Package index • SimInf

All functions

C_code(): Extract the C code from a SimInf_model object

SEIR-class: Definition of the ‘SEIR’ model

SEIR(): Create an SEIR model

SIR-class: Definition of the SIR model

SIR(): Create an SIR model

SIS-class: Definition of the SIS model

SIS(): Create an SIS model

SISe-class: Definition of the SISe model

SISe(): Create a SISe model

SISe3-class: Definition of the ‘SISe3’ model

SISe3(): Create a SISe3 model

SISe3_sp-class: Definition of the ‘SISe3_sp’ model

SISe3_sp(): Create an SISe3_sp model

SISe_sp-class: Definition of the SISe_sp model

SISe_sp(): Create a SISe_sp model

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

SimInf_abc-class: Class "SimInf_abc"

SimInf_events-class: Class "SimInf_events"

SimInf_events(): Create a SimInf_events object

SimInf_individual_events-class: Class "SimInf_individual_events"

SimInf_model-class: Class "SimInf_model"

SimInf_model(): Create a SimInf_model

SimInf_pfilter-class: Class "SimInf_pfilter"

SimInf_pmcmc-class: Class "SimInf_pmcmc"

abc(): 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

boxplot(<SimInf_model>): Box plot of number of individuals in each compartment

continue_abc(): Run more generations of ABC SMC

continue_pmcmc(): Run more iterations of PMCMC

distance_matrix(): Create a distance matrix between nodes for spatial models

edge_properties_to_matrix(): Convert an edge list with properties to a matrix

events(): 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

`gdata<-`(): Set a global data parameter for a SimInf_model object

gdata(): Extract global data from a SimInf_model object

get_individuals(): Extract individuals from SimInf_individual_events

indegree(): Determine in-degree for each node in a model

individual_events(): Individual events

lambertW0(): Lambert W0 function

ldata(): Extract local data from a node

length(<SimInf_pmcmc>): Length of the MCMC chain

logLik(<SimInf_pfilter>): Log likelihood

mparse(): Model parser to define new models to run in SimInf

n_compartments(): Determine the number of compartments in a model

n_generations(): Determine the number of generations

n_nodes(): Determine the number of nodes in a model

n_replicates(): Determine the number of replicates in a model

node_events(): Transform individual events to node events for a model

nodes: Example data with spatial distribution of nodes

outdegree(): Determine out-degree for each node in a model

package_skeleton(): Create a package skeleton from a SimInf_model

pairs(<SimInf_model>): Scatterplot of number of individuals in each compartment

pfilter(): Bootstrap particle filter

plot(<SimInf_abc>): Display the ABC posterior distribution

plot(<SimInf_events>): Display the distribution of scheduled events over time

plot(<SimInf_individual_events>): Display the distribution of individual events over time

plot(<SimInf_pfilter>): Diagnostic plot of a particle filter object

plot(<SimInf_pmcmc>): Display the PMCMC posterior distribution

plot(<SimInf_model>): Display the outcome from a simulated trajectory

pmcmc(): Particle Markov chain Monte Carlo (PMCMC) algorithm

prevalence(<SimInf_model>): Calculate prevalence from a model object with trajectory data

prevalence(<SimInf_pfilter>): Extract prevalence from running a particle filter

prevalence(<SimInf_pmcmc>): Extract prevalence from fitting a PMCMC algorithm

prevalence(): Generic function to calculate prevalence from trajectory data

`punchcard<-`(): Set a template for where to record result during a simulation

run(): Run the SimInf stochastic simulation algorithm

`select_matrix<-`(): Set the select matrix for a SimInf_model object

select_matrix(): Extract the select matrix from a SimInf_model object

set_num_threads(): Specify the number of threads that SimInf should use

`shift_matrix<-`(): Set the shift matrix for a SimInf_model object

shift_matrix(): Extract the shift matrix from a SimInf_model object

show(<SimInf_abc>): Print summary of a SimInf_abc object

show(<SimInf_events>): Brief summary of SimInf_events

show(<SimInf_individual_events>): Print summary of a SimInf_individual_events object

show(<SimInf_model>): Brief summary of SimInf_model

show(<SimInf_pfilter>): Brief summary of a SimInf_pfilter object

show(<SimInf_pmcmc>): Brief summary of a SimInf_pmcmc object

summary(<SimInf_abc>): Detailed summary of a SimInf_abc object

summary(<SimInf_events>): Detailed summary of a SimInf_events object

summary(<SimInf_individual_events>): Detailed summary of a SimInf_individual_events object

summary(<SimInf_model>): Detailed summary of a SimInf_model object

summary(<SimInf_pfilter>): Detailed summary of a SimInf_pfilter object

summary(<SimInf_pmcmc>): Detailed summary of a SimInf_pmcmc object

trajectory(<SimInf_model>): Extract data from a simulated trajectory

trajectory(<SimInf_pfilter>): Extract filtered trajectory from running a particle filter

trajectory(<SimInf_pmcmc>): Extract filtered trajectories from fitting a PMCMC algorithm

trajectory(): Generic function to extract data from a simulated trajectory

`u0<-`(): Update the initial compartment state u0 in each node

u0(): Get the initial compartment state

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

`v0<-`(): Update the initial continuous state v0 in each node