# dCount_allProbs_bi: Compute count probabilities using simple convolution In Countr: Flexible Univariate Count Models Based on Renewal Processes

## Description

Compute count probabilities using simple convolution (section 2) for the built-in distributions

Compute count probabilities using simple convolution (section 2) for user passed survival functions

## Usage

 ```1 2 3 4 5``` ```dCount_allProbs_bi(x, distPars, dist, nsteps = 100L, time = 1, extrap = TRUE, logFlag = FALSE) dCount_allProbs_user(x, distPars, extrapolPars, survR, nsteps = 100L, time = 1, extrap = TRUE, logFlag = FALSE) ```

## Arguments

 `x` integer (vector), the desired count values. `distPars` `Rcpp::List` with distribution specific slots, see details. `dist` character name of the built-in distribution, see details. `nsteps` unsiged integer number of steps used to compute the integral. `time` double time at wich to compute the probabilities. Set to 1 by default. `extrap` logical if `TRUE`, Richardson extrapolation will be applied to improve accuracy. `logFlag` logical if `TRUE` the log-probability will be returned. `extrapolPars` ma::vec of length 2. The extrapolation values. `survR` Rcpp::Function user passed survival function; should have the signature `function(t, distPars)` where `t` is a real number (>0) where the survival function is evaluated and `distPars` is a list of distribution parameters. It should return a double value.

## Details

The routine does convolutions to produce probabilities `probs(0)`, ... `probs(xmax)` using `nsteps` steps, and refines result by Richardson extrapolation if `extrap` is `TRUE` using the algorithm of section 2.

## Value

vector of probabilities P(x(i)) for i = 1, ..., n where n is `length` of `x`.

Countr documentation built on Nov. 21, 2017, 1:04 a.m.