[{"_1":2,"_153":-5,"_154":-5},"loaderData",{"_3":4,"_13":14,"_21":22,"_132":133},"root",{"_5":-5,"_6":-7,"_7":8,"_9":-7,"_10":11,"_12":-7},"currentUser","primaryEmail","siteVerification","6LeIxAcTAAAAAJcZVRqyHh71UMIEGNQ_MXjiZKhI","toast","enableDev",false,"incidentStatus","routes/_layout",{"_15":-5,"_16":17,"_18":17,"_19":11,"_20":11},"username","isAskGordonEnabled",true,"isUsingNewTemplate","showMinimalLanding","showHoneypotTrap","routes/_layout.r.$namespace.$reponame",{"_23":24,"_25":-5,"_26":11,"_27":11,"_28":29,"_30":31,"_32":-5,"_33":11,"_34":35,"_84":-5,"_85":86,"_125":-5,"_126":11,"_127":17,"_128":11,"_129":-5,"_130":131},"canonicalUrl","https://hub.docker.com/r/seabbs/speedymarkov/tags","mcpServer","canSeeDockerfile","canSeeAutobuilds","namespace","seabbs","reponame","speedymarkov","cloudBuildSource","isAutobuildsAvailable","repo",{"_36":37,"_38":39,"_41":42,"_43":44,"_45":11,"_46":47,"_48":49,"_50":51,"_53":31,"_28":29,"_54":55,"_56":40,"_57":58,"_59":60,"_61":62,"_63":-5,"_64":58,"_65":66,"_67":11,"_68":29,"_69":70,"_75":11,"_76":77,"_81":29,"_82":83},"categories",[],"content_types",[40],"image","date_registered","2019-11-22T14:03:19.198002Z","description","Speed up Discrete Markov Model Simulations.","is_private","last_updated","2019-12-11T12:54:29.411879Z","last_modified","2024-10-16T13:48:34.145251Z","media_types",[52],"application/vnd.docker.container.image.v1+json","name","pull_count",1166,"repository_type","star_count",0,"status",1,"status_description","active","source","collaborator_count","full_description","\n# Speed up Discrete Markov Model Cost Effectiveness Simulations $\"\"$ \n\n[![badge](https://img.shields.io/badge/Launch-SpeedyMarkov-blue.svg)](https://mybinder.org/v2/gh/seabbs/SpeedyMarkov/master?urlpath=rstudio)\n[![CRAN\\_Release\\_Badge](http://www.r-pkg.org/badges/version-ago/SpeedyMarkov)](https://CRAN.R-project.org/package=SpeedyMarkov)\n[![develVersion](https://img.shields.io/badge/devel%20version-0.1.0-blue.svg?style=flat)](https://github.com/seabbs/SpeedyMarkov)\n[![DOI](https://zenodo.org/badge/223414207.svg)](https://zenodo.org/badge/latestdoi/223414207)\n\n**Work in progress**\n\nThis package:\n\n - Compares a functional markov modelling approach to a reference\n approach for several example models.\n - Explores approaches to speeding up Markov modelling in a principled\n fashion making use of C++ when required.\n - Details the benefits of parallisation and provide a code structure\n in which parallisation is easy to make use of.\n - Provides a toolkit for use in discrete Markov modelling.\n - Provides optimised code that may be ported into other applications\n and workflows.\n\nThe work in this package was started at the Health Economic 2019\nhackathon hosted at Imperial. Much of this work is based on that\ndeveloped by the\n[hermes6](https://github.com/HealthEconomicsHackathon/hermes6) team. The\noriginal reference approach was developed by [Howard\nThom](https://orcid.org/0000-0001-8576-5552).