# tscount v1.4.2

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## Analysis of Count Time Series

Likelihood-based methods for model fitting and assessment, prediction and intervention analysis of count time series following generalized linear models are provided. Models with the identity and with the logarithmic link function are allowed. The conditional distribution can be Poisson or Negative Binomial.

## Functions in tscount

Name | Description | |

marcal | Predictive Model Assessment with a Marginal Calibration Plot | |

plot.interv_multiple | Plot for Iterative Intervention Detection Procedure for Count Time Series following Generalised Linear Models | |

residuals.tsglm | Residuals of a Generalised Linear Model for Time Series of Counts | |

invertinfo | Compute a Covariance Matrix from a Fisher Information Matrix | |

interv_multiple.tsglm | Detecting Multiple Interventions in Count Time Series Following Generalised Linear Models | |

pit | Predictive Model Assessment with a Probability Integral Transform Histogram | |

predict.tsglm | Predicts Method for Time Series of Counts Following Generalised Linear Models | |

tsglm | Count Time Series Following Generalised Linear Models | |

measles | Measles Infections Time Series | |

plot.interv_detect | Plot Test Statistic of Intervention Detection Procedure for Count Time Series Following Generalised Linear Models | |

summary.tsglm | Summarising Fits of Count Time Series following Generalised Linear Models | |

interv_test.tsglm | Testing for Interventions in Count Time Series Following Generalised Linear Models | |

scoring | Predictive Model Assessment with Proper Scoring Rules | |

tscount-package | Analysis of Count Time Series | |

plot.tsglm | Diagnostic Plots for a Fitted GLM-type Model for Time Series of Counts | |

tsglm.sim | Simulate a Time Series Following a Generalised Linear Model | |

se.tsglm | Standard Errors of a Fitted Generalised Linear Model for Time Series of Counts | |

countdistr | Count Data Distributions | |

QIC | Quasi Information Criterion of a Generalised Linear Model for Time Series of Counts | |

ingarch.analytical | Analytical Mean, Variance and Autocorrelation of an INGARCH Process | |

influenza | Influenza Infections Time Series | |

ehec | EHEC Infections Time Series | |

ecoli | E. coli Infections Time Series | |

interv_covariate | Describing Intervention Effects for Time Series with Deterministic Covariates | |

campy | Campylobacter Infections Time Series | |

interv_detect.tsglm | Detecting an Intervention in Count Time Series Following Generalised Linear Models | |

No Results! |

## Vignettes of tscount

Name | ||

INLA.RData | ||

bibliography.bib | ||

campy.RData | ||

covariates.RData | ||

distrcoef_n200.RData | ||

distrcoef_size1.RData | ||

qic.RData | ||

seatbelts.RData | ||

tscount-computations.R | ||

tsglm.Rnw | ||

No Results! |

## Last month downloads

## Details

Type | Package |

Date | 2020-02-29 |

License | GPL-2 | GPL-3 |

URL | http://tscount.r-forge.r-project.org |

ByteCompile | true |

NeedsCompilation | no |

LazyData | true |

Encoding | UTF-8 |

Packaged | 2020-02-29 22:42:19 UTC; Tobias |

Repository | CRAN |

Date/Publication | 2020-03-02 11:30:02 UTC |

suggests | acp , gamlss , gamlss.data , gamlss.util , gcmr , glarma , KFAS , Matrix , surveillance , VGAM , xtable |

imports | ltsa , parallel |

Contributors | Jonathan Rathjens, Roland Fried, Konstantinos Fokianos, Philipp Probst |

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