algo.farrington.threshold

pred

Quantile level in Gaussian based CI, i.e. an $(1-\alpha)%$
    confidence interval is computed.

alpha

Skewness correction, i.e. one of
 <code>"none"</code>, <code>"1/2"</code>, or <code>"2/3"</code>.

skewness.transform

Depending on the current transformation $h(y)= {y, \sqrt{y}, y^{2/3}}$, $$V(h(y_0)-h(\mu_0))=V(h(y_0))+V(h(\mu_0))$$ is used to compute a prediction interval. The prediction variance
 consists of a component due to the variance of having a single
 observation and a prediction variance.

regression

A package implementing statistical methods for the
        modeling and change-point detection in time series of counts,
        proportions and categorical data. Focus is on outbreak
        detection in count data time series originating from public
        health surveillance of infectious diseases, but applications
        could just as well originate from environmetrics, reliability
        engineering, econometrics or social sciences.  Currently the
        package contains implementations typical outbreak detection
        procedures such as Stroup et. al (1989), Farrington et al,
        (1996), Rossi et al. (1999), Rogerson and Yamada (2001), a
        Bayesian approach, negative binomial CUSUM methods and a
        detector based on generalized likelihood ratios. Furthermore,
        inference methods for the retrospective infectious disease
        model in Held et al. (2005), Held et al. (2006) and Paul et al.
        (2008) are provided. A novel CUSUM approach combining logistic
        and multinomial logistic modelling is also included. The
        package contains several real-world datasets, the ability to
        simulate outbreak data, visualize the results of the monitoring
        in temporal, spatial or spatio-temporal fashion.

algo.farrington.threshold: Threshold computations using a two sided confidence interval

Description

Usage

Arguments

Value

encoding