tag:crantastic.org,2005:/authors/4604Latest activity for Michael Hhle2017-03-13T18:01:31Zcrantastic.orgtag:crantastic.org,2005:TimelineEvent/946852019-11-12T07:04:17Z2019-11-12T07:04:17Zsurveillance was upgraded to version 1.17.2<a href="/packages/surveillance">surveillance</a> was <span class="action">upgraded</span> to version <a href="/packages/surveillance/versions/90006">1.17.2</a><br /><h3>Package description:</h3><p>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 Hhle 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 Hhle (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. (2017) <doi:10.18637/jss.v077.i11>.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/926532019-09-13T15:23:43Z2019-09-13T15:23:43Zsurveillance was upgraded to version 1.17.1<a href="/packages/surveillance">surveillance</a> was <span class="action">upgraded</span> to version <a href="/packages/surveillance/versions/88111">1.17.1</a><br /><h3>Package description:</h3><p>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 Hhle 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 Hhle (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. (2017) <doi:10.18637/jss.v077.i11>.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/850762019-02-22T14:43:20Z2019-02-22T14:43:20Zsurveillance was upgraded to version 1.17.0<a href="/packages/surveillance">surveillance</a> was <span class="action">upgraded</span> to version <a href="/packages/surveillance/versions/80883">1.17.0</a><br /><h3>Package description:</h3><p>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 Hhle 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 Hhle (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. (2017) <doi:10.18637/jss.v077.i11>.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/783542018-08-10T16:59:46Z2018-08-10T16:59:46Zsurveillance was upgraded to version 1.16.2<a href="/packages/surveillance">surveillance</a> was <span class="action">upgraded</span> to version <a href="/packages/surveillance/versions/74728">1.16.2</a><br /><h3>Package description:</h3><p>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 Hhle 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 Hhle (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. (2017) <doi:10.18637/jss.v077.i11>.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/756542018-05-28T22:22:59Z2018-05-28T22:22:59Zsurveillance was upgraded to version 1.16.1<a href="/packages/surveillance">surveillance</a> was <span class="action">upgraded</span> to version <a href="/packages/surveillance/versions/72213">1.16.1</a><br /><h3>Package description:</h3><p>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 Hhle 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 Hhle (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. (2017) <doi:10.18637/jss.v077.i11>.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/707432018-01-23T22:03:10Z2018-01-23T22:03:10Zsurveillance was upgraded to version 1.16.0<a href="/packages/surveillance">surveillance</a> was <span class="action">upgraded</span> to version <a href="/packages/surveillance/versions/67570">1.16.0</a><br /><h3>Package description:</h3><p>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 Hhle 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 Hhle (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. (2017) <doi:10.18637/jss.v077.i11>.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/671202017-10-06T21:02:27Z2017-10-06T21:02:27Zsurveillance was upgraded to version 1.15.0<a href="/packages/surveillance">surveillance</a> was <span class="action">upgraded</span> to version <a href="/packages/surveillance/versions/64202">1.15.0</a><br /><h3>Package description:</h3><p>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 Hhle 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 Hhle (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. (2017) <doi:10.18637/jss.v077.i11>.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/640152017-06-29T22:22:14Z2017-06-29T22:22:14Zsurveillance was upgraded to version 1.14.0<a href="/packages/surveillance">surveillance</a> was <span class="action">upgraded</span> to version <a href="/packages/surveillance/versions/61247">1.14.0</a><br /><h3>Package description:</h3><p>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 Hhle 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 Hhle (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. (2017) <doi:10.18637/jss.v077.i11>.