Package index • monty

Models

monty_model(): Create basic model

monty_model_combine(): Combine two models

monty_model_split(): Split a combined model

monty_model_function(): Create monty_model from a function computing density

monty_model_properties(): Describe model properties

Functions for working with models

monty_model_density(): Compute log density

monty_model_gradient(): Compute gradient of log density

monty_model_direct_sample(): Directly sample from a model

Example

monty_example(): Load example models from monty. These models exist so that we can create (hopefully) interesting examples in the documentation without them becoming overwhelming. You should probably not use these for anything other than exploring the package.

Domain specific language

monty_dsl(): Domain Specific Language for monty

monty_dsl_error_explain(): Explain monty error

monty_differentiation(): Differentiate expressions

Advanced

monty_dsl_distributions(): Information about supported distributions

monty_dsl_parse_distribution(): Parse distribution expression

Draw samples from a model

monty_sample(): Sample from a model

monty_sample_continue(): Continue sampling

Samplers

monty_sampler_random_walk(): Random Walk Sampler

monty_sampler_adaptive(): Adaptive Metropolis-Hastings Sampler

monty_sampler_hmc(): Create HMC

monty_sampler_parallel_tempering(): Parallel Tempering Sampler

Chain runners

monty_runner_serial(): Run MCMC chain in series

monty_runner_parallel(): Run MCMC chain in parallel

monty_runner_simultaneous(): Run MCMC chains simultaneously

monty_runner_callr(): Run MCMC chains in parallel with callr

Manually run chains

monty_sample_manual_prepare(): Prepare to sample with manual scheduling

monty_sample_manual_run(): Run sample with manual scheduling

monty_sample_manual_info(): Get information about manually scheduled samples

monty_sample_manual_collect(): Collect manually run samples

monty_sample_manual_cleanup(): Clean up samples

monty_sample_manual_prepare_continue(): Prepare to continue sampling with manual scheduling

Tools

monty_observer(): Create observer

monty_packer(): Build a packer

monty_packer_grouped(): Build a nested packer

monty_domain_expand(): Expand (and check) domain against a packer

monty_samples_thin(): Thin samples

with_trace_random(): Trace random number calls

Advanced

monty_sampler(): Create a monty sampler

monty_sampler_properties(): Describe sampler properties

Random numbers

monty_rng_create(): Create a monty random number generator

monty_rng_state() monty_rng_set_state(): Get and set random number state

monty_rng_jump() monty_rng_long_jump(): Jump random number state

monty_random_real() monty_random_n_real(): Sample from Uniform(0, 1)

monty_random_exponential_rate() monty_random_n_exponential_rate() monty_random_exponential_mean() monty_random_n_exponential_mean(): Sample from exponential distribution

monty_random_poisson() monty_random_n_poisson(): Sample from Poisson distribution

monty_random_beta() monty_random_n_beta(): Sample from beta distribution

monty_random_binomial() monty_random_n_binomial(): Sample from binomial distribution

monty_random_cauchy() monty_random_n_cauchy(): Sample from Cauchy distribution

monty_random_gamma_scale() monty_random_n_gamma_scale() monty_random_gamma_rate() monty_random_n_gamma_rate(): Sample from a gamma distribution. There are two parameterisations here, one in terms of rate, and one in terms of scale.

monty_random_negative_binomial_prob() monty_random_n_negative_binomial_prob() monty_random_negative_binomial_mu() monty_random_n_negative_binomial_mu(): Sample from negative binomial distribution

monty_random_normal() monty_random_n_normal(): Sample from normal distribution

monty_random_uniform() monty_random_n_uniform(): Sample from uniform distribution

monty_random_beta_binomial_prob() monty_random_n_beta_binomial_prob() monty_random_beta_binomial_ab() monty_random_n_beta_binomial_ab(): Sample from beta-binomial distribution

monty_random_hypergeometric() monty_random_n_hypergeometric(): Sample from hypergeometric distribution

monty_random_truncated_normal() monty_random_n_truncated_normal(): Sample from truncated normal

monty_random_log_normal() monty_random_n_log_normal(): Sample from log-normal

monty_random_weibull() monty_random_n_weibull(): Sample from Weibull

monty_random_zi_poisson() monty_random_n_zi_poisson(): Sample from zero-inflated Poisson distribution

monty_random_zi_negative_binomial_prob() monty_random_n_zi_negative_binomial_prob() monty_random_zi_negative_binomial_mu() monty_random_n_zi_negative_binomial_mu(): Sample from zero-inflated negative binomial distribution