tscount v1.3.0
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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 | |
| QIC | Quasi Information Criterion of a Generalised Linear Model for Time Series of Counts | |
| invertinfo | Compute a Covariance Matrix from a Fisher Information Matrix | |
| interv_test.tsglm | Testing for Interventions in Count Time Series Following Generalised Linear Models | |
| countdistr | Count Data Distributions | |
| influenza | Influenza Infections Time Series | |
| ehec | EHEC Infections Time Series | |
| se.tsglm | Standard Errors of a Fitted Generalised Linear Model for Time Series of Counts | |
| tsglm.sim | Simulate a Time Series Following a Generalised Linear Model | |
| predict.tsglm | Predicts Method for Time Series of Counts Following Generalised Linear Models | |
| ingarch.analytical | Analytical Mean, Variance and Autocorrelation of an INGARCH Process | |
| ecoli | E. coli Infections Time Series | |
| measles | Measles Infections Time Series | |
| campy | Campylobacter Infections Time Series | |
| tsglm | Count Time Series Following Generalised Linear Models | |
| plot.tsglm | Diagnostic Plots for a Fitted GLM-type Model for Time Series of Counts | |
| marcal | Predictive Model Assessment with a Marginal Calibration Plot | |
| pit | Predictive Model Assessment with a Probability Integral Transform Histogram | |
| scoring | Predictive Model Assessment with Proper Scoring Rules | |
| summary.tsglm | Summarising Fits of Count Time Series following Generalised Linear Models | |
| residuals.tsglm | Residuals of a Generalised Linear Model for Time Series of Counts | |
| plot.interv_detect | Plot Test Statistic of Intervention Detection Procedure for Count Time Series Following Generalised Linear Models | |
| plot.interv_multiple | Plot for Iterative Intervention Detection Procedure for Count Time Series following Generalised Linear Models | |
| interv_multiple.tsglm | Detecting Multiple Interventions in Count Time Series Following Generalised Linear Models | |
| tscount-package | Analysis of Count Time Series | |
| interv_detect.tsglm | Detecting an Intervention in Count Time Series Following Generalised Linear Models | |
| interv_covariate | Describing Intervention Effects for Time Series with Deterministic Covariates | |
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Details
| Type | Package |
| Date | 2016-05-13 |
| License | GPL-2 | GPL-3 |
| URL | http://tscount.r-forge.r-project.org |
| ByteCompile | true |
| NeedsCompilation | no |
| LazyData | true |
| Encoding | UTF-8 |
| Packaged | 2016-05-13 16:50:30 UTC; liboschik |
| Repository | CRAN |
| Date/Publication | 2016-05-13 19:30:24 |
| suggests | acp , gamlss , gamlss.data , gamlss.util , gcmr , glarma , KFAS , Matrix , surveillance , VGAM , xtable |
| imports | ltsa , parallel |
| Contributors | Tobias Liboschik, Jonathan Rathjens, Roland Fried, Konstantinos Fokianos, Philipp Probst |
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