arlCusum

distribution parameter for the cumulative distribution function
               (cdf) $F$, i.e. rate
               $\lambda$ for Poisson variates or probability $p$
               for binomial variates

theta

<code>"poisson"</code> or <code>"binomial"</code>

distr

Winsorizing value <code>W</code> for a robust CUSUM,
          to get a nonrobust CUSUM set          <code>W</code> > <code>k</code>+<code>h</code>. If <code>NULL</code>, a nonrobust CUSUM is used.

<code>k</code> and <code>h</code> are rounded to <code>digits</code> decimal places

digits

further arguments for the distribution function, i.e. number of trials <code>n</code>
                for binomial cdf

Calculates the average run length (ARL) for an upward CUSUM scheme for discrete
        distributions (i.e. Poisson and binomial) using the Markov chain approach.

models

Statistical methods for the modeling and monitoring of time series
        of counts, proportions and categorical data, as well as for the modeling
        of continuous-time point processes of epidemic phenomena.
        The monitoring methods focus on aberration detection in count data time
        series from public health surveillance of communicable diseases, but
        applications could just as well originate from environmetrics,
        reliability engineering, econometrics, or social sciences. The package
        implements many typical outbreak detection procedures such as the
        (improved) Farrington algorithm, or the negative binomial GLR-CUSUM
        method of H�hle and Paul (2008) <doi:10.1016/j.csda.2008.02.015>.
        A novel CUSUM approach combining logistic and multinomial logistic
        modeling is also included. The package contains several real-world data
        sets, the ability to simulate outbreak data, and to visualize the
        results of the monitoring in a temporal, spatial or spatio-temporal
        fashion. A recent overview of the available monitoring procedures is
        given by Salmon et al. (2016) <doi:10.18637/jss.v070.i10>.
        For the retrospective analysis of epidemic spread, the package provides
        three endemic-epidemic modeling frameworks with tools for visualization,
        likelihood inference, and simulation. 'hhh4' estimates models for
        (multivariate) count time series following Paul and Held (2011)
        <doi:10.1002/sim.4177> and Meyer and Held (2014) <doi:10.1214/14-AOAS743>.
        'twinSIR' models the susceptible-infectious-recovered (SIR) event
        history of a fixed population, e.g, epidemics across farms or networks,
        as a multivariate point process as proposed by H�hle (2009)
        <doi:10.1002/bimj.200900050>. 'twinstim' estimates self-exciting point
        process models for a spatio-temporal point pattern of infective events,
        e.g., time-stamped geo-referenced surveillance data, as proposed by
        Meyer et al. (2012) <doi:10.1111/j.1541-0420.2011.01684.x>.
        A recent overview of the implemented space-time modeling frameworks
        for epidemic phenomena is given by Meyer et al. (2015)
        <http://arxiv.org/abs/1411.0416>.

Sebastian Meyer

surveillance

Temporal and Spatio-Temporal Modeling and Monitoring of Epidemic
        Phenomena

arlCusum function

Based on the FORTRAN code of<p></p><p>Hawkins, D. M. (1992). Evaluation of Average Run Lengths of Cumulative
  Sum Charts for an Arbitrary Data Distribution. Communications in
  Statistics - Simulation and Computation, 21(4), p. 1001-1020.</p>

arlCusum: Calculation of Average Run Length for discrete CUSUM schemes

Description

Usage

Arguments

Value

source