Framework library releases
Releases of foundation and authoring tools libraries to implement the ready4 framework.
Tag: Software
Programs releases
Releases of programs for implementing modelling analyses.
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Subroutine releases
Releases of subroutines used in modelling analyses.
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Installing the ready4 framework foundation library
The ready4 framework foundation is the first ready4 library you should install.
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Installing tools for authoring model modules
Instructions for installing the ready4class, ready4fun and ready4pack libraries.
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Installing tools for authoring and managing model datasets
Instructions for installing the ready4use library.
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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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People module library releases
Releases of module libraries for modelling people (collectively, the Spring To Life model).
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Installing tools for authoring reproducible analyses
Instructions for installing the ready4show library.
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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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Installing ready4 computational model modules
To implement a modelling analysis with ready4 you need to install computational model modules.
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Programs
Programs are used to generate and report a model analysis.
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Subroutines
Subroutines perform part of an analysis and reporting algorithm.
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Executable releases
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Library releases
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Releases of module libraries
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Software
ready4 is a suite of software, with each included item performing a distinctive role.
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Implementation
The software to help ensure that the ready4 model adheres to consistent standards is distributed as a collection of framework code libraries.
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Paradigms
ready4 software is implemented using a combination of object-oriented and functional programming paradigms.
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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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Modularity
ready4 supports a modular approach to computational model development.
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Syntax
ready4 modules use a simple and consistent syntax.
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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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Authoring model data
The ready4use R package provides tools for supplying data to youth mental health computational models.
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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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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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Authoring scientific manuscripts
Tools from the ready4show R package support authoring of scientific summaries of analyses with ready4.
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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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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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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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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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Authoring reproducible analyses
Tools from the ready4show R package support authoring of programs and subroutines to implement and report analyses with ready4.
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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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Code repositories
ready4 software is freely available from multiple open access repositories.
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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
Code libraries are used to distribute software for applying our framework and implementing computational model modules.
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Current libraries
ready4 libraries include tools for applying a modelling framework and for implementing computational models.
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Executables
Executables (programs and sub-routines) are used to apply computational models to data and to report the resulting analyses.
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User interfaces
User interfaces make it easier for non-technical users to explore and use ready4 models.
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Code 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.
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Framework libraries
There are two types of framework libraries - a foundational library and libraries of authoring tools.
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Model module libraries
There are three types of model module libraries - those for describing input data, developing models and making predictions.
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Predicting Quality Adjusted Life Years in young people attending primary mental health services
Applying Spring To Life model modules to map psychological and functional measures to AQoL-6D health utility
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readyforwhatsnext - conference posters now available
Initial set of academic posters relating to the development of the readyforwhatsnext model.