# Julia Interface#

## Installation#

### From JuliaRegistries#

BridgeStan is registered on JuliaRegistries each release.

```
] add BridgeStan
```

The first time you compile a model, the BridgeStan source code for your current version will be downloaded to a hidden directory in the users `HOME`

directory. 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 section 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 Julia interface, you can either install it directly from Github by running the following inside a Julia REPL

```
] add https://github.com/roualdes/bridgestan.git:julia
```

Or, since you have already downloaded the repository, you can run

```
] dev julia/
```

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 Julia package depends on Julia 1.6+ and the `Inflate`

package.

## Example Program#

An example program is provided alongside the Julia interface code in `example.jl`

:

## Show example.jl

```
using BridgeStan
const BS = BridgeStan
# These paths are what they are because this example lives in a subfolder
# of the BridgeStan repository. If you're running this on your own, you
# will most likely want to delete the next line (to have BridgeStan
# download its sources for you) and change the paths on the following two
BS.set_bridgestan_path!("../")
bernoulli_stan = joinpath(@__DIR__, "../test_models/bernoulli/bernoulli.stan")
bernoulli_data = joinpath(@__DIR__, "../test_models/bernoulli/bernoulli.data.json")
smb = BS.StanModel(bernoulli_stan, bernoulli_data);
println("This model's name is $(BS.name(smb)).")
println("It has $(BS.param_num(smb)) parameters.")
x = rand(BS.param_unc_num(smb));
q = @. log(x / (1 - x)); # unconstrained scale
lp, grad = BS.log_density_gradient(smb, q, jacobian = false)
println("log_density and gradient of Bernoulli model: $((lp, grad))")
```

## API Reference#

### StanModel interface#

#
** BridgeStan.StanModel** —

*Type*.

```
StanModel(lib, data="", seed=204; stanc_args=[], make_args=[], warn=true)
```

A StanModel instance encapsulates a Stan model instantiated with data.

Construct a Stan model from the supplied library file path and data. If lib is a path to a file ending in `.stan`

, this will first compile the model. Compilation occurs if no shared object file exists for the supplied Stan file or if a shared object file exists and the Stan file has changed since last compilation. This is equivalent to calling `compile_model`

and then the constructor of `StanModel`

. If `warn`

is false, the warning about re-loading the same shared objects is suppressed.

Data should either be a string containing a JSON string literal, a path to a data file ending in `.json`

, or the empty string.

If seed is supplied, it is used to initialize the RNG used by the model’s constructor.

#
** BridgeStan.log_density** —

*Function*.

```
log_density(sm, q; propto=true, jacobian=true)
```

Return the log density of the specified unconstrained parameters.

This calculation drops constant terms that do not depend on the parameters if `propto`

is `true`

and includes change of variables terms for constrained parameters if `jacobian`

is `true`

.

#
** BridgeStan.log_density_gradient** —

*Function*.

```
log_density_gradient(sm, q; propto=true, jacobian=true)
```

Returns a tuple of the log density and gradient of the specified unconstrained parameters.

This calculation drops constant terms that do not depend on the parameters if `propto`

is `true`

and includes change of variables terms for constrained parameters if `jacobian`

is `true`

.

This allocates new memory for the gradient output each call. See `log_density_gradient!`

for a version which allows re-using existing memory.

#
** BridgeStan.log_density_hessian** —

*Function*.

```
log_density_hessian(sm, q; propto=true, jacobian=true)
```

Returns a tuple of the log density, gradient, and Hessian of the specified unconstrained parameters.

This calculation drops constant terms that do not depend on the parameters if `propto`

is `true`

and includes change of variables terms for constrained parameters if `jacobian`

is `true`

.

This allocates new memory for the gradient and Hessian output each call. See `log_density_hessian!`

for a version which allows re-using existing memory.

