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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Finding modules and sub-modules
How to find individual ready4 modules and sub-modules.
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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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Using ready4 modules
ready4 modules can be be used to model the people, places, platforms and programs that shape young people’s mental health.
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Our priorities
Our current list of priorities for the development of ready4 shape when and how we need your help.
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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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Modules pipeline
Unreleased software and other preliminary work is currently being developed into modules for modelling people, places, platforms and programs.
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Contribution types
There are a number of ways you can contribute to ready4.
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Subroutines
Subroutines perform part of an analysis and reporting algorithm.
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Authoring ready4 modules
Tools from the ready4class, ready4 fun and ready4pack R packages streamline and standardise the authoring of ready4 modules.
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Overview
How ready4 aims to improve policy and system planning in youth mental health.
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Examples
See how ready4 has been applied to real world decision problems.
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Getting started
What you need to know to start using ready4.
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Motivation
To be accountable, flexible and up to date, ready4 is being implemented as a modular and open source computational model.
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Concepts
A number of concepts are helpful to understand prior to using ready4.
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Model
ready4 represents youth mental health systems in computer code.
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Module
ready4 is comprised of self-contained, reusable components called “modules”.
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Coders
Coders can use ready4 to enhance the impact and re-usability of their algorithms.
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Modellers
Modellers can use ready4 to leverage the work of other modellers and to implement reproducible modelling analyses.
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Modelling project
A ready4 modelling project develops a computational model, adds data and runs analyses.
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Planners
Planners can use ready4 decision aids to generate useful insights.
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Reproducible research
Some core concepts relating to reproducible research have multiple conflicting definitions - this is how we use them.
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Users
How you use and contribute to ready4 will depend on the type of work you do.
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Funders
ready4 provides funders with opportunities to improve the quality, breadth and accountability of supports provided to youth mental health policymakers and system planners.
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Researchers
Researchers can use ready4 to enhance the reproducibility, replicability and transferability of their work.
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Stakeholders
In addition to the main types of intended user, a number of other stakeholders can benefit from and contribute to ready4.
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Transferability
Some models have the potential to be used in multiple contexts - but will often need adaptation for this to be appropriate.
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Young people
Young people can help ensure that ready4 remains accountable for addressing topics of importance to them.
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Software
ready4 is a suite of software, with each included item performing a distinctive role.
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Framework
The ready4 framework comprises a set of standards and the software to implement it.
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Standards
The ready4 framework identifies a set of standards to which the ready4 model, its datasets and analyses are expected to adhere.
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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
The ready4 computational model is the complete collection of all modules developed with the ready4 framework. These modules can be re-used and combined to create other computational models.
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Finding and using open access data
Tools from the ready4 and ready4use libraries can be used to search for relevant open access data collections and ingest data from these collections.
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Model youth choices
Replication programs for designing, analysing and reporting discrete choice experiments.
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Data
To the greatest extent feasible, the data supplied to ready4 modules is accessed and shared via open access data repositories.
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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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Analyses
Programs, sub-routines and user-interfaces combine ready4 modules and datasets to implement reproducible analyses of youth mental health policy and system design topics.
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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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Modules for modelling people
Modules to model the characteristics, relationships, behaviours, risk factors and outcomes of young people and individuals who interact with young people are collectively referred to as the “Spring To Life” model. The currently available modules listed here will be supplemented by additional unreleased work in progress.
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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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Modules for modelling places
Modules for spatio-temporal modelling of the environments that shape young people’s mental health are collectively referred to as the “Springtides” model. No places modules are yet available - just an app built using unreleased work in progress modules.
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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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Modules for modelling platforms
Modules that model the processes, eligibility requirements, staffing and configurations of youth service platforms are collectively referred to as the “First Bounce” model. No platforms modules are yet available - see details on unreleased work in progress.
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Modules for modelling programs
Modules for modelling the efficacy, cost-effectiveness and budget impact of youth mental health programs (e.g. interventions for prevention, treatment and wellbeing) are collectively referred to as the “On Target” model. No programs modules are yet available - see details on preliminary work in progress.
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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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Provide advice
ready4 needs the guidance of young people, decision-makers and technical experts to shape its development.
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Contribute code
Help improve the reliability, functionality and ease of use of ready4 software.
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Contributions
How to contribute to ready4’s development.
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Fund projects
Help us secure our future and accelerate our development.
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Undertake projects
Plan, conduct and disseminate ready4 modelling projects.
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Support the ready4 community
Help develop high quality, clear and comprehensive documentation, instruction and responsive help.
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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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Contributor covenant (code of conduct)
To foster an inclusive and respectful community, all contributors to ready4 are expected to adhere to the Contributor Covenant.
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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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Priority 1: Launch ready4 Minimum Viable Product (MVP) systems model
We want to give potential users confidence that they can appropriately apply ready4 to their decision problems by bringing all our existing development release and unreleased software to production release status.
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Priority 2: Maintain ready4
We want the ready4 framework, model, datasets and decision aids to continually improve and update in response to the needs of potential users and stakeholders.
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Priority 3: Apply ready4 to undertake replications and transfers
We want maintained production releases of ready4 module libraries to be used to implement replications and transfers of the original studies for which that software was developed.
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Priority 4: Grow a ready4 community
We want to develop a community of ready4 users, contributors and stakeholders to sustain the development, maintenance, application, extension and impact of the project.
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Priority 5: Extend the scope of the ready4 model
We want progressively extend the capability of the ready4 model to explore new decision topics in youth mental health.
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Priority 6: Integrate ready4 with other open source tools
We want coders and modellers working in languages such as python to be able to readily use and contribute to ready4.
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Pipeline of people modules
Current unreleased work to develop modules for modelling the characteristics, relationships, behaviours, risk factors and outcomes of young people and those important to them.
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Pipeline of places modules
Current unreleased work to develop modules for modelling the demographic, environmental and proximity drivers of access, equity and outcomes in youth mental health.
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Pipeline of platforms modules
Current unreleased work to develop modules for modelling the optimal staffing and configuration of support services for young people.
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Pipeline of programs modules
Current very preliminary work to develop modules for modelling the affordability, value for money and appropriate targeting of interventions for young people.
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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.