R: A Framework for Data-Driven Stochastic Disease Spread Simulations

# A Framework for Data-Driven Stochastic Disease Spread Simulations

## Documentation for package ‘SimInf’ version 10.1.0

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

A B C D E G I L M N O P R S T U V

SimInf-package | A Framework for Data-Driven Stochastic Disease Spread Simulations

## -- A --

abc | Approximate Bayesian computation
abc-method | Approximate Bayesian computation
add_spatial_coupling_to_ldata | Add information about spatial coupling between nodes to 'ldata'
as.data.frame.SimInf_abc | Coerce to data frame
as.data.frame.SimInf_events | Coerce events to a data frame
as.data.frame.SimInf_individual_events | Coerce to data frame
as.data.frame.SimInf_pmcmc | Coerce to data frame

## -- B --

boxplot-method | Box plot of number of individuals in each compartment

## -- C --

continue_abc | Run more generations of ABC SMC
continue_abc-method | Run more generations of ABC SMC
continue_pmcmc | Run more iterations of PMCMC
continue_pmcmc-method | Run more iterations of PMCMC
C_code | Extract the C code from a 'SimInf_model' object

## -- D --

distance_matrix | Create a distance matrix between nodes for spatial models

## -- E --

edge_properties_to_matrix | Convert an edge list with properties to a matrix
events | Extract the events from a 'SimInf_model' object
events-method | Extract the events from a 'SimInf_model' object
events_SEIR | Example data to initialize events for the 'SEIR' model
events_SIR | Example data to initialize events for the 'SIR' model
events_SIS | Example data to initialize events for the 'SIS' model
events_SISe | Example data to initialize events for the 'SISe' model
events_SISe3 | Example data to initialize events for the 'SISe3' model

## -- G --

gdata | Extract global data from a 'SimInf_model' object
gdata-method | Extract global data from a 'SimInf_model' object
gdata<- | Set a global data parameter for a 'SimInf_model' object
gdata<--method | Set a global data parameter for a 'SimInf_model' object
get_individuals | Extract individuals from 'SimInf_individual_events'
get_individuals-method | Extract individuals from 'SimInf_individual_events'

## -- I --

indegree | Determine in-degree for each node in a model
individual_events | Individual events

## -- L --

lambertW0 | Lambert W0 function
ldata | Extract local data from a node
ldata-method | Extract local data from a node
length-method | Length of the MCMC chain
logLik-method | Log likelihood

## -- M --

mparse | Model parser to define new models to run in 'SimInf'

## -- N --

nodes | Example data with spatial distribution of nodes
node_events | Transform individual events to node events for a model
node_events-method | Transform individual events to node events for a model
n_compartments | Determine the number of compartments in a model
n_compartments-method | Determine the number of compartments in a model
n_generations | Determine the number of generations
n_generations-method | Determine the number of generations
n_nodes | Determine the number of nodes in a model
n_nodes-method | Determine the number of nodes in a model
n_replicates | Determine the number of replicates in a model
n_replicates-method | Determine the number of replicates in a model

## -- O --

outdegree | Determine out-degree for each node in a model

## -- P --

package_skeleton | Create a package skeleton from a 'SimInf_model'
pairs-method | Scatterplot of number of individuals in each compartment
pfilter | Bootstrap particle filter
pfilter-method | Bootstrap particle filter
plot-method | Display the ABC posterior distribution
plot-method | Display the distribution of scheduled events over time
plot-method | Display the distribution of individual events over time
plot-method | Diagnostic plot of a particle filter object
plot-method | Display the PMCMC posterior distribution
plot-method | Display the outcome from a simulated trajectory
pmcmc | Particle Markov chain Monte Carlo (PMCMC) algorithm
pmcmc-method | Particle Markov chain Monte Carlo (PMCMC) algorithm
prevalence | Generic function to calculate prevalence from trajectory data
prevalence-method | Calculate prevalence from a model object with trajectory data
prevalence-method | Extract prevalence from running a particle filter
prevalence-method | Extract prevalence from fitting a PMCMC algorithm
punchcard<- | Set a template for where to record result during a simulation
punchcard<--method | Set a template for where to record result during a simulation

