Package index
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monty_model()
- Create basic model
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monty_model_combine()
- Combine two models
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monty_model_split()
- Split a combined model
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monty_model_function()
- Create
monty_model
from a function computing density
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monty_model_properties()
- Describe model properties
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monty_model_density()
- Compute log density
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monty_model_gradient()
- Compute gradient of log density
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monty_model_direct_sample()
- Directly sample from a model
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monty_example()
- Example models
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monty_dsl()
- Domain Specific Language for monty
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monty_dsl_error_explain()
- Explain monty error
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monty_differentiation()
- Differentiate expressions
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monty_dsl_distributions()
- Information about supported distributions
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monty_dsl_parse_distribution()
- Parse distribution expression
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monty_sample()
- Sample from a model
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monty_sample_continue()
- Continue sampling
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monty_sampler_random_walk()
- Random Walk Sampler
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monty_sampler_adaptive()
- Adaptive Metropolis-Hastings Sampler
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monty_sampler_hmc()
- Create HMC
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monty_sampler_nested_random_walk()
- Nested Random Walk Sampler
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monty_sampler_nested_adaptive()
- Nested Adaptive Metropolis-Hastings Sampler
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monty_sampler_parallel_tempering()
- Parallel Tempering Sampler
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monty_runner_serial()
- Run MCMC chain in series
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monty_runner_parallel()
- Run MCMC chain in parallel
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monty_runner_simultaneous()
- Run MCMC chains simultaneously
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monty_runner_callr()
- Run MCMC chains in parallel with
callr
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monty_sample_manual_prepare()
- Prepare to sample with manual scheduling
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monty_sample_manual_run()
- Run sample with manual scheduling
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monty_sample_manual_info()
- Get information about manually scheduled samples
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monty_sample_manual_collect()
- Collect manually run samples
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monty_sample_manual_cleanup()
- Clean up samples
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monty_sample_manual_prepare_continue()
- Prepare to continue sampling with manual scheduling
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monty_observer()
- Create observer
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monty_packer()
- Build a packer
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monty_packer_grouped()
- Build a nested packer
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monty_domain_expand()
- Expand (and check) domain against a packer
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monty_samples_thin()
- Thin samples
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with_trace_random()
- Trace random number calls
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monty_rng_create()
- Create a monty random number generator
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monty_rng_state()
monty_rng_set_state()
- Get and set random number state
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monty_rng_jump()
monty_rng_long_jump()
- Jump random number state
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monty_random_real()
monty_random_n_real()
- Sample from Uniform(0, 1)
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monty_random_exponential_rate()
monty_random_n_exponential_rate()
monty_random_exponential_mean()
monty_random_n_exponential_mean()
- Sample from exponential distribution
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monty_random_poisson()
monty_random_n_poisson()
- Sample from Poisson distribution
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monty_random_beta()
monty_random_n_beta()
- Sample from beta distribution
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monty_random_binomial()
monty_random_n_binomial()
- Sample from binomial distribution
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monty_random_cauchy()
monty_random_n_cauchy()
- Sample from Cauchy distribution
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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.
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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
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monty_random_normal()
monty_random_n_normal()
- Sample from normal distribution
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monty_random_uniform()
monty_random_n_uniform()
- Sample from uniform distribution
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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
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monty_random_hypergeometric()
monty_random_n_hypergeometric()
- Sample from hypergeometric distribution
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monty_random_truncated_normal()
monty_random_n_truncated_normal()
- Sample from truncated normal
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monty_random_log_normal()
monty_random_n_log_normal()
- Sample from log-normal
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monty_random_weibull()
monty_random_n_weibull()
- Sample from Weibull