Deconvolve the incidence input with the Richardson-Lucy (R-L) algorithm

Deconvolve the incidence input with the Richardson-Lucy (R-L) algorithm

.deconvolve_incidence_Richardson_Lucy(
  incidence_input,
  delay_distribution,
  threshold_chi_squared = 1,
  max_iterations = 100,
  verbose = FALSE
)

Arguments

incidence_input

Module input object. List with two elements:

  1. A numeric vector named values: the incidence recorded on consecutive time steps.

  2. An integer named index_offset: the offset, counted in number of time steps, by which the first value in values is shifted compared to a reference time step This parameter allows one to keep track of the date of the first value in values without needing to carry a date column around. A positive offset means values are delayed in the future compared to the reference values. A negative offset means the opposite.
delay_distribution

numeric square matrix or vector.
threshold_chi_squared

numeric scalar. Threshold for chi-squared values under which the R-L algorithm stops.
max_iterations

integer. Maximum threshold for the number of iterations in the R-L algorithm.
verbose

Boolean. Print verbose output?

Value

A list with two elements:

  1. A numeric vector named values: the result of the computations on the input data.

  2. An integer named index_offset: the offset, counted in number of time steps, by which the result is shifted compared to an index_offset of 0. This parameter allows one to keep track of the date of the first value in values without needing to carry a date column around. A positive offset means values are delayed in the future compared to the reference values. A negative offset means the opposite. Note that the index_offset of the output of the function call accounts for the (optional) index_offset of the input.

If index_offset is 0 and simplify_output = TRUE, the index_offset is dropped and the values element is returned as a numeric vector.