{"Package":"mcmcr","Title":"Manipulate MCMC Samples","Version":"0.6.2","Authors@R":"c(\nperson(\"Joe\", \"Thorley\", , \"joe@poissonconsulting.ca\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-7683-4592\")),\nperson(\"Kirill\", \"Müller\", role = \"ctb\",\ncomment = c(ORCID = \"0000-0002-1416-3412\")),\nperson(\"Nadine\", \"Hussein\", role = \"ctb\",\ncomment = c(ORCID = \"0000-0003-4470-8361\")),\nperson(\"Ayla\", \"Pearson\", role = \"ctb\",\ncomment = c(ORCID = \"0000-0001-7388-1222\")),\nperson(\"Poisson Consulting\", role = c(\"cph\", \"fnd\"))\n)","Description":"Functions and classes to store, manipulate and summarise\nMonte Carlo Markov Chain (MCMC) samples. For more information see\nBrooks et al. (2011) .","License":"MIT + file LICENSE","URL":"https://github.com/poissonconsulting/mcmcr,\nhttps://poissonconsulting.github.io/mcmcr/","BugReports":"https://github.com/poissonconsulting/mcmcr/issues","Depends":"R (>= 4.0)","Imports":"abind, chk, coda, extras, generics, lifecycle, nlist, purrr,\nstats, term, tibble, universals, utils","Suggests":"covr, graphics, rlang, testthat (>= 3.0.0), withr","RdMacros":"lifecycle","Config/Needs/website":"poissonconsulting/poissontemplate","Config/testthat/edition":"3","Encoding":"UTF-8","Language":"en-US","LazyData":"true","RoxygenNote":"7.3.2.9000","NeedsCompilation":"no","Packaged":"2025-01-23 16:04:47 UTC; joe","Author":"Joe Thorley [aut, cre] (),\nKirill Müller [ctb] (),\nNadine Hussein [ctb] (),\nAyla Pearson [ctb] (),\nPoisson Consulting [cph, fnd]","Maintainer":"Joe Thorley ","Repository":"CRAN","Date/Publication":"2025-01-23 16:50:01 UTC","repoType":"cran","tarballUrl":"ftp://cran.r-project.org/pub/R/src/contrib/mcmcr_0.6.2.tar.gz","jsonAuthors":[{"name":"Joe Thorley","email":"joe@poissonconsulting.ca","maintainer":true},{"name":"Kirill Müller","email":null,"maintainer":false},{"name":"Nadine Hussein","email":null,"maintainer":false},{"name":"Ayla Pearson","email":null,"maintainer":false},{"name":"Poisson Consulting ","email":null,"maintainer":false}],"readme":"\n\n\n# mcmcr

\n\n\n\n[![Lifecycle:\nstable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html#stable)\n[![R-CMD-check](https://github.com/poissonconsulting/mcmcr/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/poissonconsulting/mcmcr/actions/workflows/R-CMD-check.yaml)\n[![Codecov test\ncoverage](https://codecov.io/gh/poissonconsulting/mcmcr/graph/badge.svg)](https://app.codecov.io/gh/poissonconsulting/mcmcr)\n[![CRAN\nstatus](https://www.r-pkg.org/badges/version/mcmcr)](https://cran.r-project.org/package=mcmcr)\n![CRAN Downloads](https://cranlogs.r-pkg.org/badges/mcmcr)\n\n\n`mcmcr` is an R package to manipulate Monte Carlo Markov Chain (MCMC)\nsamples.\n\n## Introduction\n\nFor the purposes of this discussion, an MCMC *sample* represents the\nvalue of a *term* from a single *iteration* of a single *chain*. While a\nsimple *parameter* such as an intercept corresponds to a single term,\nmore complex parameters such as an interaction between two factors\nconsists of multiple terms with their own inherent dimensionality - in\nthis case a matrix. A set of MCMC samples can be stored in different\nways.