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/619942017-04-28T22:42:33Z2017-04-28T22:42:33Zsurveillance was upgraded to version 1.13.1<a href="/packages/surveillance">surveillance</a> was <span class="action">upgraded</span> to version <a href="/packages/surveillance/versions/59288">1.13.1</a><br /><h3>Package description:</h3><p>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 Hhle 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 Hhle (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. (2017) <doi:10.18637/jss.v077.i11>.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/597052017-03-13T18:01:31Z2017-03-13T18:01:31ZbinomSamSize was upgraded to version 0.1-5<a href="/packages/binomSamSize">binomSamSize</a> was <span class="action">upgraded</span> to version <a href="/packages/binomSamSize/versions/57140">0.1-5</a><br /><h3>Package description:</h3><p>A suite of functions to compute confidence intervals and necessary sample sizes for the parameter p of the Bernoulli B(p) distribution under simple random sampling or under pooled sampling. Such computations are e.g. of interest when investigating the incidence or prevalence in populations. The package contains functions to compute coverage probabilities and coverage coefficients of the provided confidence intervals procedures. Sample size calculations are based on expected length.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/583592017-01-09T16:51:20Z2017-01-09T16:51:20Zsurveillance was upgraded to version 1.13.0<a href="/packages/surveillance">surveillance</a> was <span class="action">upgraded</span> to version <a href="/packages/surveillance/versions/55881">1.13.0</a><br /><h3>Package description:</h3><p>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 Hhle 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 Hhle (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>.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/573852016-12-05T07:40:18Z2016-12-05T07:40:18ZbinomSamSize was upgraded to version 0.1-4<a href="/packages/binomSamSize">binomSamSize</a> was <span class="action">upgraded</span> to version <a href="/packages/binomSamSize/versions/54936">0.1-4</a><br /><h3>Package description:</h3><p>A suite of functions to compute confidence intervals and necessary sample sizes for the parameter p of the Bernoulli B(p) distribution under simple random sampling or under pooled sampling. Such computations are e.g. of interest when investigating the incidence or prevalence in populations. The package contains functions to compute coverage probabilities and coverage coefficients of the provided confidence intervals procedures. Sample size calculations are based on expected length.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/568452016-11-17T13:41:50Z2016-11-17T13:41:50Zsurveillance was upgraded to version 1.12.2<a href="/packages/surveillance">surveillance</a> was <span class="action">upgraded</span> to version <a href="/packages/surveillance/versions/54470">1.12.2</a><br /><h3>Package description:</h3><p>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 Hhle 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 Hhle (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>.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/513092016-05-18T05:01:38Z2016-05-18T05:01:38Zsurveillance was upgraded to version 1.12.1<a href="/packages/surveillance">surveillance</a> was <span class="action">upgraded</span> to version <a href="/packages/surveillance/versions/49573">1.12.1</a><br /><h3>Package description:</h3><p>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 Hhle 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 Hhle (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>.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/500622016-04-03T15:02:07Z2016-04-03T15:02:07Zsurveillance was upgraded to version 1.12.0<a href="/packages/surveillance">surveillance</a> was <span class="action">upgraded</span> to version <a href="/packages/surveillance/versions/48420">1.12.0</a><br /><h3>Package description:</h3><p>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 Hhle 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. (2014) <http://arxiv.org/abs/1411.1292>. 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 Hhle (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>.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/492922016-03-12T00:15:32Z2016-03-12T00:15:32Zcrantastic_production tagged surveillance with TimeSeries<a href="/users/146">crantastic_production</a> <span class="action">tagged</span> <a href="/packages/surveillance">surveillance</a> with <a href="/task_views/TimeSeries">TimeSeries</a>crantastic_productiontag:crantastic.org,2005:TimelineEvent/491092016-03-12T00:15:28Z2016-03-12T00:15:28Zcrantastic_production tagged surveillance with SpatioTemporal<a href="/users/146">crantastic_production</a> <span class="action">tagged</span> <a href="/packages/surveillance">surveillance</a> with <a href="/task_views/SpatioTemporal">SpatioTemporal</a>crantastic_productiontag:crantastic.org,2005:TimelineEvent/471682016-02-09T08:52:56Z2016-02-09T08:52:56Zsurveillance was upgraded to version 1.11.0<a href="/packages/surveillance">surveillance</a> was <span class="action">upgraded</span> to version <a href="/packages/surveillance/versions/46927">1.11.0</a><br /><h3>Package description:</h3><p>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 Hhle 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. (2014) <http://arxiv.org/abs/1411.1292>. 