zetaweights

numeric, symmetric matrix of neighbourhood orders.

nbmat

single numeric decay parameter (default: 1). Should be
  positive.

single numeric specifying an upper limit for the power
    law. For neighbourhood orders > <code>maxlag</code>, the resulting weight
    is 0. Defaults to no truncation.

maxlag

Should the resulting weight matrix be normalized such
    that rows sum to 1?

normalize

Compute power-law weights with decay parameter <code>d</code>
  based on a matrix of neighbourhood orders <code>nbmat</code>
  (e.g., as obtained via <code><a href="/link/nbOrder?package=surveillance&version=1.12.1" rd-options="" data-mini-rdoc="surveillance::nbOrder">nbOrder</a></code>).
  Without normalization and truncation,
  this is just $o^{-d}$ (where $o$ is a neighbourhood order).
  This function is mainly used internally for <code><a href="/link/W_powerlaw?package=surveillance&version=1.12.1" rd-options="" data-mini-rdoc="surveillance::W_powerlaw">W_powerlaw</a></code>
  weights in <code><a href="/link/hhh4?package=surveillance&version=1.12.1" rd-options="" data-mini-rdoc="surveillance::hhh4">hhh4</a></code> models.

utilities

spatial

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>.

zetaweights: Power-Law Weights According to Neighbourhood Order

Description

Usage

Arguments

Value

See Also

Examples