Installing tools for authoring model modules
Instructions for installing the ready4class, ready4fun and ready4pack libraries.
Instructions for installing the ready4class, ready4fun and ready4pack libraries.
Instructions for installing the ready4use library.
Instructions for installing the ready4show library.
We created a basic synthetic dataset of to represent a clinical youth mental health sample.
Unreleased software and other preliminary work is currently being developed into modules for modelling people, places, platforms and programs.
ready4 uses an object oriented programming (OOP) paradigm to implement computational models.
The ready4class R package supports partially automated and standardised workflows for defining the data structures to be used in computational models.
ready4 uses functional programming to maximise the re-usability of model algorithms.
ready4 supports a modular approach to computational model development.
ready4 modules use a simple and consistent syntax.
The ready4fun R package supports standardised approaches to code authoring that facilitate partial automation of the documenting of model algorithms.
ready4 supports tools to streamline the testing, description and distribution of computational model modules.
The ready4use R package provides tools for supplying data to youth mental health computational models.
Online open access data repositories are the preferred storage locations for ready4 model datasets.
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.
Tools from the ready4show R package support authoring of scientific summaries of analyses with ready4.
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.
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.
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.
Tools from the ready4show R package support authoring of programs and subroutines to implement and report analyses with ready4.
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.
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.
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.
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.
We used functions (soon to be formalised into ready4 modules) from the mychoice R package to design to a discrete choice experiment.
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.
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.
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.
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.
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.
Whether and how you should use a specific version of ready4 software depends in part on its release status.
Current unreleased work to develop modules for modelling the characteristics, relationships, behaviours, risk factors and outcomes of young people and those important to them.
Current unreleased work to develop modules for modelling the demographic, environmental and proximity drivers of access, equity and outcomes in youth mental health.
Current unreleased work to develop modules for modelling the optimal staffing and configuration of support services for young people.
Current very preliminary work to develop modules for modelling the affordability, value for money and appropriate targeting of interventions for young people.
A subroutine for generating catalogues of utility mapping models created with the TTU library.
A template subroutine for generating a scientific manuscript for use with the ready4show library.
A subroutine for generating a scientific manuscript of a longitudinal utility mapping study undertaken with the TTU library.
A subroutine for a summary of the main results from a Discrete Choice Experiment implemented with the mychoice library.
Some work in progress code has yet to be publicly released or fornmally acknowledged as part of the ready4 suite.
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.
Production releases are the versions of software intended for end-users.
Archived releases are permanent, uniquely identified records of key project milestones.
There are two types of framework libraries - a foundational library and libraries of authoring tools.
There are three types of model module libraries - those for describing input data, developing models and making predictions.
Applying Spring To Life model modules to map psychological and functional measures to AQoL-6D health utility