Package: monty 0.3.22
Rich FitzJohn
monty: Monte Carlo Models
Experimental sources for the next generation of mcstate, now called 'monty', which will support much of the old mcstate functionality but new things like better parameter interfaces, Hamiltonian Monte Carlo, and other features.
Authors:
monty_0.3.22.tar.gz
monty_0.3.22.zip(r-4.5)monty_0.3.22.zip(r-4.4)monty_0.3.22.zip(r-4.3)
monty_0.3.22.tgz(r-4.4-x86_64)monty_0.3.22.tgz(r-4.4-arm64)monty_0.3.22.tgz(r-4.3-x86_64)monty_0.3.22.tgz(r-4.3-arm64)
monty_0.3.22.tar.gz(r-4.5-noble)monty_0.3.22.tar.gz(r-4.4-noble)
monty_0.3.22.tgz(r-4.4-emscripten)monty_0.3.22.tgz(r-4.3-emscripten)
monty.pdf |monty.html✨
monty/json (API)
| # Install 'monty' in R: |
| install.packages('monty', repos = c('https://mrc-ide.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/mrc-ide/monty/issues
Pkgdown:https://mrc-ide.github.io
Last updated 5 days agofrom:bbe410ff20 (on main). Checks:OK: 9. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Dec 16 2024 |
| R-4.5-win-x86_64 | OK | Dec 16 2024 |
| R-4.5-linux-x86_64 | OK | Dec 16 2024 |
| R-4.4-win-x86_64 | OK | Dec 16 2024 |
| R-4.4-mac-x86_64 | OK | Dec 16 2024 |
| R-4.4-mac-aarch64 | OK | Dec 16 2024 |
| R-4.3-win-x86_64 | OK | Dec 16 2024 |
| R-4.3-mac-x86_64 | OK | Dec 16 2024 |
| R-4.3-mac-aarch64 | OK | Dec 16 2024 |
Exports:monty_differentiationmonty_domain_expandmonty_dslmonty_dsl_distributionsmonty_dsl_error_explainmonty_dsl_parse_distributionmonty_examplemonty_modelmonty_model_combinemonty_model_densitymonty_model_direct_samplemonty_model_functionmonty_model_gradientmonty_model_propertiesmonty_model_splitmonty_observermonty_packermonty_packer_groupedmonty_random_betamonty_random_beta_binomial_abmonty_random_beta_binomial_probmonty_random_binomialmonty_random_cauchymonty_random_exponential_meanmonty_random_exponential_ratemonty_random_gamma_ratemonty_random_gamma_scalemonty_random_hypergeometricmonty_random_log_normalmonty_random_n_betamonty_random_n_beta_binomial_abmonty_random_n_beta_binomial_probmonty_random_n_binomialmonty_random_n_cauchymonty_random_n_exponential_meanmonty_random_n_exponential_ratemonty_random_n_gamma_ratemonty_random_n_gamma_scalemonty_random_n_hypergeometricmonty_random_n_log_normalmonty_random_n_negative_binomial_mumonty_random_n_negative_binomial_probmonty_random_n_normalmonty_random_n_poissonmonty_random_n_realmonty_random_n_truncated_normalmonty_random_n_uniformmonty_random_n_weibullmonty_random_negative_binomial_mumonty_random_negative_binomial_probmonty_random_normalmonty_random_poissonmonty_random_realmonty_random_truncated_normalmonty_random_uniformmonty_random_weibullmonty_rng_createmonty_rng_jumpmonty_rng_long_jumpmonty_rng_set_statemonty_rng_statemonty_runner_callrmonty_runner_parallelmonty_runner_serialmonty_runner_simultaneousmonty_samplemonty_sample_continuemonty_sample_manual_cleanupmonty_sample_manual_collectmonty_sample_manual_infomonty_sample_manual_preparemonty_sample_manual_prepare_continuemonty_sample_manual_runmonty_sampler_adaptivemonty_sampler_hmcmonty_sampler_nested_adaptivemonty_sampler_nested_random_walkmonty_sampler_random_walkmonty_samples_thinwith_trace_random
DSL parse errors
Rendered fromdsl-errors.Rmdusingknitr::rmarkdownon Dec 16 2024.Last update: 2024-10-10
Started: 2024-08-06
Introduction to monty
Rendered frommonty.Rmdusingknitr::rmarkdownon Dec 16 2024.Last update: 2024-10-02
Started: 2024-08-21
Migration from mcstate
Rendered frommigration.Rmdusingknitr::rmarkdownon Dec 16 2024.Last update: 2024-12-16
Started: 2024-12-16
Probabilistic DSL
Rendered fromdsl.Rmdusingknitr::rmarkdownon Dec 16 2024.Last update: 2024-12-04
Started: 2024-08-05
Samplers
Rendered fromsamplers.Rmdusingknitr::rmarkdownon Dec 16 2024.Last update: 2024-12-04
Started: 2024-03-08
Working with samples
Rendered fromsamples.Rmdusingknitr::rmarkdownon Dec 16 2024.Last update: 2024-12-04
Started: 2024-08-16
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Differentiate expressions | monty_differentiation |
