Installing the ready4 framework foundation library
The ready4 framework foundation is the first ready4 library you should install.
Tag: Software
Installing tools for authoring computational models
Instructions for installing the ready4class, ready4fun and ready4pack libraries.
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Template module
ready4 provides a template to facilitate modular implementation of health economic models.
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Framework library releases
Releases of foundation and authoring tools libraries to implement the ready4 framework.
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Code repositories
ready4 software is freely available from multiple open access repositories.
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Installing tools for authoring and managing model datasets
Instructions for installing the ready4use library.
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Provide end users with a simple and consistent syntax for using model modules
Methods are associated with ready4 modules using a novel syntax.
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Installing authoring tools
Depending on how you plan to use ready4, you may need to install some or all of its authoring tools.
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Installing tools for authoring reproducible analyses
Instructions for installing the ready4show library.
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Installing ready4 computational model modules
To implement a modelling analysis with ready4 you need to install computational model modules.
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Create a synthetic population of young people attending primary mental health services
We created a basic synthetic dataset of to represent a clinical youth mental health sample.
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Partially automate maintenance of a modelling project's website
ready4 includes tools that can help maintain the documentation website for a modelling project.
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Library releases
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Downloads
ready4 is a suite of software libraries, freely available for download and installation.
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Develop, adapt and maintain computational health economic models
ready4 software framework libraries provide tools for authoring, documenting and maintaining living and transferable health economic models analyses.
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Use modules of health economic models to implement repeatable research
ready4 software framework libraries provide tools for finding reusable modules of health economic models, supplying them with compatible data and authoring reproducible analysis scripts.
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Implementation
The ready4 prototype software framework is distributed as a collection of framework code libraries that support object-oriented and functional approaches to implementing modular and open source computational models.
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Paradigms
ready4 software is implemented using a combination of object-oriented and functional programming paradigms.
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Tutorials
ready4 software framework libraries provide tools for authoring and sharing model modules, datasets and analyses.
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Why ready4 is object oriented
ready4 uses an object oriented programming (OOP) paradigm to implement computational models.
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Authoring model data structures
The ready4class R package supports partially automated and standardised workflows for defining the data structures to be used in computational models.
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The role of functional programming in ready4 development
ready4 uses functional programming to maximise the re-usability of model algorithms.
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Authoring model algorithms
The ready4fun R package supports standardised approaches to code authoring that facilitate partial automation of the documenting of model algorithms.
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Dissemating citable, documented and quality assured model module libraries
ready4 supports tools to streamline the testing, description and distribution of computational model modules.
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Model youth choices
Replication programs for designing, analysing and reporting discrete choice experiments.
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Create synthetic populations
Replication programs for constructing synthetic populations.
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Model health utility
Replication programs for developing, finding and applying utility mapping algorithms.
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Decision aids
Decision aids provide user interfaces that make it easy to generate practical insight from ready4.
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Code to reproduce and/or replicate scientific studies
The code used when applying ready4 to a number of real world youth mental health policy and research projects is publicly available.
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Subroutine templates for authoring analysis reports
Sub-routine programs can be used to automatically generate standardised reports of analyses undertaken with ready4.
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Find themed collections of ready4 modules
Tools from the ready4 R library can help find details about module libraries from a modelling project.
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Commands for using model modules
Apply model modules using a simple and consistent syntax.
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Ingest data from an open access repository
A tutorial from the Acumen website about using ready4 to search and retrieve data from the Australian Mental Health Systems Models Dataverse.
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Search open access data collections
Online open access data repositories are the preferred storage locations for ready4 model datasets.
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Standardise Variable Values With Fuzzy Logic And Correspondence Tables
Costing health economic datasets is an activity that can involve repeated use of lookup tables. This tutorial describes how a module from the costly R package can help you to use a combination of fuzzy logic and correspondence tables to standardise variable values and thus facilitate partial automation of costing algorithms.
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Finding modules of health economic models
The ready4 library provides tools to help retrieve details of model module collections and documentation on individual model modules.
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Find examples of how model modules are used
Tools from the ready4 R library can help find details about the use of individual model modules.
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Authoring scientific manuscripts
Tools from the ready4show R package support authoring of scientific summaries of analyses with ready4.
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Add a data dictionary to a dataset
Pairing a dataset with its dictionary makes it easier to interpret. This tutorial describes how a module from the ready4use R package can help you to pair a dataset and its dictionary.
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Standardise Variable Values With Lookup Codes
This tutorial describes how a module from the costly R package can help you to use lookup codes to standardise variable values and thus facilitate partial automation of costing algorithms.