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 Hhle (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>.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/449372015-11-06T15:12:25Z2015-11-06T15:12:25Zsurveillance was upgraded to version 1.10-0<a href="/packages/surveillance">surveillance</a> was <span class="action">upgraded</span> to version <a href="/packages/surveillance/versions/44714">1.10-0</a><br /><h3>Package description:</h3><p>Implementation of statistical methods for the modeling and change-point detection in time series of counts, proportions and categorical data, as well as for the modeling of continuous-time epidemic phenomena, e.g., discrete-space setups such as the spatially enriched Susceptible-Exposed-Infectious-Recovered (SEIR) models, or continuous-space point process data such as the occurrence of infectious diseases. Main focus is on outbreak detection in count data time series originating from public health surveillance of communicable diseases, but applications could just as well originate from environmetrics, reliability engineering, econometrics or social sciences. Currently, the package contains implementations of many typical outbreak detection procedures such as Farrington et al (1996), Noufaily et al (2012) or the negative binomial LR-CUSUM method described in Hhle and Paul (2008). A novel CUSUM approach combining logistic and multinomial logistic modelling is also included. Furthermore, inference methods for the retrospective infectious disease models in Held et al (2005), Held et al (2006), Paul et al (2008), Paul and Held (2011), Held and Paul (2012), and Meyer and Held (2014) are provided. Continuous self-exciting spatio-temporal point processes are modeled through additive-multiplicative conditional intensities as described in Hhle (2009) ('twinSIR', discrete space) and Meyer et al (2012) ('twinstim', continuous space). The package contains several real-world data sets, the ability to simulate outbreak data, visualize the results of the monitoring in temporal, spatial or spatio-temporal fashion. Note: Using the 'boda' algorithm requires the 'INLA' package, which should be installed automatically through the specified Additional_repositories, if uninstalled dependencies are also requested. As this might not work under OS X it might be necessary to manually install the 'INLA' package as specified at <http://www.r-inla.org/download>.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/413752015-06-12T21:52:12Z2015-06-12T21:52:12Zsurveillance was upgraded to version 1.9-1<a href="/packages/surveillance">surveillance</a> was <span class="action">upgraded</span> to version <a href="/packages/surveillance/versions/41175">1.9-1</a><br /><h3>Package description:</h3><p>Implementation of statistical methods for the modeling and change-point detection in time series of counts, proportions and categorical data, as well as for the modeling of continuous-time epidemic phenomena, e.g., discrete-space setups such as the spatially enriched Susceptible-Exposed-Infectious-Recovered (SEIR) models, or continuous-space point process data such as the occurrence of infectious diseases. Main focus is on outbreak detection in count data time series originating from public health surveillance of communicable diseases, but applications could just as well originate from environmetrics, reliability engineering, econometrics or social sciences. Currently, the package contains implementations of many typical outbreak detection procedures such as Farrington et al (1996), Noufaily et al (2012) or the negative binomial LR-CUSUM method described in Hhle and Paul (2008). A novel CUSUM approach combining logistic and multinomial logistic modelling is also included. Furthermore, inference methods for the retrospective infectious disease models in Held et al (2005), Held et al (2006), Paul et al (2008), Paul and Held (2011), Held and Paul (2012), and Meyer and Held (2014) are provided. Continuous self-exciting spatio-temporal point processes are modeled through additive-multiplicative conditional intensities as described in Hhle (2009) ('twinSIR', discrete space) and Meyer et al (2012) ('twinstim', continuous space). The package contains several real-world data sets, the ability to simulate outbreak data, visualize the results of the monitoring in temporal, spatial or spatio-temporal fashion. Note: Using the 'boda' algorithm requires the 'INLA' package, which should be installed automatically through the specified Additional_repositories, if uninstalled dependencies are also requested. As this might not work under OS X it might be necessary to manually install the 'INLA' package as specified at <http://www.r-inla.org/download>.