\n\n## Installation\n\nInstall the CRAN version (when released):\n\n``` r\ninstall.packages(\"SpeedyMarkov\")\n```\n\nAlternatively install the development version from GitHub:\n\n``` r\n# install.packages(\"remotes\")\nremotes::install_github(\"seabbs/SpeedyMarkov\")\n```\n\n## Documentation\n\n[![Documentation](https://img.shields.io/badge/Documentation-release-lightgrey.svg?style=flat)](https://www.samabbott.co.uk/SpeedyMarkov/)\n[![Development\ndocumentation](https://img.shields.io/badge/Documentation-development-lightblue.svg?style=flat)](https://www.samabbott.co.uk/SpeedyMarkov/dev)\n[![Functions](https://img.shields.io/badge/Documentation-functions-orange.svg?style=flat)](https://www.samabbott.co.uk/SpeedyMarkov/reference/index.html)\n\n## Testing\n\n[![Travis-CI Build\nStatus](https://travis-ci.org/seabbs/SpeedyMarkov.svg?branch=master)](https://travis-ci.org/seabbs/SpeedyMarkov)\n[![AppVeyor Build\nStatus](https://ci.appveyor.com/api/projects/status/github/seabbs/SpeedyMarkov?branch=master&svg=true)](https://ci.appveyor.com/project/seabbs/SpeedyMarkov)\n[![Coverage\nStatus](https://img.shields.io/codecov/c/github/seabbs/SpeedyMarkov/master.svg)](https://codecov.io/github/seabbs/SpeedyMarkov?branch=master)\n\n## Quick start\n\nThe first step is to specify a Markov model in the format specified by\n`SpeedyMarkov`. An example framework is `example_two_state_markov` which\nis a two state Markov model that compares an intervention to a baseline.\nSee `?example_two_state_markov` for more details.\n\n``` r\nSpeedyMarkov::example_two_state_markov()\n#> $transitions_list\n#> $transitions_list$SoC\n#> function(samples = NULL) {\n#> # Sample transitions\n#> tmp <- list(VGAM::rdiric(samples, c(88, 12)),\n#> VGAM::rdiric(samples, c(8, 92)))\n#> \n#> # Arrange as matrices\n#> tmp <- SpeedyMarkov::matrix_arrange(tmp)\n#> \n#> return(tmp)\n#> }\n#> \n#> \n#> \n#> $transitions_list$`Soc with Website`\n#> function(baseline = NULL) {\n#> \n#> #Sample transitions for each baseline matrix\n#> samples <- length(baseline)\n#> tmp <- VGAM::rdiric(samples,c(85,15))\n#> \n#> # Update baseline\n#> updated <- purrr::map(1:samples, function(sample) {\n#> update <- baseline[[sample]]\n#> update[1, ] <- tmp[sample, ]\n#> return(update)\n#> })\n#> \n#> return(updated)\n#> }\n#> \n#> \n#> \n#> \n#> $qalys\n#> function(samples = NULL) {\n#> qaly <- function(samples = 1) {\n#> ## Sample\n#> tmp <- list(stats::rnorm(samples, mean = 0.95,sd = 0.01) / 2,\n#> rep(1 / 2, samples))\n#> \n#> out <- SpeedyMarkov::vector_arrange(tmp)\n#> \n#> return(out)\n#> }\n#> \n#> soc <- qaly(samples = samples)\n#> soc_with_website <- soc\n#> \n#> out <- list(soc, soc_with_website)\n#> names(out) <- list(\"SoC\", \"Soc with Website\")\n#> \n#> out <- purrr::transpose(out)\n#> \n#> return(out)\n#> }\n#> \n#> \n#> \n#> $intervention_costs\n#> function(samples = NULL) {\n#> ## Sample\n#> tmp <- list(rep(0, samples),\n#> rep(50, samples))\n#> \n#> out <- SpeedyMarkov::vector_arrange(tmp)\n#> \n#> return(out)\n#> }\n#> \n#> \n#> \n#> $state_costs\n#> function(samples = NULL) {\n#> state_cost <- function(samples = 1) {\n#> tmp <- list(rep(0, samples),\n#> rep(0, samples))\n#> \n#> out <- SpeedyMarkov::vector_arrange(tmp)\n#> \n#> return(out)\n#> \n#> }\n#> \n#> soc <- state_cost(samples = samples)\n#> soc_with_website <- soc\n#> \n#> out <- list(soc, soc_with_website)\n#> names(out) <- list(\"SoC\", \"Soc with Website\")\n#> \n#> out <- purrr::transpose(out)\n#> \n#> return(out)\n#> }\n#> \n#> \n#> \n#> $cohorts\n#> function(samples = NULL) {\n#> \n#> cohort <- function(samples = 1) {\n#> tmp <- list(rep(1, samples),\n#> rep(0, samples))\n#> \n#> out <- SpeedyMarkov::vector_arrange(tmp)\n#> \n#> return(out)\n#> }\n#> \n#> soc <- cohort(samples = samples)\n#> soc_with_website <- soc\n#> \n#> out <- list(soc, soc_with_website)\n#> names(out) <- list(\"SoC\", \"Soc with Website\")\n#> \n#> out <- purrr::transpose(out)\n#> \n#> return(out)\n#> }\n#> \n#> \n#> \n#> attr(,\"class\")\n#> [1] \"SpeedyMarkov\" \"list\"\n```\n\nOnce a model has been specified a cost effectiveness analysis can run\nusing the following function\ncall.\n\n``` r\nSpeedyMarkov::markov_ce_pipeline(SpeedyMarkov::example_two_state_markov(), \n duration = 100, samples = 10, discount = 1.035, \n baseline = 1, willingness_to_pay_thresold = 20000)\n#> $simulations_with_ce\n#> # A tibble: 20 x 12\n#> sample intervention transition state_cost intervention_co… cohort qalys\n#> \n#> 1 1 SoC 0 2 1 Soc with We… 50 3 2 SoC 0 4 2 Soc with We… 50 5 3 SoC 0 6 3 Soc with We… 50 7 4 SoC 0 8 4 Soc with We… 50 9 5 SoC 0 10 5 Soc with We… 50 11 6 SoC 0 12 6 Soc with We… 50 13 7 SoC 0 14 7 Soc with We… 50 15 8 SoC 0 16 8 Soc with We… 50 17 9 SoC 0 18 9 Soc with We… 50 19 10 SoC 0 20 10 Soc with We… 50 # … with 5 more variables: total_costs , total_qalys ,\n#> # incremental_costs , incremental_qalys ,\n#> # incremental_net_benefit \n#> \n#> $summarised_ce\n#> # A tibble: 2 x 13\n#> intervention mean_costs sd_costs mean_qalys sd_qlays mean_incrementa…\n#> \n#> 1 SoC 0 0 14.0 0.0861 0 \n#> 2 Soc with We… 50 0 14.0 0.103 -0.00248\n#> # … with 7 more variables: sd_incremental_qlays ,\n#> # mean_incremental_costs , sd_incremental_costs ,\n#> # mean_incremental_net_benefit , sd_incremental_net_benefit ,\n#> # probability_cost_effective , icer \n```\n\nSee\n[Functions](https://www.samabbott.co.uk/SpeedyMarkov/reference/index.html)\nfor more details of the functions included in the package\n(`markov_ce_pipeline` for example wraps multiple modular - user\ncustomisable - functions). The package vignettes also provide more\ndetail.\n\n## Contributing\n\nFile an issue [here](https://github.com/seabbs/SpeedyMarkov/issues) if\nthere is a feature that you think is missing from the package, or better\nyet submit a pull request.\n\nPlease note that the `SpeedyMarkov` project is released with a\n[Contributor Code of\nConduct](https://github.com/seabbs/SpeedyMarkov/blob/master/.github/CODE_OF_CONDUCT.md).\nBy contributing to this project, you agree to abide by its terms.