iMoMo: iMoMo.
In amvillegas/iMoMo: Mortality Improvement Rate Modelling

Initialises an iMoMo oject which represents a generalised Age-Period-Cohort improvement rate mortality model

iMoMo(
  staticAgeFun = TRUE,
  periodAgeFun = "NP",
  cohortAgeFun = NULL,
  type = c("indirect", "direct"),
  constFun = function(ax, bx, kt, b0x, gc, wxt, ages) list(ax = ax, bx = bx, kt = kt,
    b0x = b0x, gc = gc),
  constFunEst = function(Ax, ax, bx, kt, b0x, gc, wxt, ages) list(Ax = Ax, ax = ax, bx
    = bx, kt = kt, b0x = b0x, gc = gc)
)

`staticAgeFun`	logical value indicating if a static age function α_x is to be included.
`periodAgeFun`	a list of length N with the definitions of the period age modulating parameters β_x^{(i)}. Each entry can take values: `"NP"` for non-parametric age terms, `"1"` for β_x^{(i)}=1 or a predefined parametric function of age (see details). Set this to `NULL` if there are no period terms in the model.
`cohortAgeFun`	defines the cohort age modulating parameter β_x^{(0)}. It can take values: `"NP"` for non-parametric age terms, `"1"` for β_x^{(0)}=1, a predefined parametric function of age (see details) or `NULL` if there is no cohort effect.
`type`	defines the type of estimation method to be used. `"indirect"` would estimate the equivalent mortality rate model and the transform the model into improvement rates. `"direct"` would estimate the model directly on the improvement rate model.
`constFun`	function defining the identifiability constraints of the model. It must be a function of the form `constFun <- function(ax, bx, kt, b0x, gc, wxt, ages)` taking a set of fitted model parameters and returning a list `list(ax = ax, bx = bx, kt = kt, b0x = b0x, gc = gc)` of the model parameters with the identifiability constraints applied. If omitted no identifiability constraints are applied to the model.
`constFunEst`	function defining the identifiability constraints for the equivalent mortality rate model. It must be a function of the form `constFunEst <- function(Ax, ax, bx, kt, b0x, gc, wxt, ages)` taking a set of fitted model parameters and returning a list `list(Ax = Ax, ax = ax, bx = bx, kt = kt, b0x = b0x, gc = gc)` of the model parameters with the identifiability constraints applied. If omitted no identifiability constraints are applied to the estimaion model.