| Expand (and check) domain against a packer | monty_domain_expand |
| Domain Specific Language for monty | monty_dsl |
| Information about supported distributions | monty_dsl_distributions |
| Explain monty error | monty_dsl_error_explain |
| Parse distribution expression | monty_dsl_parse_distribution |
| Example models | monty_example |
| Create basic model | monty_model |
| Combine two models | monty_model_combine |
| Compute log density | monty_model_density |
| Directly sample from a model | monty_model_direct_sample |
| Create 'monty_model' from a function computing density | monty_model_function |
| Compute gradient of log density | monty_model_gradient |
| Describe model properties | monty_model_properties |
| Split a combined model | monty_model_split |
| Create observer | monty_observer |
| Build a packer | monty_packer |
| Build a nested packer | monty_packer_grouped |
| Sample from beta distribution | monty_random_beta monty_random_n_beta |
| Sample from beta-binomial distribution | monty_random_beta_binomial_ab monty_random_beta_binomial_prob monty_random_n_beta_binomial_ab monty_random_n_beta_binomial_prob |
| Sample from binomial distribution | monty_random_binomial monty_random_n_binomial |
| Sample from Cauchy distribution | monty_random_cauchy monty_random_n_cauchy |
| Sample from exponential distribution | monty_random_exponential_mean monty_random_exponential_rate monty_random_n_exponential_mean monty_random_n_exponential_rate |
| Sample from a gamma distribution. There are two parameterisations here, one in terms of rate, and one in terms of scale. | monty_random_gamma_rate monty_random_gamma_scale monty_random_n_gamma_rate monty_random_n_gamma_scale |
| Sample from hypergeometric distribution | monty_random_hypergeometric monty_random_n_hypergeometric |
| Sample from log-normal | monty_random_log_normal monty_random_n_log_normal |
| Sample from negative binomial distribution | monty_random_negative_binomial_mu monty_random_negative_binomial_prob monty_random_n_negative_binomial_mu monty_random_n_negative_binomial_prob |
| Sample from normal distribution | monty_random_normal monty_random_n_normal |
| Sample from Poisson distribution | monty_random_n_poisson monty_random_poisson |
| Sample from Uniform(0, 1) | monty_random_n_real monty_random_real |
| Sample from truncated normal | monty_random_n_truncated_normal monty_random_truncated_normal |
| Sample from uniform distribution | monty_random_n_uniform monty_random_uniform |
| Sample from Weibull | monty_random_n_weibull monty_random_weibull |
| Create a monty random number generator | monty_rng_create |
| Jump random number state | monty_rng_jump monty_rng_long_jump |
| Get and set random number state | monty_rng_set_state monty_rng_state |
| Run MCMC chains in parallel with 'callr' | monty_runner_callr |
| Run MCMC chain in parallel | monty_runner_parallel |
| Run MCMC chain in series | monty_runner_serial |
| Run MCMC chains simultaneously | monty_runner_simultaneous |
| Sample from a model | monty_sample |
| Continue sampling | monty_sample_continue |
| Clean up samples | monty_sample_manual_cleanup |
| Collect manually run samples | monty_sample_manual_collect |
| Get information about manually scheduled samples | monty_sample_manual_info |
| Prepare to sample with manual scheduling | monty_sample_manual_prepare |
| Prepare to continue sampling with manual scheduling | monty_sample_manual_prepare_continue |
| Run sample with manual scheduling | monty_sample_manual_run |
| Adaptive Metropolis-Hastings Sampler | monty_sampler_adaptive |
| Create HMC | monty_sampler_hmc |
| Nested Adaptive Metropolis-Hastings Sampler | monty_sampler_nested_adaptive |
| Nested Random Walk Sampler | monty_sampler_nested_random_walk |
| Random Walk Sampler | monty_sampler_random_walk |
| Thin samples | monty_samples_thin |
| Trace random number calls | with_trace_random |