\n\n## Citing\n\nIf using `SpeedyMarkov` please consider citing the package in the\nrelevant work. Citation information can be generated in R using the\nfollowing (after installing the package),\n\n``` r\ncitation(\"SpeedyMarkov\")\n#> \n#> To cite SpeedyMarkov in publications use:\n#> \n#> Sam Abbott (2019). SpeedyMarkov - -\n#> \n#> A BibTeX entry for LaTeX users is\n#> \n#> @Article{,\n#> title = {SpeedyMarkov},\n#> author = {Sam Abbott and Howard Thom},\n#> journal = {-},\n#> year = {2019},\n#> }\n```\n\n## Docker\n\nThis package has been developed in docker based on the\n`rocker/tidyverse` image, to access the development environment enter\nthe following at the command line (with an active docker daemon\nrunning),\n\n``` bash\ndocker pull seabbs/SpeedyMarkov\ndocker run -d -p 8787:8787 -e USER=SpeedyMarkov -e PASSWORD=SpeedyMarkov --name SpeedyMarkov seabbs/speedymarkov\n```\n\nThe rstudio client can be accessed on port `8787` at `localhost` (or\nyour machines ip). The default username is ceplotr and the default\npassword is SpeedyMarkov. Alternatively, access the development\nenvironment via\n[binder](https://mybinder.org/v2/gh/seabbs/SpeedyMarkov/master?urlpath=rstudio).\n","has_starred","hub_user","immutable_tags_settings",{"_71":11,"_72":73},"enabled","rules",[74],".*","is_automated","permissions",{"_78":17,"_79":11,"_80":11},"read","write","admin","user","storage_size",26156054107,"orgData","recentTags",[87],{"_88":40,"_89":90,"_91":92,"_93":94,"_95":96,"_46":114,"_115":90,"_116":29,"_117":52,"_53":118,"_119":120,"_121":105,"_122":114,"_123":62,"_124":17},"content_type","creator",1943981,"full_size",2513000297,"id",77305783,"images",[97],{"_98":99,"_100":101,"_102":103,"_104":105,"_106":107,"_108":109,"_110":103,"_111":-5,"_112":92,"_59":62,"_113":-5},"architecture","amd64","digest","sha256:54fe6d96b1cb4488724cfc3d08f6755d282bf44f262615f74989df1d35f09795","features","","last_pulled","2026-09-08T20:37:26.148747466Z","last_pushed","2019-12-11T12:54:30Z","os","linux","os_features","os_version","size","variant","2019-12-11T12:54:29.128491Z","last_updater","last_updater_username","media_type","latest","repository",8064008,"tag_last_pulled","tag_last_pushed","tag_status","v2","sbxKitMetadata","isPotentialSpam","isHubSbxKitsEnabled","displaySecurityMetadata","tagAttestations","tagsHaveAttestations",[],"routes/_layout.r.$namespace.$reponame.tags",{"_34":134,"_141":142,"_130":151,"_152":11},{"_36":135,"_38":136,"_41":42,"_43":44,"_45":11,"_46":47,"_48":49,"_50":137,"_53":31,"_28":29,"_54":55,"_56":40,"_57":58,"_59":60,"_61":62,"_63":-5,"_64":58,"_65":66,"_67":11,"_68":29,"_69":138,"_75":11,"_76":140,"_81":29,"_82":83},[],[40],[52],{"_71":11,"_72":139},[74],{"_78":17,"_79":11,"_80":11},"tags",{"_143":60,"_144":-5,"_145":-5,"_146":147},"count","next","previous","results",[148],{"_88":40,"_89":90,"_91":92,"_93":94,"_95":149,"_46":114,"_115":90,"_116":29,"_117":52,"_53":118,"_119":120,"_121":105,"_122":114,"_123":62,"_124":17},[150],{"_98":99,"_100":101,"_102":103,"_104":105,"_106":107,"_108":109,"_110":103,"_111":-5,"_112":92,"_59":62,"_113":-5},[],"canDeleteTags","actionData","errors"]

seabbs/speedymarkov

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