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Add metadata to datasets of individual human records
Appending appropriate metadata to datasets of individual unit records can facilitate partial automation of some modelling tasks. This tutorial describes how a module from the youthvars R package can help you to add metadata to a youth mental health dataset so that it can be more readily used by other ready4 modules.
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Validate variable total scores
Vector based classes can be used to help validate variable values. This tutorial describes how to do that with sub-module classes exported as part of the youthvars R package.
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Ingest, label and share model data
The ready4use R package provides tools for supplying data to model modules.
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Using model modules for repeatable analyses
The ready4 framework provides tools to support the authoring of programs and subroutines to run and report analyses with ready4.
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Share data via online repositories
The retrieval and dissemination of data from online data repositories is an essential enabler of open source modelling. This tutorial describes how a module from the ready4use R package can help you to manage this process.
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Score health utility
Using modules from the scorz R package, individual responses to a multi-attribute utility instrument survey can be converted into health utility total scores. This tutorial describes how to do for adolescent AQoL-6D health utility.
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Explore candidate utility mapping models
Using modules from the specific R package, it is possible to undertake an exploratory utility mapping analysis. This tutorial illustrates a hypotehtical example of exploring how to map to EQ-5D health utility.
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Implement a utility mapping study
Using modules from the TTU R package, it is possible to implement a fully reproducible utility mapping study. This tutorial illustrates the main steps using a hypothetical AQoL-6D utility mapping study.
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Find and deploy utility mapping models
Using tools (soon to be formalised into ready4 modules) from the youthu R package, it is possible to find and deploy relevant utility mapping algorithms. This tutorial illustrates the main steps for predicting AQoL-6D utility from psychological and functional measures collected on clinical samples of young people.
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Design a Discrete Choice Experiment
We used functions (soon to be formalised into ready4 modules) from the mychoice R package to design to a discrete choice experiment.
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Use utility mapping algorithms to help implement cost-utility analyses
Using tools (soon to be formalised into ready4 framework modules) from the youthu R package, it is possible to use utility mapping algorithms to help implement cost-utility analyses. This tutorial illustrates the main steps for doing so using psychological and functional measures collected on clinical samples of young people.
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Develop choice models
Using tools (soon to be formalised into ready4 framework modules) from the mychoice R package, it is possible to develop choice models from responses to a discrete choice experiment survey.
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Analyse the results of a Discrete Choice Experiment
Using functions (soon to be formalised into ready4 framework modules) from the mychoice R package, it is possible to develop choice models from responses to a discrete choice experiment survey.
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Predicting the spatial epidemiology of emerging mental disorders
We previously developed a user interface for the epidemiology modules of our Springtides model of places.
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Develop health utility mapping algorithms
Using modules from the TTU, youthvars, scorz and specific libraries, we developed utility mapping algorithms from a sample of young people attending primary mental health care services.
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Predict health utility
Using functions (soon to be formalised into ready4 framework modules) from the youthu R package, we predicted health utility for a synthetic population of young people attending primary mental health care services.
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System requirements
What you need in order to be able to use ready4 software on your machine.
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Framework library descriptions
There are two types of framework libraries - a foundational library and libraries of authoring tools.
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Release statuses
Whether and how you should use a specific version of ready4 software depends in part on its release status.
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Code libraries
The ready4 framework is distributed as six R libraries.
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Library documentation
Each ready4 code library is supported by a standardised set of documentation resources.
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Terms of use
ready4 is distributed without warranties under open source licenses - we just ask you to appropriately cite it.
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Dependencies
Search for ready4 library and function dependencies using our interactive app.
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Open source licensing
ready4 is freely available to all under copy-left licensing arrangements.
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Citing ready4
If you find ready4 useful, please cite it appropriately - it is easy to do!
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Disclaimer
ready4 is distributed without any warranties.
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Installation and set-up
Important information to review before installing and using our software
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Make a catalogue of utility mapping models
A subroutine for generating catalogues of utility mapping models created with the TTU library.
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Author a template scientific manuscript
A template subroutine for generating a scientific manuscript for use with the ready4show library.
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Author a draft scientific manuscript for a utility mapping study
A subroutine for generating a scientific manuscript of a longitudinal utility mapping study undertaken with the TTU library.
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Make results summary for a Discrete Choice Experiment
A subroutine for a summary of the main results from a Discrete Choice Experiment implemented with the mychoice library.
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Unreleased code
Some work in progress code has yet to be publicly released or fornmally acknowledged as part of the ready4 suite.
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Development releases
Development releases provide the most comprehensive and up to date public record of a ready4 project’s source code but may be poorly documented and tested.
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Production releases
Production releases are the versions of software intended for end-users.
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Archived releases
Archived releases are permanent, uniquely identified records of key project milestones.