#
** BridgeStan.log_density_hessian_vector_product** —

*Function*.

```
log_density_hessian_vector_product(sm, q, v; propto=true, jacobian=true)
```

Returns log density and the product of the Hessian of the log density with the vector `v`

at the specified unconstrained parameters.

`propto`

is `true`

and includes change of variables terms for constrained parameters if `jacobian`

is `true`

.

This allocates new memory for the output each call. See `log_density_hessian_vector_product!`

for a version which allows re-using existing memory.

#
** BridgeStan.param_constrain** —

*Function*.

```
param_constrain(sm, theta_unc, out; include_tp=false, include_gq=false, rng=nothing)
```

Returns a vector constrained parameters given unconstrained parameters. Additionally (if `include_tp`

and `include_gq`

are set, respectively) returns transformed parameters and generated quantities.

If `include_gq`

is `true`

, then `rng`

must be provided. See `StanRNG`

for details on how to construct RNGs.

This allocates new memory for the output each call. See `param_constrain!`

for a version which allows re-using existing memory.

This is the inverse of `param_unconstrain`

.

#
** BridgeStan.param_unconstrain** —

*Function*.

```
param_unconstrain(sm, theta)
```

Returns a vector of unconstrained params 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 `param_unc_names()`

). If structured input is needed, use `param_unconstrain_json`

This allocates new memory for the output each call. See `param_unconstrain!`

for a version which allows re-using existing memory.

This is the inverse of `param_constrain`

.

#
** BridgeStan.param_unconstrain_json** —

*Function*.

```
param_unconstrain_json(sm, theta)
```

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.

This allocates new memory for the output each call. See `param_unconstrain_json!`

for a version which allows re-using existing memory.

#
** BridgeStan.name** —

*Function*.

```
name(sm)
```

Return the name of the model `sm`

#
** BridgeStan.model_info** —

*Function*.

```
model_info(sm)
```

Return information about the model `sm`

.

This includes the Stan version and important compiler flags.

#
** BridgeStan.param_num** —

*Function*.

```
param_num(sm; include_tp=false, include_gq=false)
```

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

This is the total of all the sizes of items declared in the `parameters`

block of the model. If `include_tp`

or `include_gq`

are true, items declared in the `transformed parameters`

and `generate quantities`

blocks are included, respectively.

#
** BridgeStan.param_unc_num** —

*Function*.

```
param_unc_num(sm)
```

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.

#
** BridgeStan.param_names** —

*Function*.

```
param_names(sm; include_tp=false, include_gq=false)
```

Return the indexed names of the (constrained) parameters, including transformed parameters and/or generated quantities as indicated.

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 names `"a.2.3"`

. Parameter order of the output is column major and more generally last-index major for containers.

#
** BridgeStan.param_unc_names** —

*Function*.

```
param_unc_names(sm)
```

Return the indexed names of the unconstrained parameters.

For example, a scalar unconstrained parameter `b`

has indexed name `b`

and a vector entry `b[3]`

has indexed name `b.3`

.

#
** BridgeStan.log_density_gradient!** —

*Function*.

```
log_density_gradient!(sm, q, out; propto=true, jacobian=true)
```

Returns a tuple of the log density and gradient of the specified unconstrained parameters.

`propto`

is `true`

and includes change of variables terms for constrained parameters if `jacobian`

is `true`

.

The gradient is stored in the vector `out`

, and a reference is returned. See `log_density_gradient`

for a version which allocates fresh memory.

#
** BridgeStan.log_density_hessian!** —

*Function*.

```
log_density_hessian!(sm, q, out_grad, out_hess; propto=true, jacobian=true)
```

Returns a tuple of the log density, gradient, and Hessian of the specified unconstrained parameters.

`propto`

is `true`

and includes change of variables terms for constrained parameters if `jacobian`

is `true`

.

The gradient is stored in the vector `out_grad`

and the Hessian is stored in `out_hess`

and references are returned. See `log_density_hessian`

for a version which allocates fresh memory.

