rdirichlet

Generates cell probabilities from the Dirichlet distribution.

Performs drug demand forecasting by modeling drug dispensing data while taking into account predicted enrollment and treatment discontinuation dates. The gap time between randomization and the first drug dispensing visit is modeled using interval-censored exponential, Weibull, log-logistic, or log-normal distributions (Anderson-Bergman (2017) <doi:10.18637/jss.v081.i12>). The number of skipped visits is modeled using Poisson, zero-inflated Poisson, or negative binomial distributions (Zeileis, Kleiber & Jackman (2008) <doi:10.18637/jss.v027.i08>). The gap time between two consecutive drug dispensing visits given the number of skipped visits is modeled using linear regression based on least squares or least absolute deviations (Birkes & Dodge (1993, ISBN:0-471-56881-3)). The number of dispensed doses is modeled using linear or linear mixed-effects models (McCulloch & Searle (2001, ISBN:0-471-19364-X)).

Kaifeng Lu

drugDemand

Drug Demand Forecasting

rdirichlet function

<dl><dt>n</dt>
<dd>The number of observations.</dd>
<dt>alpha</dt>
<dd>The shape parameters of the Dirichlet distribution.</dd></dl>

Arguments

Kaifeng Lu, <a href="/link/kaifenglu%40gmail.com?package=drugDemand&version=0.1.3" data-mini-rdoc="drugDemand::kaifenglu@gmail.com">kaifenglu@gmail.com</a>

Author

Random Number Generator for the Dirichlet Distribution — rdirichlet

<dl>

<dt>n</dt>
<dd>The number of observations.</dd>


<dt>alpha</dt>
<dd>The shape parameters of the Dirichlet distribution.</dd>

</dl>

Kaifeng Lu, <a href='mailto:kaifenglu@gmail.com'>kaifenglu@gmail.com</a>

rdirichlet: Random Number Generator for the Dirichlet Distribution

Description

Usage

Value

Arguments

Author

Examples