6. CLI reference#

This page has reference information about Maud’s command line interface.

6.1. sample#

Generate MCMC samples given a user input directory.

This function creates a new directory in output_dir with a name starting with “maud_output”. It first copies the directory at data_path into the new this directory at new_dir/user_input, then runs the running_stan.sample function to write samples in new_dir/samples. Finally it prints the results of cmdstanpy’s diagnose and summary methods.

Run the sample function as a click command.

sample [OPTIONS] DATA_PATH

Options

--output_dir <output_dir>#

Where to save Maud’s output

Arguments

DATA_PATH#

Required argument

6.2. simulate#

Generate draws from the initial values.

simulate [OPTIONS] DATA_PATH

Options

--output_dir <output_dir>#

Where to save the output

-n <n>#

Number of simulations

Arguments

DATA_PATH#

Required argument

6.3. variational#

Generate MCMC samples given a user input directory.

This function creates a new directory in output_dir with a name starting with “maud_output”. It first copies the directory at data_path into the new this directory at new_dir/user_input, then runs the running_stan.sample function to write samples in new_dir/samples. Finally it prints the results of cmdstanpy’s diagnose and summary methods.

variational [OPTIONS] DATA_PATH

Options

--output_dir <output_dir>#

Where to save Maud’s output

Arguments

DATA_PATH#

Required argument

6.4. optimize#

Optimize the model parameters.

optimize [OPTIONS] DATA_PATH

Options

--output_dir <output_dir>#

Where to save the output

Arguments

DATA_PATH#

Required argument

6.5. predict#

Generate MCMC samples given a Maud output folder at train_path.

This function creates a new directory in output_dir with a name starting with “maud-predict-output”. It first copies the testing directory at train_path into the new this directory at new_dir/user_input, then runs the running_stan.predict_out_of_sample function to write samples in new_dir/oos_samples.

The trained output is stored in the new_dir/trained_samples folder along with the user input required to generate the trained samples.

predict [OPTIONS] DATA_PATH

Arguments

DATA_PATH#

Required argument