trendtestR: Exploratory Trend Analysis and Visualization for Time-Series and
Grouped Data
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>.
| Version: |
1.0.1 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
dplyr, ggplot2 (≥ 3.3.0), lubridate, emmeans, e1071, forecast, MASS, multcomp, tidyselect, tidyr, tseries, car, FSA, ggpubr, rlang, splines, pscl, mgcv |
| Suggests: |
testthat (≥ 3.0.0), mockr, knitr, rmarkdown |
| Published: |
2025-09-02 |
| Author: |
Gelan Huang [aut, cre] |
| Maintainer: |
Gelan Huang <huanggelan97 at icloud.com> |
| BugReports: |
https://github.com/GrahnH/trendtestR/issues |
| License: |
GPL (≥ 3) |
| URL: |
https://github.com/GrahnH/trendtestR |
| NeedsCompilation: |
no |
| CRAN checks: |
trendtestR results |
Documentation:
Downloads:
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