Package 'denim'

Title: Generate and Simulate Deterministic Discrete-Time Compartmental Models
Description: R package to build and simulate deterministic discrete-time compartmental models that can be non-Markov. Length of stay in each compartment can be defined to follow a parametric distribution (d_exponential(), d_gamma(), d_weibull(), d_lognormal()) or a non-parametric distribution (nonparametric()). Other supported types of transition from one compartment to another includes fixed transition (constant()), multinomial (multinomial()), fixed transition probability (transprob()).
Authors: Thinh Ong [aut, cph] , Anh Phan [aut, cre], Marc Choisy [aut] , Niels Lohman [ctb], Bjoern Hoehrmann [ctb], Florian Loitsch [ctb], Ingo Berg [ctb]
Maintainer: Anh Phan <[email protected]>
License: MIT + file LICENSE
Version: 1.0.0.9000
Built: 2024-10-08 05:19:38 UTC
Source: https://github.com/thinhong/denim

Help Index

denim

Description

Simulate deterministic discrete time model

Details

Imports

Author(s)

Maintainer: Anh Phan [email protected]

Authors:

Other contributors:

  • Niels Lohman [contributor]

  • Bjoern Hoehrmann [email protected] [contributor]

  • Florian Loitsch [contributor]

  • Ingo Berg [contributor]

See Also

Useful links:

Fixed transition

Description

Define a fixed number of individuals of the left compartment transit to the right compartment at every time step

Usage

constant(x)

Arguments

x

number of individuals who move from one compartment to another

Value

a Distribution object for simulator

Examples

transitions <- list("S->I" = constant(10))

Discrete exponential distribution

Description

Discrete exponential distribution

Usage

d_exponential(rate)

Arguments

rate

rate parameter of an exponential distribution

Value

a Distribution object for simulator

Examples

transitions <- list("I -> D" = d_exponential(0.3))

Discrete gamma distribution

Description

Discrete gamma distribution

Usage

d_gamma(scale, shape)

Arguments

scale

scale parameter of a gamma distribution
shape

shape parameter of a gamma distribution

Value

a Distribution object for simulator

Examples

transitions <- list("S -> I" = d_gamma(1, 5))

Discrete log-normal distribution

Description

Discrete log-normal distribution

Usage

d_lognormal(mu, sigma)

Arguments

mu

location parameter or the ln mean
sigma

scale parameter or ln standard deviation

Value

a Distribution object for simulator

Examples

transitions <- list("I -> D" = d_lognormal(3, 0.6))

Discrete Weibull distribution

Description

Discrete Weibull distribution

Usage

d_weibull(scale, shape)

Arguments

scale

scale parameter of a Weibull distribution
shape

shape parameter of a Weibull distribution

Value

a Distribution object for simulator

Examples

transitions <- list("I -> D" = d_weibull(0.6, 2))

Mathematical expression

Description

Mathematical expression

Usage

mathexpr(expr)

Arguments

expr

User defined mathematial expression. he expression will be processed by muparser library which offers a wide variety of operators. Visit muparser website (https://beltoforion.de/en/muparser/features.php) to see full list of available operators.

Value

a Distribution object for simulator

Examples

transitions <- list("S->I"=mathexpr("beta*S/N"))
# definition for parameters in the expression required
params <- c(N = 1000, beta = 0.3)

Multinomial

Description

Define a set of probabilities of transition from one compartment to multiple compartments 

"I -> R, D" = multinomial(0.9, 0.1),
"I -> R" = d_gamma(3, 2),
"I -> D" = d_lognormal(2, 0.5)

is equal to 

"0.9 * I -> R" = d_gamma(3, 2),
"0.1 * I -> D" = d_lognormal(2, 0.5)

Usage

multinomial(...)

Arguments

...

a vector of probabilities, must add up to 1

Value

a Distribution object for simulator

Nonparametric distribution

Description

Convert a vector of frequencies, percentages... into a distribution

Usage

nonparametric(...)

Arguments

...

a vector of values

Value

a Distribution object for simulator

Examples

transitions <- list("S->I"=nonparametric(0.1, 0.2, 0.5, 0.2))

Simulator for deterministic discrete time model with memory

Description

Simulation function that call the C++ simulator

Usage

sim(
  transitions,
  initialValues,
  parameters = NULL,
  simulationDuration,
  timeStep = 1,
  errorTolerance = 0.001
)

Arguments

transitions

a list of transitions follows this format "transition" = distribution()
initialValues

a vector contains the initial values of all compartments defined in the transitions, follows this format compartment_name = initial_value
parameters

a vector contains values of any parameters that are not compartments, usually parameters used in mathexp() functions
simulationDuration

duration of time to be simulate
timeStep

set the output time interval. For example, if simulationDuration = 10 means 10 days and timeStep = 0.1, the output will display results for each 0.1 daily interval
errorTolerance

set the threshold so that a cumulative distribution function can be rounded to 1. For example, if we want a cumulative probability of 0.999 to be rounded as 1, we set errorTolerance = 0.001 (1 - 0.999 = 0.001). Default is 0.001

Value

a data.frame with class denim that can be plotted with a plot() method

Examples

transitions <- list(
   "S -> I" = "beta * S * I / N",
   "I -> R" = d_gamma(3, 2)
)

initialValues <- c(
   S = 999, 
   I = 1, 
   R = 0
)

parameters <- c(
   beta = 0.012,
   N = 1000
)

simulationDuration <- 30
timeStep <- 0.01

mod <- sim(transitions = transitions, 
           initialValues = initialValues, 
           parameters = parameters, 
           simulationDuration = simulationDuration, 
           timeStep = timeStep)

Transition probability

Description

A fixed percentage of the left compartment transit to the right compartment at every time step

Usage

transprob(x)

Arguments

x

a float number between 0 to 1

Value

a Distribution object for simulator

Examples

transitions <- list("S->I"=transprob(0.8))