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/413352015-06-11T10:32:13Z2015-06-11T10:32:13Zsurveillance was upgraded to version 1.9-0<a href="/packages/surveillance">surveillance</a> was <span class="action">upgraded</span> to version <a href="/packages/surveillance/versions/41135">1.9-0</a><br /><h3>Package description:</h3><p>Implementation of statistical methods for the modeling and change-point detection in time series of counts, proportions and categorical data, as well as for the modeling of continuous-time epidemic phenomena, e.g., discrete-space setups such as the spatially enriched Susceptible-Exposed-Infectious-Recovered (SEIR) models, or continuous-space point process data such as the occurrence of infectious diseases. Main focus is on outbreak detection in count data time series originating from public health surveillance of communicable diseases, but applications could just as well originate from environmetrics, reliability engineering, econometrics or social sciences. Currently, the package contains implementations of many typical outbreak detection procedures such as Farrington et al (1996), Noufaily et al (2012) or the negative binomial LR-CUSUM method described in Hhle and Paul (2008). A novel CUSUM approach combining logistic and multinomial logistic modelling is also included. Furthermore, inference methods for the retrospective infectious disease models in Held et al (2005), Held et al (2006), Paul et al (2008), Paul and Held (2011), Held and Paul (2012), and Meyer and Held (2014) are provided. Continuous self-exciting spatio-temporal point processes are modeled through additive-multiplicative conditional intensities as described in Hhle (2009) ('twinSIR', discrete space) and Meyer et al (2012) ('twinstim', continuous space). The package contains several real-world data sets, the ability to simulate outbreak data, visualize the results of the monitoring in temporal, spatial or spatio-temporal fashion. Note: Using the 'boda' algorithm requires the 'INLA' package, which should be installed automatically through the specified Additional_repositories, if uninstalled dependencies are also requested. As this might not work under OS X it might be necessary to manually install the 'INLA' package as specified at http://www.r-inla.org/download.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/381652015-01-06T06:32:46Z2015-01-06T06:32:46Zsurveillance was upgraded to version 1.8-3<a href="/packages/surveillance">surveillance</a> was <span class="action">upgraded</span> to version <a href="/packages/surveillance/versions/36467">1.8-3</a><br /><h3>Package description:</h3><p>A package implementing statistical methods for the modeling and change-point detection in time series of counts, proportions and categorical data, as well as for the modeling of continuous-time epidemic phenomena, e.g. discrete-space setups such as the spatially enriched Susceptible-Exposed-Infectious-Recovered (SEIR) models for surveillance data, or continuous-space point process data such as the occurrence of disease or earthquakes. Main 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 of typical outbreak detection procedures such as Farrington et al (1996), Noufaily et al (2012) or the negative binomial LR-CUSUM method described in Hoehle and Paul (2008). Furthermore, inference methods for the retrospective infectious disease model in Held et al (2005), Held et al (2006), Paul et al (2008) and Paul and Held (2011) are provided. A novel CUSUM approach combining logistic and multinomial logistic modelling is also included. Continuous self-exciting spatio-temporal point processes are modeled through additive-multiplicative conditional intensities as described in Höhle (2009) ("twinSIR", discrete space) and Meyer et al (2012) ("twinstim", continuous space). The package contains several real-world data sets, the ability to simulate outbreak data, visualize the results of the monitoring in temporal, spatial or spatio-temporal fashion. Note: Using the 'boda' algorithm requires the the INLA package, which should be installed automatically through the specified Additional_repositories, if uninstalled dependencies are also requested. As this might not work under Mac OS X it might be necessary to manually install the INLA package as specified at http://www.r-inla.org/download.