tscount1.4.3 package

Analysis of Count Time Series

countdistr

Count Data Distributions

ingarch.analytical

Analytical Mean, Variance and Autocorrelation of an INGARCH Process

interv_covariate

Describing Intervention Effects for Time Series with Deterministic Cov...

interv_detect.tsglm

Detecting an Intervention in Count Time Series Following Generalised L...

interv_multiple.tsglm

Detecting Multiple Interventions in Count Time Series Following Genera...

interv_test.tsglm

Testing for Interventions in Count Time Series Following Generalised L...

invertinfo

Compute a Covariance Matrix from a Fisher Information Matrix

marcal

Predictive Model Assessment with a Marginal Calibration Plot

pit

Predictive Model Assessment with a Probability Integral Transform Hist...

plot.interv_detect

Plot Test Statistic of Intervention Detection Procedure for Count Time...

plot.interv_multiple

Plot for Iterative Intervention Detection Procedure for Count Time Ser...

plot.tsglm

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

predict.tsglm

Predicts Method for Time Series of Counts Following Generalised Linear...

QIC

Quasi Information Criterion of a Generalised Linear Model for Time Ser...

residuals.tsglm

Residuals of a Generalised Linear Model for Time Series of Counts

scoring

Predictive Model Assessment with Proper Scoring Rules

se.tsglm

Standard Errors of a Fitted Generalised Linear Model for Time Series o...

summary.tsglm

Summarising Fits of Count Time Series following Generalised Linear Mod...

tscount-package

Analysis of Count Time Series

tsglm

Count Time Series Following Generalised Linear Models

tsglm.sim

Simulate a Time Series Following a Generalised Linear Model

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.