] has indexed name “a.2.3”. Parameter order of the output is column major and more generally last-index major for containers.

Usage#
StanModel$param_unc_names()
Returns#

A list of character vectors of the names.

Method param_num()#

Return the number of (constrained) parameters in the model.

Usage#
StanModel$param_num(include_tp = FALSE, include_gq = FALSE)
Arguments#

  • include_tp: Whether to include variables from transformed parameters.

  • include_gq: Whether to include variables from generated quantities.

Returns#

The number of parameters in the model.

Method param_unc_num()#

Return the number of unconstrained parameters in the model.

This function is mainly different from param_num when variables are declared with constraints. For example, simplex[5] has a constrained size of 5, but an unconstrained size of 4.

Usage#
StanModel$param_unc_num()
Returns#

The number of parameters in the model.

Method param_constrain()#

Returns a vector of constrained parameters given the unconstrained parameters. See also StanModel$param_unconstrain(), the inverse of this function.

Usage#
StanModel$param_constrain(
  theta_unc,
  include_tp = FALSE,
  include_gq = FALSE,
  rng
)
Arguments#

  • theta_unc: The vector of unconstrained parameters.

  • include_tp: Whether to also output the transformed parameters of the model.

  • include_gq: Whether to also output the generated quantities of the model.

  • rng: The random number generator to use if include_gq is TRUE. See StanModel$new_rng().

Returns#

The constrained parameters of the model.

Method new_rng()#

Create a new persistent PRNG object for use in param_constrain().

Usage#
StanModel$new_rng(seed)
Arguments#

  • seed: The seed for the PRNG.

Returns#

A StanRNG object.

Method param_unconstrain()#

Returns a vector of unconstrained parameters give the constrained parameters.

It is assumed that these will be in the same order as internally represented by the model (e.g., in the same order as StanModel$param_names()). If structured input is needed, use StanModel$param_unconstrain_json(). See also StanModel$param_constrain(), the inverse of this function.

Usage#
StanModel$param_unconstrain(theta)
Arguments#

  • theta: The vector of constrained parameters.

Returns#

The unconstrained parameters of the model.

Method param_unconstrain_json()#

This accepts a JSON string of constrained parameters and returns the unconstrained parameters.

The JSON is expected to be in the JSON Format for CmdStan.

Usage#
StanModel$param_unconstrain_json(json)
Arguments#

  • json: Character vector containing a string representation of JSON data.

Returns#

The unconstrained parameters of the model.

Method log_density()#

Return the log density of the specified unconstrained parameters.

Usage#
StanModel$log_density(theta_unc, propto = TRUE, jacobian = TRUE)
Arguments#

  • theta_unc: The vector of unconstrained parameters.

  • propto: If TRUE, drop terms which do not depend on the parameters.

  • jacobian: If TRUE, include change of variables terms for constrained parameters.

Returns#

The log density.

Method log_density_gradient()#

Return the log density and gradient of the specified unconstrained parameters.

Usage#
StanModel$log_density_gradient(theta_unc, propto = TRUE, jacobian = TRUE)
Arguments#

  • theta_unc: The vector of unconstrained parameters.

  • propto: If TRUE, drop terms which do not depend on the parameters.

  • jacobian: If TRUE, include change of variables terms for constrained parameters.

Returns#

List containing entries val (the log density) and gradient (the gradient).

Method log_density_hessian()#

Return the log density, gradient, and Hessian of the specified unconstrained parameters.

Usage#
StanModel$log_density_hessian(theta_unc, propto = TRUE, jacobian = TRUE)
Arguments#

  • theta_unc: The vector of unconstrained parameters.

  • propto: If TRUE, drop terms which do not depend on the parameters.

  • jacobian: If TRUE, include change of variables terms for constrained parameters.

Returns#

List containing entries val (the log density), gradient (the gradient), and hessian (the Hessian).

Method log_density_hessian_vector_product()#

Return the log density and the product of the Hessian with the specified vector.

Usage#
StanModel$log_density_hessian_vector_product(
  theta_unc,
  v,
  propto = TRUE,
  jacobian = TRUE
)
Arguments#

  • theta_unc: The vector of unconstrained parameters.

  • v: The vector to multiply the Hessian by.

  • propto: If TRUE, drop terms which do not depend on the parameters.

  • jacobian: If TRUE, include change of variables terms for constrained parameters.

Returns#

List containing entries val (the log density) and Hvp (the hessian-vector product).

Compilation utilities#

Function compile_model()#

compile_model(stan_file, stanc_args = NULL, make_args = NULL)

Arguments#

  • stan_file: A path to a Stan model file.

  • make_args: A vector of additional arguments to pass to Make. For example, c('STAN_THREADS=True') will enable threading for the compiled model. If the same flags are defined in make/local, the versions passed here will take precedent.

  • stanc_arg: A vector of arguments to pass to stanc3. For example, c('--O1') will enable compiler optimization level 1.

Returns#

Path to the compiled model.

Compiles a Stan model.

Details#

Run BridgeStan’s Makefile on a .stan file, creating the .so used by the StanModel class. This function checks that the path to BridgeStan is valid and will error if not. This can be set with set_bridgestan_path.

See Also#

set_bridgestan_path()

Function set_bridgestan_path()#

set_bridgestan_path(path)

Set the path to BridgeStan.

Details#

This should point to the top-level folder of the repository.

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