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/375352014-10-30T15:13:03Z2014-10-30T15:13:03Zsurveillance was upgraded to version 1.8-1<a href="/packages/surveillance">surveillance</a> was <span class="action">upgraded</span> to version <a href="/packages/surveillance/versions/35589">1.8-1</a><br /><h3>Package description:</h3><p>A package implementing statistical methods for the modeling and change-point detection in time series of counts, proportions and categorical data, as well as for the modeling of continuous-time epidemic phenomena, e.g. discrete-space setups such as the spatially enriched Susceptible-Exposed-Infectious-Recovered (SEIR) models for surveillance data, or continuous-space point process data such as the occurrence of disease or earthquakes. Main 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 of typical outbreak detection procedures such as Farrington et al (1996), Noufaily et al (2012) or the negative binomial LR-CUSUM method described in Hoehle and Paul (2008). Furthermore, inference methods for the retrospective infectious disease model in Held et al (2005), Held et al (2006), Paul et al (2008) and Paul and Held (2011) are provided. A novel CUSUM approach combining logistic and multinomial logistic modelling is also included. Continuous self-exciting spatio-temporal point processes are modeled through additive-multiplicative conditional intensities as described in Höhle (2009) ("twinSIR", discrete space) and Meyer et al (2012) ("twinstim", continuous space). The package contains several real-world data sets, the ability to simulate outbreak data, visualize the results of the monitoring in temporal, spatial or spatio-temporal fashion. Note: The suggested package INLA is unfortunately not available from any mainstream repository - in case one wants to use the 'boda' algorithm one needs to manually install the INLA package as specified at http://www.r-inla.org/download.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/320152013-04-21T16:11:12Z2013-04-21T16:11:12Zsurveillance was upgraded to version 1.5-4<a href="/packages/surveillance">surveillance</a> was <span class="action">upgraded</span> to version <a href="/packages/surveillance/versions/27219">1.5-4</a><br /><h3>Package description:</h3><p>A package implementing statistical methods for the modeling and change-point detection in time series of counts, proportions and categorical data, as well as for the modeling of continuous-time epidemic phenomena, e.g. discrete-space setups such as the spatially enriched Susceptible-Exposed-Infectious-Recovered (SEIR) models for surveillance data, or continuous-space point process data such as the occurrence of disease or earthquakes. Main 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 of 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), Paul et al (2008) and Paul and Held (2011) are provided. A novel CUSUM approach combining logistic and multinomial logistic modelling is also included. Continuous self-exciting spatio-temporal point processes are modeled through additive-multiplicative conditional intensities as described in Höhle (2009) ("twinSIR", discrete space) and Meyer et al (2012) ("twinstim", continuous space). 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.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/306312013-03-19T06:31:30Z2013-03-19T06:31:30Zsurveillance was upgraded to version 1.5-2<a href="/packages/surveillance">surveillance</a> was <span class="action">upgraded</span> to version <a href="/packages/surveillance/versions/26092">1.5-2</a><br /><h3>Package description:</h3><p>A package implementing statistical methods for the modeling and change-point detection in time series of counts, proportions and categorical data, as well as for the modeling of continuous-time epidemic phenomena, e.g. discrete-space setups such as the spatially enriched Susceptible-Exposed-Infectious-Recovered (SEIR) models for surveillance data, or continuous-space point process data such as the occurrence of disease or earthquakes. Main 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 of 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), Paul et al (2008) and Paul and Held (2011) are provided. A novel CUSUM approach combining logistic and multinomial logistic modelling is also included. Continuous self-exciting spatio-temporal point processes are modeled through additive-multiplicative conditional intensities as described in Höhle (2009) ("twinSIR", discrete space) and Meyer et al (2012) ("twinstim", continuous space). 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.</p>crantastic.org