# DClusterm v1.0-1

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## Model-Based Detection of Disease Clusters

Model-based methods for the detection of disease clusters using GLMs, GLMMs and zero-inflated models. These methods are described in 'V. G<c3><b3>mez-Rubio et al.' (2019) <doi:10.18637/jss.v090.i14> and 'V. G<c3><b3>mez-Rubio et al.' (2018) <doi:10.1007/978-3-030-01584-8_1>.

## Functions in DClusterm

 Name Description get.stclusters Gets areas in a spatio-temporal cluster get.allknclusters Extract indices of the areas in the clusters detected glmAndZIP.iscluster Obtains the cluster with the maximum log-likelihood ratio or minimum DIC of all the clusters with the same center and start and end dates. knbinary Constructs data frame with clusters in binary format. SetVbleCluster Constructs a variable that indicates the locations and times that pertain to a cluster. slimknclusters Remove overlapping clusters mergeknclusters Merges clusters so that they are identifed as levels of a factor. brainNM Brain cancer in New Mexico, USA, 1973-1991. CalcStatsAllClusters Obtains the clusters with the maximum log-likelihood ratio or minimum DIC for each center and start and end dates. CalcStatClusterGivenCenter Calls the function to obtain the cluster with the maximum log-likelihood ratio or minimum DIC of all the clusters with the same center and start and end dates. computeprob Computes the probability that a model parameter is <=k from inla marginals SelectStatsAllClustersNoOverlap Removes the overlapping clusters. CreateGridDClusterm Creates grid over the study area. NY8 Leukemia in an eight-county region of upstate New York, 1978-1982. DetectClustersModel Detects clusters and computes their significance. Navarre Brain cancer in males in Navarre, Spain, 1988-1994. No Results!