Use multinomial function to automatically fill holes in age data

Function to compute proportions-at-age for each length using multinomial approach From Chris Legault (which was based on code from Mike Bednarski)

get_multinomial_props(
  agedata,
  small.len = 1,
  big.len,
  small.age,
  big.age,
  ref.age,
  printConvergence = F
)

Arguments

small.len: Numeric scalar. Smallest length to be estimated (Usually based on observations for the year)
big.len: Numeric scalar. Largest length to be estimated (Usually based on observations for the year)
small.age: Numeric scalar.
big.age: Numeric scalar. Maximum age needed in the key (Usually based on observations for the year)
ref.age: Numeric scalar. Used in the GLM as the age against which all the other ages are estimated. It is typically the most abundant age.
ageData: Data frame

List