Пакунок: r-cran-surveillance (1.24.0-1) [debports]

[ Source:  ]

GNU R package for the Modeling and Monitoring of Epidemic Phenomena

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 Höhle 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 Höhle (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>.

Інші пакунки пов'язані з r-cran-surveillance

• dep: libc6 (>= 2.35)

  Бібліотека GNU C: спільні бібліотеки
  also a virtual package provided by libc6-udeb

• dep: libgcc-s1 (>= 3.0)

  Допоміжна бібліотека GCC

• dep: libstdc++6 (>= 5)

  Стандартна бібліотека C++ GNU, версії 3

• dep: r-api-4.0

  virtual package provided by r-base-core

• dep: r-cran-mass

  GNU R package of Venables and Ripley's MASS

• dep: r-cran-matrix

  GNU R package of classes for dense and sparse matrices

• dep: r-cran-nlme

  GNU R package for (non-)linear mixed effects models

• dep: r-cran-polycub (>= 0.8.0)

  GNU R Cubature over Polygonal Domains

• dep: r-cran-sp (>= 2.1-4)

  GNU R classes and methods for spatial data

• dep: r-cran-spatstat.geom

  GNU R geometrical functionality of the 'spatstat' package

• dep: r-cran-xtable (>= 1.7-0)

  GNU R coerce data to LaTeX and HTML tables

• rec: r-cran-animation

  GNU R gallery of animations and utilities to create animations

• rec: r-cran-coda

  Output analysis and diagnostics for MCMC simulations in R

• rec: r-cran-colorspace

  GNU R Color Space Manipulation

• rec: r-cran-gridextra (>= 2.0.0)

  GNU R package with extensions for the grid package

• rec: r-cran-gsl

  GNU R wrapper for the GNU Scientific Library

• rec: r-cran-intervals

  GNU R tools for working with points and intervals

• rec: r-cran-knitr

  GNU R package for dynamic report generation using Literate Programming

• rec: r-cran-lattice (>= 0.20-44)

  GNU R package for 'Trellis' graphics

• rec: r-cran-maxlik

  GNU R maximum likelihood estimation

• rec: r-cran-memoise

  Memoise functions

• rec: r-cran-msm

  GNU R Multi-state Markov and hidden Markov models in continuous time

• rec: r-cran-numderiv

  GNU R package for accurate numerical derivatives

• rec: r-cran-polyclip

  GNU R Polygon Clipping

• rec: r-cran-quadprog

  GNU R package for solving quadratic programming problems

• rec: r-cran-scales

  Scale functions for visualization

• rec: r-cran-sf

  Simple Features for R

• rec: r-cran-spc

  GNU R Statistical Process Control

• rec: r-cran-spdep

  GNU R spatial dependence: weighting schemes, statistics and models

• rec: r-cran-tinytest (>= 1.2.4)

  Lightweight and Feature Complete Unit Testing Framework

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Архітектура	Розмір пакунка	Розмір після встановлення	Файли
ppc64 (unofficial port)	5,449.1 kB	6,517.0 kB	[список файлів]