Learn R Programming

⚠️There's a newer version (1.4.3) of this package.Take me there.

tscount (version 1.4.1)

Analysis of Count Time Series

Description

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.

Copy Link

Version

Install

install.packages('tscount')

Monthly Downloads

612

Version

1.4.1

License

GPL-2 | GPL-3

Maintainer

Tobias Liboschik

Last Published

November 24th, 2017

Functions in tscount (1.4.1)

interv_detect.tsglm

Detecting an Intervention in Count Time Series Following Generalised Linear Models

EHEC Infections Time Series

Influenza Infections Time Series

interv_multiple.tsglm

Detecting Multiple Interventions in Count Time Series Following Generalised Linear Models

ingarch.analytical

Analytical Mean, Variance and Autocorrelation of an INGARCH Process

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

Campylobacter Infections Time Series

interv_covariate

Describing Intervention Effects for Time Series with Deterministic Covariates

Count Data Distributions

Predictive Model Assessment with a Marginal Calibration Plot

E. coli Infections Time Series

interv_test.tsglm

Testing for Interventions in Count Time Series Following Generalised Linear Models

Measles Infections Time Series

Compute a Covariance Matrix from a Fisher Information Matrix

Predicts Method for Time Series of Counts Following Generalised Linear Models

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

Diagnostic Plots for a Fitted GLM-type Model for Time Series of Counts

Predictive Model Assessment with a Probability Integral Transform Histogram

Predictive Model Assessment with Proper Scoring Rules

plot.interv_detect

Plot Test Statistic of Intervention Detection Procedure for Count Time Series Following Generalised Linear Models

Standard Errors of a Fitted Generalised Linear Model for Time Series of Counts

Count Time Series Following Generalised Linear Models

Simulate a Time Series Following a Generalised Linear Model

Summarising Fits of Count Time Series following Generalised Linear Models

tscount-package

Analysis of Count Time Series