intersperse

an object inheriting from class <code>"epidata"</code>.

epidata

a numeric vector of time points inside the observation period of the
    <code>epidata</code>.

stoptimes

logical indicating if a <code><a href="/link/txtProgressBar?package=surveillance&version=1.12.1" rd-options="" data-mini-rdoc="surveillance::txtProgressBar">txtProgressBar</a></code> should be shown
    while inserting blocks for extra <code>stoptimes</code>.

verbose

This function modifies an object inheriting from class <code>"epidata"</code> such 
  that it features the specified stop time points.  For this purpose, the time 
  interval in the event history into which the new stop falls will be split 
  up into two parts, one block for the time period until the new stop -- where 
  no infection or removal occurs -- and the other block for the time period 
  from the new stop to the end of the original interval.Main application is to enable the use of <code>knots</code> in <code>twinSIR</code>, which
  are not existing stop time points in the <code>"epidata"</code> object.

manip

spatial

epidata_intersperse

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>.

epidata_intersperse: Impute Blocks for Extra Stops in `"epidata"` Objects

Description

Usage

Arguments

Value

See Also

Examples