R: Analysis of Count Time Series

# Analysis of Count Time Series

## Documentation for package ‘tscount’ version 1.4.3

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS

## Help Pages

tscount-package | Analysis of Count Time Series
ardistr | Count Data Distributions
campy | Campylobacter Infections Time Series
checkdistr | Count Data Distributions
countdistr | Count Data Distributions
ddistr | Count Data Distributions
ecoli | E. coli Infections Time Series
ehec | EHEC Infections Time Series
influenza | Influenza Infections Time Series
ingarch.acf | Analytical Mean, Variance and Autocorrelation of an INGARCH Process
ingarch.analytical | Analytical Mean, Variance and Autocorrelation of an INGARCH Process
ingarch.mean | Analytical Mean, Variance and Autocorrelation of an INGARCH Process
ingarch.var | Analytical Mean, Variance and Autocorrelation of an INGARCH Process
interv_covariate | Describing Intervention Effects for Time Series with Deterministic Covariates
interv_detect | Detecting an Intervention in Count Time Series Following Generalised Linear Models
interv_detect.tsglm | Detecting an Intervention in Count Time Series Following Generalised Linear Models
interv_multiple | Detecting Multiple Interventions in Count Time Series Following Generalised Linear Models
interv_multiple.tsglm | Detecting Multiple Interventions in Count Time Series Following Generalised Linear Models
interv_test | Testing for Interventions in Count Time Series Following Generalised Linear Models
interv_test.tsglm | Testing for Interventions in Count Time Series Following Generalised Linear Models
invertinfo | Compute a Covariance Matrix from a Fisher Information Matrix
logLik.tsglm | Count Time Series Following Generalised Linear Models
marcal | Predictive Model Assessment with a Marginal Calibration Plot
marcal.default | Predictive Model Assessment with a Marginal Calibration Plot
marcal.tsglm | Predictive Model Assessment with a Marginal Calibration Plot
measles | Measles Infections Time Series
pdistr | Count Data Distributions
pit | Predictive Model Assessment with a Probability Integral Transform Histogram
pit.default | Predictive Model Assessment with a Probability Integral Transform Histogram
pit.tsglm | Predictive Model Assessment with a Probability Integral Transform Histogram
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
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 Models
print.summary.tsglm | Summarising Fits of Count Time Series following Generalised Linear Models
print.tsglm | Count Time Series Following Generalised Linear Models
qdistr | Count Data Distributions
QIC | Quasi Information Criterion of a Generalised Linear Model for Time Series of Counts
QIC.tsglm | Quasi Information Criterion of a Generalised Linear Model for Time Series of Counts
rdistr | Count Data Distributions
residuals.tsglm | Residuals of a Generalised Linear Model for Time Series of Counts
scoring | Predictive Model Assessment with Proper Scoring Rules
scoring.default | Predictive Model Assessment with Proper Scoring Rules
scoring.tsglm | Predictive Model Assessment with Proper Scoring Rules
sddistr | Count Data Distributions
se | Standard Errors of a Fitted Generalised Linear Model for Time Series of Counts
se.tsglm | Standard Errors of a Fitted Generalised Linear Model for Time Series of Counts
summary.tsglm | Summarising Fits of Count Time Series following Generalised Linear Models
tscount | Analysis of Count Time Series
tsglm | Count Time Series Following Generalised Linear Models
tsglm.meanfit | Count Time Series Following Generalised Linear Models
tsglm.sim | Simulate a Time Series Following a Generalised Linear Model
vcov.tsglm | Count Time Series Following Generalised Linear Models