## -- R --

run | Run the SimInf stochastic simulation algorithm
run-method | Run the SimInf stochastic simulation algorithm

## -- S --

SEIR | Create an SEIR model
SEIR-class | Definition of the 'SEIR' model
select_matrix | Extract the select matrix from a 'SimInf_model' object
select_matrix-method | Extract the select matrix from a 'SimInf_model' object
select_matrix<- | Set the select matrix for a 'SimInf_model' object
select_matrix<--method | Set the select matrix for a 'SimInf_model' object
set_num_threads | Specify the number of threads that SimInf should use
shift_matrix | Extract the shift matrix from a 'SimInf_model' object
shift_matrix-method | Extract the shift matrix from a 'SimInf_model' object
shift_matrix<- | Set the shift matrix for a 'SimInf_model' object
shift_matrix<--method | Set the shift matrix for a 'SimInf_model' object
show-method | Print summary of a 'SimInf_abc' object
show-method | Brief summary of 'SimInf_events'
show-method | Print summary of a 'SimInf_individual_events' object
show-method | Brief summary of 'SimInf_model'
show-method | Brief summary of a 'SimInf_pfilter' object
show-method | Brief summary of a 'SimInf_pmcmc' object
SimInf | A Framework for Data-Driven Stochastic Disease Spread Simulations
SimInf_abc-class | Class '"SimInf_abc"'
SimInf_events | Create a 'SimInf_events' object
SimInf_events-class | Class '"SimInf_events"'
SimInf_individual_events-class | Class '"SimInf_individual_events"'
SimInf_model | Create a 'SimInf_model'
SimInf_model-class | Class '"SimInf_model"'
SimInf_pfilter-class | Class '"SimInf_pfilter"'
SimInf_pmcmc-class | Class '"SimInf_pmcmc"'
SIR | Create an SIR model
SIR-class | Definition of the SIR model
SIS | Create an SIS model
SIS-class | Definition of the SIS model
SISe | Create a SISe model
SISe-class | Definition of the 'SISe' model
SISe3 | Create a 'SISe3' model
SISe3-class | Definition of the 'SISe3' model
SISe3_sp | Create an 'SISe3_sp' model
SISe3_sp-class | Definition of the 'SISe3_sp' model
SISe_sp | Create a 'SISe_sp' model
SISe_sp-class | Definition of the 'SISe_sp' model
summary-method | Detailed summary of a 'SimInf_abc' object
summary-method | Detailed summary of a 'SimInf_events' object
summary-method | Detailed summary of a 'SimInf_individual_events' object
summary-method | Detailed summary of a 'SimInf_model' object
summary-method | Detailed summary of a 'SimInf_pfilter' object
summary-method | Detailed summary of a 'SimInf_pmcmc' object

## -- T --

trajectory | Generic function to extract data from a simulated trajectory
trajectory-method | Extract data from a simulated trajectory
trajectory-method | Extract filtered trajectory from running a particle filter
trajectory-method | Extract filtered trajectories from fitting a PMCMC algorithm

## -- U --

u0 | Get the initial compartment state
u0-method | Get the initial compartment state
u0<- | Update the initial compartment state u0 in each node
u0<--method | Update the initial compartment state u0 in each node
u0_SEIR | Example data to initialize the 'SEIR' model
u0_SIR | Example data to initialize the 'SIR' model
u0_SIS | Example data to initialize the 'SIS' model
u0_SISe | Example data to initialize the 'SISe' model
u0_SISe3 | Example data to initialize the 'SISe3' model

## -- V --

v0<- | Update the initial continuous state v0 in each node
v0<--method | Update the initial continuous state v0 in each node