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trendtestR (version 1.0.1)

Exploratory Trend Analysis and Visualization for Time-Series and Grouped Data

Description

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) , Wood, S.N.(2017, ISBN:978-1498728331), Hyndman RJ, Khandakar Y (2008) , Simon Jackman (2024) , Achim Zeileis, Christian Kleiber, Simon Jackman (2008) .

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Install

install.packages('trendtestR')

Monthly Downloads

129

Version

1.0.1

License

GPL (>= 3)

Issues

0

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0

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Maintainer

Gelan Huang

Last Published

September 2nd, 2025

Functions in trendtestR (1.0.1)

run_multi_group_tests

Multi-Group Test with Assumption Checks / Mehr-Gruppen-Test mit Annahmepruefung
infer_value_type

Infer variable type from numeric vector / Typ-Erkennung numerischer Vektoren
plot_weekly_cases

Visualize Weekly Aggregated Values / Woechentliche aggregierte Werte visualisieren
filter_by_groupcol

Filter and optionally reshape a data frame by group column / Nach Gruppenspalte filtern und optional umstrukturieren
run_count_two_group_tests

Statistical Test for Count Data (Two Groups) / Statistischer Test fuer Zaehldaten (Zwei Gruppen)
run_group_tests

Automated Selection of Statistical Group Tests / Automatisierte Auswahl statistischer Gruppentests
run_count_multi_group_tests

Statistical Test for Count Data (Multi-Groups) / Statistischer Test fuer Zaehldaten (Mehrere Gruppen)
prepare_group_data

Prepare Grouped Data for Statistical Testing
explore_zinb_trend

Explore zero-inflated models (ZIP/ZINB) for count data trends / Analyse von Zero-Inflated-Modellen (ZIP/ZINB) fuer Zeitreihen mit Zaehldaten
standardize_case_columns

Standardize date and value columns / Standardisierung von Datum und Werten
run_paired_tests

Paired / Unpaired Two-Group Tests with Assumption Checks / Zwei-Gruppen-Test mit Vorannahmepruefung
compare_distribution_by_granularity

Compare Normality across Granularity Levels / Vergleich der Normalverteilung je Granularitaet
check_continuity_by_window

Check Time Series Continuity within Defined Window / Pruefung der Zeitreihen-Kontinuitaet
explore_continuous_trend

Explore linear and GLM trends for continuous data with automatic model selection / Analyse linearer und GLM-Trends fuer kontinuierliche Daten mit automatischer Modellauswahl
compare_monthly_cases

Compare Monthly Case Trends across Years / Vergleich monatlicher Falltrends zwischen Jahren
check_rate_diff_arima_ready

Assess Time Series Readiness for ARIMA Modeling / Pruefung der Eignung fuer ARIMA-Zeitreihenmodellierung
explore_poisson_trend_Legacy

(Legacy) Old version of explore_poisson_trend()
explore_trend_auto

Main dispatcher for trend analysis based on data type / Hauptverzweiger fuer Trendanalyse basierend auf Datentyp
explore_poisson_trend

Explore time-based GAM for count data trend with automatic model selection / Zeitbasierte GAM-Trendanalyse fuer Zaehldaten mit automatischer Modellauswahl
check_input_validity

Validate Time and Group Inputs for Case Comparison / Eingabepruefung fuer Zeit- und Gruppierungsvariablen
diagnose_model_trend

Diagnose a fitted model using residual plots and statistical tests (ggplot2 only) / Modell-Diagnose mittels Residuenplots und statistischen Tests (nur ggplot2)