Get a IP generating function from time varying mean and SD of a gamma function

Usage

cfg_gamma_ip_fn(mean_fn = ~2, sd_fn = function(mean) sqrt(mean))

Arguments

mean_fn: a function which gives the time-varying mean of a gamma distribution, The function will be called minimally with .x or t which will be the time as a time period. Other variables may be present.
sd_fn: a function which gives the time-varying mean of a gamma distribution. The function will be called with minimally .x or t which will be the time period and .y or mean will be the mean. Other variables may be present.

Value

a time dependent function that inputs a time (as a time_period) and returns an ip delay distribution for each day defined by the mean_fn and sd_fn

Examples

fn = cfg_gamma_ip_fn(mean_fn = \(t) ifelse(t < 5, 4, 2))
# a gamma function that changes mean at time 5
fn(4)
#> [[1]]
#> # A tibble: 15 × 5
#> # Groups:   boot [1]
#>      tau    a0    a1 probability  boot
#>    <int> <dbl> <dbl>       <dbl> <int>
#>  1     0   0     0.5    0.00175      1
#>  2     1   0.5   1.5    0.0639       1
#>  3     2   1.5   2.5    0.177        1
#>  4     3   2.5   3.5    0.221        1
#>  5     4   3.5   4.5    0.194        1
#>  6     5   4.5   5.5    0.141        1
#>  7     6   5.5   6.5    0.0898       1
#>  8     7   6.5   7.5    0.0527       1
#>  9     8   7.5   8.5    0.0290       1
#> 10     9   8.5   9.5    0.0152       1
#> 11    10   9.5  10.5    0.00771      1
#> 12    11  10.5  11.5    0.00378      1
#> 13    12  11.5  12.5    0.00181      1
#> 14    13  12.5  13.5    0.000848     1
#> 15    14  13.5  14.5    0.000707     1
#> 
fn(7)
#> [[1]]
#> # A tibble: 11 × 5
#> # Groups:   boot [1]
#>      tau    a0    a1 probability  boot
#>    <int> <dbl> <dbl>       <dbl> <int>
#>  1     0   0     0.5    0.0902       1
#>  2     1   0.5   1.5    0.352        1
#>  3     2   1.5   2.5    0.271        1
#>  4     3   2.5   3.5    0.151        1
#>  5     4   3.5   4.5    0.0748       1
#>  6     5   4.5   5.5    0.0345       1
#>  7     6   5.5   6.5    0.0153       1
#>  8     7   6.5   7.5    0.00657      1
#>  9     8   7.5   8.5    0.00277      1
#> 10     9   8.5   9.5    0.00115      1
#> 11    10   9.5  10.5    0.000786     1
#>