\n\n### Existing Classes\n\nThe three most common S3 classes store MCMC samples as follows:\n\n- `coda::mcmc` stores the MCMC samples from a single chain as a matrix\n where each each row represents an iteration and each column represents\n a variable\n- `coda::mcmc.list` stores multiple `mcmc` objects (with identical\n dimensions) as a list where each object represents a parallel chain\n- `rjags::mcarray` stores the samples from a single parameter where the\n initial dimensions are the parameter dimensions, the second to last\n dimension is iterations and the last dimension is chains.\n\nIn the first two cases the terms/parameters are represented by a single\ndimension which means that the dimensionality inherent in the parameters\nis stored in the labelling of the variables, ie,\n`\"bIntercept\", \"bInteraction[1,2]\", \"bInteraction[2,1]\", ...`. The\nstructure of the `mcmc` and `mcmc.list` objects emphasizes the\ntime-series nature of MCMC samples and is optimized for thining. In\ncontrast `mcarray` objects preserve the dimensionality of the\nparameters.\n\n### New Classes\n\nThe `mcmcr` package defines three related S3 classes which also preserve\nthe dimensionality of the parameters:\n\n- `mcmcr::mcmcarray` is very similar to `rjags::mcarray` except that the\n first dimension is the chains, the second dimension is iterations and\n the subsequent dimensions represent the dimensionality of the\n parameter (it is called `mcmcarray` to emphasize that the MCMC\n dimensions ie the chains and iterations come first);\n- `mcmcr::mcmcr` stores multiple uniquely named `mcmcarray` objects with\n the same number of chains and iterations.\n- `mcmcr::mcmcrs` stores multiple `mcmcr` objects with the same\n parameters, chains and iterations.\n\nAll five classes (`mcmc`, `mcmc.list`, `mcarray`, `mcmcarray`, `mcmcr`\nand `mcmcrs`) are collectively referred to as MCMC objects.\n\n## Why mcmcr?\n\n`mcmcarray` objects were developed to facilitate manipulation of the\nMCMC samples. `mcmcr` objects were developed to allow a set of\ndimensionality preserving parameters from a single analysis to be\nmanipulated as a whole. `mcmcrs` objects were developed to allow the\nresults of multiple analyses using the same model to be manipulated\ntogether.\n\nThe `mcmcr` package (together with the\n[term](https://github.com/poissonconsulting/term) and\n[nlist](https://github.com/poissonconsulting/nlist) packages) introduces\na variety of (often) generic functions to manipulate and query\n`mcmcarray`, `mcmcr` and `mcmcrs` objects (and `term` and `nlist` and\n`nlists` objects).\n\nIn particular it provides functions to\n\n- coerce from and to `mcarray`, `mcmc` and `mcmc.list` objects;\n- extract an objects `coef` table (as a tibble);\n- query an object’s `nchains`, `niters`, `term::npars`, `term::nterms`,\n `nlist::nsims` and `nlist::nsams` as well as it’s parameter dimensions\n (`term::pdims`) and term indices (`term::tindex`);\n- `subset` objects by chains, iterations and/or parameters;\n- `bind_xx` a pair of objects by their `xx_chains`, `xx_iterations`,\n `xx_parameters` or (parameter) `xx_dimensions`;\n- combine the samples of two (or more) MCMC objects using\n `combine_samples` (or `combine_samples_n`) or combine the samples of a\n single MCMC object by reducing its dimensions using\n `combine_dimensions`;\n- `collapse_chains` or `split_chains` an object’s chains;\n- `mcmc_map` over an objects values;\n- transpose an objects parameter dimensions using `mcmc_aperm`;\n- assess if an object has `converged` using `rhat` and `esr`\n (effectively sampling rate);\n- and of course `thin`, `rhat`, `ess` (effective sample size), `print`,\n `plot` etc said objects.