Nothing

```
#' Log-likelihood of a count probability computed by convolution (bi)
#'
#' Compute the log-likelihood of a count model using convolution
#' methods to compute the probabilities.
#' \code{dCount_conv_loglik_bi} is for the builtin distributions.
#' \code{dCount_conv_loglik_user} is for user defined survival functions.
#'
#' @param distPars list of the same length as x with each slot being itself a
#' named list containing the distribution parameters corresponding to
#' \code{x[i]}.
#' @param method character, convolution method to be used; choices are
#' \code{"dePril"} (section 3.2), \code{"direct"} (section 2) or
#' \code{"naive"} (section 3.1).
#' @inheritParams dCount_allProbs_bi
#' @param na.rm logical, if TRUE, \code{NA}s (produced by taking the log of
#' very small probabilities) will be replaced by the smallest allowed
#' probability; default is \code{TRUE}.
#' @param weights numeric, vector of weights to apply. If \code{NULL}, a vector
#' of ones.
#' @return numeric, the log-likelihood of the count process
#' @examples
#' x <- 0:10
#' lambda <- 2.56
#' distPars <- list(scale = lambda, shape = 1)
#' distParsList <- lapply(seq(along = x), function(ind) distPars)
#' extrapolParsList <- lapply(seq(along = x), function(ind) c(2, 1))
#' ## user pwei
#' pwei_user <- function(tt, distP) {
#' alpha <- exp(-log(distP[["scale"]]) / distP[["shape"]])
#' pweibull(q = tt, scale = alpha, shape = distP[["shape"]],
#' lower.tail = FALSE)
#' }
#'
#' ## log-likehood allProbs Poisson
#' dCount_conv_loglik_bi(x, distParsList,
#' "weibull", "direct", nsteps = 400)
#'
#' dCount_conv_loglik_user(x, distParsList, extrapolParsList,
#' pwei_user, "direct", nsteps = 400)
#'
#' ## log-likehood naive Poisson
#' dCount_conv_loglik_bi(x, distParsList,
#' "weibull", "naive", nsteps = 400)
#'
#' dCount_conv_loglik_user(x, distParsList, extrapolParsList,
#' pwei_user, "naive", nsteps = 400)
#'
#' ## log-likehood dePril Poisson
#' dCount_conv_loglik_bi(x, distParsList,
#' "weibull", "dePril", nsteps = 400)
#'
#' dCount_conv_loglik_user(x, distParsList, extrapolParsList,
#' pwei_user, "dePril", nsteps = 400)
#' @export
dCount_conv_loglik_bi <- function(x, distPars,
dist = c("weibull", "gamma", "gengamma",
"burr"),
method = c( "dePril", "direct", "naive"),
nsteps = 100,
time = 1.0, extrap = TRUE,
na.rm = TRUE, weights = NULL) {
dist <- match.arg(dist)
method <- match.arg(method)
if (is.null(weights))
weights <- rep(1, length(x))
pbs <- dCount_conv_bi(x, distPars, dist, method, nsteps = nsteps,
time = time, extrap = extrap, log = TRUE)
pbs <- pbs * weights
if (na.rm)
pbs[is.na(pbs)] <- .logNaReplace()
sum(pbs)
}
## Log-likelihood of a count probability computed by convolution (user)
##log-likelihood of a count probability computed by convolution methods for user
##passed survival functions.
#' @param extrapolPars list of same length as x where each slot is a vector of
#' length 2 (the extrapolation values to be used) corresponding to
#' \code{x[i]}.
#' @param survR a user defined survival function; should have the signature
#' \code{function(t, distPars)} where \code{t} is a real number (>0) where
#' the survival function is evaluated and \code{distPars} is a list of
#' distribution parameters. It should return a double value.
#' @inheritParams dCount_conv_loglik_bi
## @return double, the log-likelihood of the count process
#' @examples
#' ## see dCount_conv_loglik_bi()
#' @rdname dCount_conv_loglik_bi
#' @export
dCount_conv_loglik_user <- function(x, distPars, extrapolPars, survR,
method = c( "dePril", "direct", "naive"),
nsteps = 100, time = 1.0, extrap = TRUE,
na.rm = TRUE, weights = NULL) {
if (is.null(weights))
weights <- rep(1, length(x))
method <- match.arg(method)
pbs <- dCount_conv_user(x, distPars, extrapolPars, survR,
method = method,
nsteps = nsteps, time = time,
extrap = extrap, log = TRUE)
pbs <- pbs * weights
if (na.rm)
pbs[is.na(pbs)] <- .logNaReplace()
sum(pbs)
}
```

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