| Title: | Integration for the UM-Bridge Protocol | 
| Version: | 1.0 | 
| Maintainer: | Linus Seelinger <mail@linusseelinger.de> | 
| Description: | A convenient wrapper for the UM-Bridge protocol. UM-Bridge is a protocol designed for coupling uncertainty quantification (or statistical / optimization) software to numerical models. A model is represented as a mathematical function with optional support for derivatives via Jacobian actions etc. | 
| License: | MIT + file LICENSE | 
| Encoding: | UTF-8 | 
| RoxygenNote: | 7.2.1 | 
| BugReports: | https://github.com/um-bridge | 
| Imports: | httr2, jsonlite, magrittr | 
| Suggests: | testthat (≥ 3.0.0) | 
| Config/testthat/edition: | 3 | 
| NeedsCompilation: | no | 
| Packaged: | 2022-09-23 07:05:45 UTC; linus | 
| Author: | Linus Seelinger | 
| Repository: | CRAN | 
| Date/Publication: | 2022-09-23 07:30:02 UTC | 
Evaluate Hessian of model.
Description
Evaluate Hessian of model.
Usage
apply_hessian(
  url,
  name,
  out_wrt,
  in_wrt1,
  in_wrt2,
  parameters,
  sens,
  vec,
  config = jsonlite::fromJSON("{}")
)
Arguments
| url | URL the model is running at. | 
| name | Name of the desired model. | 
| out_wrt | Output variable to take Hessian with respect to. | 
| in_wrt1 | First input variable to take Hessian with respect to. | 
| in_wrt2 | Second input variable to take Hessian with respect to. | 
| parameters | Model input parameter (a list of vectors). | 
| sens | Sensitivity with respect to output. | 
| vec | Vector to multiply Hessian by. | 
| config | Model-specific configuration options. | 
Value
Hessian with respect to given inputs and outputs, applied to given sensitivity and vector.
Evaluate Jacobian of model.
Description
Evaluate Jacobian of model.
Usage
apply_jacobian(
  url,
  name,
  out_wrt,
  in_wrt,
  parameters,
  vec,
  config = jsonlite::fromJSON("{}")
)
Arguments
| url | URL the model is running at. | 
| name | Name of the desired model. | 
| out_wrt | Output variable to take Jacobian with respect to. | 
| in_wrt | Input variable to take Jacobian with respect to. | 
| parameters | Model input parameter (a list of vectors). | 
| vec | Vector to multiply Jacobian by. | 
| config | Model-specific configuration options. | 
Value
Jacobian with respect to given input and output variables, applied to given vector.
Evaluate model.
Description
Evaluate model.
Usage
evaluate(url, name, parameters, config = jsonlite::fromJSON("{}"))
Arguments
| url | URL the model is running at. | 
| name | Name of the desired model. | 
| parameters | Model input parameter (a list of vectors). | 
| config | Model-specific configuration options. | 
Value
The model output (a list of vectors).
Get models supported by server.
Description
Get models supported by server.
Usage
get_models(url)
Arguments
| url | URL the model is running at. | 
Value
List of models supported by server.
Evaluate gradient of target functional depending on model.
Description
Evaluate gradient of target functional depending on model.
Usage
gradient(
  url,
  name,
  out_wrt,
  in_wrt,
  parameters,
  sens,
  config = jsonlite::fromJSON("{}")
)
Arguments
| url | URL the model is running at. | 
| name | Name of the desired model. | 
| out_wrt | Output variable to take gradient with respect to. | 
| in_wrt | Input variable to take gradient with respect to. | 
| parameters | Model input parameter (a list of vectors). | 
| sens | Sensitivity of target functional with respect to model output. | 
| config | Model-specific configuration options. | 
Value
Gradient of target functional.
Retrieve model's input dimensions.
Description
Retrieve model's input dimensions.
Usage
model_input_sizes(url, name, config = jsonlite::fromJSON("{}"))
Arguments
| url | URL the model is running at. | 
| name | Name of the desired model. | 
| config | Model-specific configuration options. | 
Value
List of input dimensions.
Retrieve model's output dimensions.
Description
Retrieve model's output dimensions.
Usage
model_output_sizes(url, name, config = jsonlite::fromJSON("{}"))
Arguments
| url | URL the model is running at. | 
| name | Name of the desired model | 
| config | Model-specific configuration options. | 
Value
List of output dimensions.
Check if model's protocol version is supported by this client.
Description
Check if model's protocol version is supported by this client.
Usage
protocol_version_supported(url)
Arguments
| url | URL the model is running at. | 
Value
TRUE if model's protocol version is supported by this client, FALSE otherwise.
Check if model supports Hessian action.
Description
Check if model supports Hessian action.
Usage
supports_apply_hessian(url, name)
Arguments
| url | URL the model is running at. | 
| name | Name of the desired model. | 
Value
TRUE if model supports Hessian action, FALSE otherwise.
Check if model supports Jacobian action.
Description
Check if model supports Jacobian action.
Usage
supports_apply_jacobian(url, name)
Arguments
| url | URL the model is running at. | 
| name | Name of the desired model. | 
Value
TRUE if model supports Jacobian action, FALSE otherwise.
Check if model supports evaluation.
Description
Check if model supports evaluation.
Usage
supports_evaluate(url, name)
Arguments
| url | URL the model is running at. | 
| name | Name of the desired model. | 
Value
TRUE if model supports evaluation, FALSE otherwise.
Check if model supports gradient evaluation.
Description
Check if model supports gradient evaluation.
Usage
supports_gradient(url, name)
Arguments
| url | URL the model is running at. | 
| name | Name of the desired model. | 
Value
TRUE if model supports gradient evaluation, FALSE otherwise.