\n\nThe code is opinionated which has the advantage of providing a small set\nof stream-lined functions. For example the only ‘convergence’ metric is\nthe uncorrected, untransformed, univariate split R-hat (potential scale\nreduction factor). If you can convince me that additional features are\nimportant I will add them or accept a pull request (see below).\nAlternatively you might want to use the `mcmcr` package to manipulate\nyour samples before coercing them to an `mcmc.list` to take advantage of\nall the summary functions in packages such as `coda`.\n\n## Demonstration\n\n``` r\nlibrary(mcmcr)\n#> Registered S3 method overwritten by 'mcmcr':\n#> method from \n#> as.mcmc.nlists nlist\n\nmcmcr_example\n#> $alpha\n#> [1] 3.718025 4.718025\n#> \n#> nchains: 2 \n#> niters: 400 \n#> \n#> $beta\n#> [,1] [,2]\n#> [1,] 0.9716535 1.971654\n#> [2,] 1.9716535 2.971654\n#> \n#> nchains: 2 \n#> niters: 400 \n#> \n#> $sigma\n#> [1] 0.7911975\n#> \n#> nchains: 2 \n#> niters: 400\n\ncoef(mcmcr_example, simplify = TRUE)\n#> term estimate lower upper svalue\n#> 1 alpha[1] 3.7180250 2.2120540 5.232403 9.645658\n#> 2 alpha[2] 4.7180250 3.2120540 6.232403 9.645658\n#> 3 beta[1,1] 0.9716535 0.2514796 1.713996 5.397731\n#> 4 beta[2,1] 1.9716535 1.2514796 2.713996 7.323730\n#> 5 beta[1,2] 1.9716535 1.2514796 2.713996 7.323730\n#> 6 beta[2,2] 2.9716535 2.2514796 3.713996 9.645658\n#> 7 sigma 0.7911975 0.4249618 2.559520 9.645658\nrhat(mcmcr_example, by = \"term\")\n#> $alpha\n#> [1] 2.002 2.002\n#> \n#> $beta\n#> [,1] [,2]\n#> [1,] 1.147 1.147\n#> [2,] 1.147 1.147\n#> \n#> $sigma\n#> [1] 1\nplot(mcmcr_example[[\"alpha\"]])\n```\n\n

\n\n## Installation\n\n### Release\n\nTo install the release version from\n[CRAN](https://CRAN.R-project.org/package=mcmcr).\n\n``` r\ninstall.packages(\"mcmcr\")\n```\n\nThe website for the release version is at\n.\n\n### Development\n\nTo install the development version from\n[GitHub](https://github.com/poissonconsulting/mcmcr)\n\n``` r\n# install.packages(\"remotes\")\nremotes::install_github(\"poissonconsulting/mcmcr\")\n```\n\nor from [r-universe](https://poissonconsulting.r-universe.dev/mcmcr).\n\n``` r\ninstall.packages(\"mcmcr\", repos = c(\"https://poissonconsulting.r-universe.dev\", \"https://cloud.r-project.org\"))\n```\n\n## Inspiration\n\n[coda](https://github.com/cran/coda) and\n[rjags](https://github.com/cran/rjags)\n\n## Contribution\n\nPlease report any\n[issues](https://github.com/poissonconsulting/mcmcr/issues).\n\n[Pull requests](https://github.com/poissonconsulting/mcmcr/pulls) are\nalways welcome.\n\n## Code of Conduct\n\nPlease note that the mcmcr project is released with a [Contributor Code\nof\nConduct](https://contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html).\nBy contributing to this project, you agree to abide by its terms.\n\n## References\n\nBrooks, S., Gelman, A., Jones, G.L., and Meng, X.-L. (Editors). 2011.\nHandbook for Markov Chain Monte Carlo. Taylor & Francis, Boca Raton.\nISBN: 978-1-4200-7941-8.","jobInfo":{"package":"mcmcr","version":"0.6.2","parsingStatus":"success","parserVersion":2,"parsedAt":"2025-01-23T18:30:26+0000"}}

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Examples