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Type: Package
Title: Toolkits to Develop Individual-Based Models in Infectious Disease
Version: 1.0.0
Date: 2016-11-16
Description: It provides a generic set of tools for initializing a synthetic population with each individual in specific disease states, and making transitions between those disease states according to the rates calculated on each timestep. The new version 1.0.0 has C++ code integration to make the functions run faster. It has also a higher level function to actually run the transitions for the number of timesteps that users specify. Additional functions will follow for changing attributes on demographic, health belief and movement.
License: MIT + file LICENSE
LazyData: TRUE
RoxygenNote: 5.0.1
Suggests: testthat
LinkingTo: Rcpp
Imports: Rcpp
NeedsCompilation: yes
Packaged: 2016-11-16 09:19:06 UTC; Sai
Author: Sai Thein Than Tun [aut, cre]
Maintainer: Sai Thein Than Tun <theinthantun.sai@gmail.com>
Repository: CRAN
Date/Publication: 2016-11-16 10:51:54

Calculate cumulative probabilities for state transitions.

Description

This function takes in a vector of probabilities of states transitions and calculate the probability of staying in the original state and output the cumulative probabilities for all possibilities.

Usage

cumprob(probs, actual = FALSE)

Arguments

probs

A numeric vector of the probabilities of transition to states.

actual

A logical value, if TRUE, will calculate actual cumulative probabilities which may surpass 1!.

Value

A numeric vector of cumulative probabilites inclusive of the probability of having the same state in the next timestep.

Examples

cumprob(c(.2,.2,.9))
cumprob(c(.2,.2,.9), actual=TRUE)
cumprob(c(.2,.2,.2))


Miscellaneous functions to support the ibmcraftr packare are here.

Description

Miscellaneous functions to support the ibmcraftr packare are here.

Usage

rate2prob(rates)

Arguments

rates

A numeric scalar or vector to be transformed into rates.

Value

A numeric scalar or vector in terms of probabilities.

Examples

rate2prob(c(.1, .5))


Run state_trans function over a given number of timesteps.

Description

Organize population data and transition parameters to run state_trans function over the given number of timesteps.

Usage

run_state_trans(timesteps, param, pop, transient = "", useC = TRUE)

Arguments

timesteps

A numeric scalar based on which the state_trans function will run for that specific no. of timesteps and accumulate the results.

param

A list of lists. Each low-level list must contain transition parameters required by the state_trans function.

pop

A state matrix created from syn_pop function. This matrix represents the states of the population.

transient

A character vector. Each element must include formula(e)/expression(s) to evaluate dynamic parameters after each timestep.

useC

A logical value, which is TRUE by default, will run state_transition function written in RCPP, stRCPP.

Value

A summary matrix of the states all individuals in the population are in.

Examples

pop <- syn_pop(c(19,1,0,0,0)) #synthesizing population
b <- 2 #effective contact rate
param <- list(
list(1,c(2,5),c(NA,.1)), #transition from state 1 to 2 using FOI lambda
list(2,3,100), #transition from state 2 to 3,
list(3,4,100)  #the 3rd term ensures the transition to the next stage
)

timesteps <- 10
transient <- c("param[[1]][[3]][1] <- rate2prob(b*sum(pop[,2],pop[,3])/sum(pop))")
eval(parse(text=transient))

run_state_trans(timesteps, param, pop, transient)
run_state_trans(timesteps, param, pop, transient, useC = FALSE)


Make state transitions using Rcpp.

Description

Take in the matrix of the states of synthetic population (created by syn_pop function) and calculate the transitions from one state to other state(s) using the transition probabilities [not rate(s)]. The major difference from the R alone version was that instead of having the transition rate(s), transition probabilities are used. These probabilities will thus be calculated with another function.

Usage

stRCPP(origin, new.states, params, s.matrix)

Arguments

origin

A number which represents the column index s.matrix you want to do the transition from

new.states

A numeric vector or a number which represents the column index s.matrix you want as the destination(s) for the transition

params

A numeric vector of similar length to new.states which serves as the transition rate(s)

s.matrix

A state matrix created from syn_pop function

Value

A transition matrix of the same dimension as s.matrix. -1 indicates that the individual has left the corresponding state. +1 indicates that the individual has become the corresponding state.

Examples

pop <- syn_pop(c(19,1,0,0))
stRCPP(1,2,.1,pop)


Make state transitions.

Description

Take in the matrix of the states of synthetic population (created by syn_pop function) and calculate the transitions from one state to other state(s) using the transition rate(s).

Usage

state_trans(origin, new.states, params, s.matrix)

Arguments

origin

A number which represents the column index s.matrix you want to do the transition from

new.states

A numeric vector or a number which represents the column index s.matrix you want as the destination(s) for the transition

params

A numeric vector of similar length to new.states which serves as the transition rate(s)

s.matrix

A state matrix created from syn_pop function

Value

A transition matrix of the same dimension as s.matrix. -1 indicates that the individual has left the corresponding state. +1 indicates that the individual has become the corresponding state.

Examples

pop <- syn_pop(c(19,1,0,0))
state_trans(1,2,.1,pop)
state_trans(1,4,100,pop)


Create a synthetic population having several states.

Description

Populate a matrix in which columns represent the states of the individuals and rows represent the individuals.

Usage

syn_pop(states, shuffle = FALSE)

Arguments

states

A numeric vector with each element representing the number of individuals in a particular state its index corresponds to.

shuffle

A logical value to enable shuffling of the individuals (rows) in the resulting matrix.

Value

A matrix of 0s, and 1s. The rows representing the individuals and the columns representing the states the individuals are in

Examples

syn_pop(c(3,2,1))
syn_pop(c(0,0,1,5), shuffle=TRUE)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.