Setup | Running the MCMC | Exploring outputs and checking MCMC performance | Using C++ functions

Model | Single temperature rung (no Metropolis coupling) | Multiple temperature rungs

A simple model of population growth

Installing Rcpp | Installing and loading drjacoby

Setup | Running the MCMC | How many rungs to use?

Setup | Running multiple chains | Running multiple chains using C++ log likelihood or log prior functions

Handling lack of vault gracefully | Installing vault

Connecting to vault | Reading, writing, listing and deleting secrets | Alternative login approaches | Username and password (userpass) | GitHub (github) | LDAP (ldap)

Avoiding output() | Limited support for interpolate() | Delays are not supported | Not all distributions are supported | Interface differences

Using MCMC to infer parameters

The packages | Documentation | The TypeScript ecosystem | Roadmap

Coping with missing data

Single variable: Logistic growth | Specifying parameters | More than one variable: the Lorenz attractor | Delay models | Arrays | Generalised Lotka-Volterra model | Interpolating functions

Ordinary differential equation models | Models implemented in R | Dense output and interpolation | Delay differential equation models | A model in R | Differences with deSolve | Models implemented in C

A simple example - random walk | Model code | Constructing a model | Running a model in parallel | A more interesting example | Other methods | Reordering particles | Set particle state | Reset the model | Use within a package

Background using R's random number generator | Basic implementation using dust | Parallel implementation with dust and OpenMP | More on the pointer object

Running multiple realisations | Parallelisation | Efficient running | Efficient state handling | Useful verbs | A compilation target

Using print() | print format strings | Conditional display | Controlling precision | Current limitations

Setup | All-in-one | Restarting

Stochastic SIR model definition | Inferring parameters with mcstate | Model data | Defining the comparison function | Inferring parameters | Using MCMC to infer parameters | Tuning the pMCMC | Running predictions | Fitting to multiple datastreams

Stochastic SIR model definition | Implementing the SIR model using odin.dust | Saving a model into a package | Running the SIR model with dust | Adding age structure to the model

A note on seeding | Distributed seeding | Continuing the streams | Considerations | Use cases | Summary

Principles | Running a model with GPU support | Writing a GPU-capable model | Data comparison functions | Developing a GPU model

Supported distributions | Performance | Underlying random number engine | Reusing the random random number generator in other projects | In a package | Standalone, parallel with OpenMP | Standalone, parallel on a GPU | Other packages with similar functionality

Discrete compartmental models in a nutshell | From continuous to discrete time | Stochastic processes | Binomial distribution | Poisson distribution | Multinomial distribution | Implementation using odin | Deterministic SIR model | Stochastic SIR model | A stochastic SEIRDS model

Basic operators | Array support | Operators | Mathematical operators | Stochastic models

Age-Specific Parameters

Problem motivation | Defining blocks | The Likelihood

Setting up an IF2 run | Running the IF2 algorithm

Model data | Comparison, model and particle filter | Nested parameters | pMCMC and Visualisations

Within-model parallelism | Between-chain parallelism | Considerations

Box-Muller | Polar | Ziggurat | Sampling | Sampling from the tail | The edges | Optimisations

Considerations

Model | MCMC | Plots

Model | MCMC | Posterior plots

Model | Run MCMC | Plots

Overview | Estimating R on sliding weekly windows, with a parametric serial interval | Estimating R with a non parametric serial interval distribution | Estimating R accounting for uncertainty on the serial interval distribution | Estimating R and the serial interval using data on pairs infector/infected | Changing the time windows for estimation | Different ways of specifying the incidence | Specifying imported cases | EpiEstimApp