#
** BridgeStan.log_density_hessian_vector_product!** —

*Function*.

```
log_density_hessian_vector_product!(sm, q, v, out; propto=true, jacobian=true)
```

Returns log density and the product of the Hessian of the log density with the vector `v`

at the specified unconstrained parameters.

`propto`

is `true`

and includes change of variables terms for constrained parameters if `jacobian`

is `true`

.

The product is stored in the vector `out`

and a reference is returned. See `log_density_hessian_vector_product`

for a version which allocates fresh memory.

#
** BridgeStan.param_constrain!** —

*Function*.

```
param_constrain!(sm, theta_unc, out; include_tp=false, include_gq=false, rng=nothing)
```

Returns a vector constrained parameters given unconstrained parameters. Additionally (if `include_tp`

and `include_gq`

are set, respectively) returns transformed parameters and generated quantities.

If `include_gq`

is `true`

, then `rng`

must be provided. See `StanRNG`

for details on how to construct RNGs.

The result is stored in the vector `out`

, and a reference is returned. See `param_constrain`

for a version which allocates fresh memory.

This is the inverse of `param_unconstrain!`

.

#
** BridgeStan.param_unconstrain!** —

*Function*.

```
param_unconstrain!(sm, theta, out)
```

Returns a vector of unconstrained params 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 `param_names()`

). If structured input is needed, use `param_unconstrain_json!`

The result is stored in the vector `out`

, and a reference is returned. See `param_unconstrain`

for a version which allocates fresh memory.

This is the inverse of `param_constrain!`

.

#
** BridgeStan.param_unconstrain_json!** —

*Function*.

```
param_unconstrain_json!(sm, theta, out)
```

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.

The result is stored in the vector `out`

, and a reference is returned. See `param_unconstrain_json`

for a version which allocates fresh memory.

#
** BridgeStan.StanRNG** —

*Type*.

```
StanRNG(sm::StanModel, seed)
```

Construct a StanRNG instance from a `StanModel`

instance and a seed.

This can be used in the `param_constrain`

and `param_constrain!`

methods when using the generated quantities block.

This object is not thread-safe, one should be created per thread.

#
** BridgeStan.new_rng** —

*Function*.

```
new_rng(sm::StanModel, seed)
```

Construct a StanRNG instance from a `StanModel`

instance and a seed. This function is a wrapper around the constructor `StanRNG`

.

This can be used in the `param_constrain`

and `param_constrain!`

methods when using the generated quantities block.

The StanRNG object created is not thread-safe, one should be created per thread.

### Compilation utilities#

#
** BridgeStan.compile_model** —

*Function*.

```
compile_model(stan_file; stanc_args=[], make_args=[])
```

Run BridgeStan’s Makefile on a `.stan`

file, creating the `.so`

used by StanModel and return a path to the compiled library. Arguments to `stanc3`

can be passed as a vector, for example `["--O1"]`

enables level 1 compiler optimizations. Additional arguments to `make`

can be passed as a vector, for example `["STAN_THREADS=true"]`

enables the model’s threading capabilities. If the same flags are defined in `make/local`

, the versions passed here will take precedent.

This function checks that the path to BridgeStan is valid and will error if it is not. This can be set with `set_bridgestan_path!()`

.

#
** BridgeStan.get_bridgestan_path** —

*Function*.

```
get_bridgestan_path(;download=true) -> String
```

Return the path the the BridgeStan directory.

If the environment variable `$BRIDGESTAN`

is set, this will be returned.

If `$BRIDGESTAN`

is not set and `download`

is true, this function downloads a copy of the BridgeStan source code for the currently installed version under a folder called `.bridgestan`

in the user’s home directory if one is not already present.

See `set_bridgestan_path!()`

to set the path from within Julia.

#
** BridgeStan.set_bridgestan_path!** —

*Function*.

```
set_bridgestan_path!(path)
```

Set the path BridgeStan.