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(): Example models

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_nested_random_walk(): Nested Random Walk Sampler

monty_sampler_nested_adaptive(): Nested Adaptive Metropolis-Hastings 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

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