Provides a coherent interface for evaluating models fit with the
  trending package.  This package is part of the RECON
  (<https://www.repidemicsconsortium.org/>) toolkit for outbreak analysis.

Provides a coherent interface to multiple modelling tools for
  fitting trends along with a standardised approach for generating confidence
  and prediction intervals.

Estimation of causal odds ratio and power calculation given trends in exposure prevalence    and outcome frequencies of stratified data.

Fitting models for, and simulation of, trend locally stationary 
    wavelet (TLSW) time series models, which take account of time-varying 
    trend and dependence structure in a univariate time series. The TLSW model, 
    and its estimation, is described in McGonigle, Killick and Nunes (2022a) 
    <doi:10.1111/jtsa.12643>, (2022b) <doi:10.1214/22-EJS2044>. Further information regarding the use of the package, along with detailed examples, can be found in McGonigle, Killick and Nunes (2025) <doi:10.18637/jss.v115.i10>. New users will 
    likely want to start with the TLSW function.

Performs the detection of linear trend changes for univariate time series 
    by implementing the bottom-up unbalanced wavelet transformation proposed by 
    H. Maeng and P. Fryzlewicz (2023). The estimated number and locations of the 
    change-points are returned with the piecewise-linear estimator for signal.

Extract trends from monthly and quarterly economic time series.
    Provides two main functions: augment_trends() for pipe-friendly 'tibble' workflows
    and extract_trends() for direct time series analysis. Includes key econometric
    filters and modern parameter experimentation tools.

Provides a set of exploratory data analysis (EDA) tools for 
    visualizing trends, diagnosing data types for beginner-friendly workflows, 
    and automatically routing to suitable statistical tests or trend exploration 
    models. Includes unified plotting functions for trend lines, grouped boxplots, 
    and comparative scatterplots; automated statistical testing (e.g., t-test, 
    Wilcoxon, ANOVA, Kruskal-Wallis, Tukey, Dunn) with optional effect size 
    calculation; and model-based trend analysis using generalized additive 
    models (GAM) for count data, generalized linear models (GLM) for continuous 
    data, and zero-inflated models (ZIP/ZINB) for count data with potential 
    zero-inflation. 
    Also supports time-window continuity checks, cross-year 
    handling in compare_monthly_cases(), and ARIMA-ready preparation with 
    stationarity diagnostics, ensuring consistent parameter styles for 
    reproducible research and user-friendly workflows.Methods are 
    based on R Core Team (2024) <https://www.R-project.org/>, 
    Wood, S.N.(2017, ISBN:978-1498728331),
    Hyndman RJ, Khandakar Y (2008) <doi:10.18637/jss.v027.i03>, 
    Simon Jackman (2024) <https://github.com/atahk/pscl/>,    
    Achim Zeileis, Christian Kleiber, Simon Jackman (2008) <doi:10.18637/jss.v027.i08>.