This package 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>.

This package provides a synthetic control offers a way of evaluating the effect of an intervention in comparative case studies. The package makes a number of improvements when implementing the method in R. These improvements allow users to inspect, visualize, and tune the synthetic control more easily. A key benefit of a tidy implementation is that the entire preparation process for building the synthetic control can be accomplished in a single pipe.

This package provides a constrained two-dimensional Delaunay triangulation package providing both triangulation and generation of voronoi mosaics of irregular spaced data. Please note that most of the functions are now also covered in package interp, which is a re-implementation from scratch under a free license based on a different triangulation algorithm.

This package provides a general framework of two directional simultaneous inference is provided for high-dimensional as well as the fixed dimensional models with manifest variable or latent variable structure, such as high-dimensional mean models, high- dimensional sparse regression models, and high-dimensional latent factors models. It is making the simultaneous inference on a set of parameters from two directions, one is testing whether the estimated zero parameters indeed are zero and the other is testing whether there exists zero in the parameter set of non-zero. More details can be referred to Wei Liu, et al. (2022) <doi:10.48550/arXiv.2012.11100>.

The main objective of cooperative Transferable-Utility games (TU-games) is to allocate a good among the agents involved. The package implements major solution concepts including the Shapley value, Banzhaf value, and egalitarian rules, alongside their extensions for structured games: the Owen value and Banzhaf-Owen value for games with a priori unions, and the Myerson value for communication games on networks. To address the inherent exponential computational complexity of exact evaluation, the package offers both exact algorithms and linear approximation methods based on sampling, enabling the analysis of large-scale games. Additionally, it supports core set-based solutions, allowing computation of the vertices and the centroid of the core.

This package contains functions for applying the T^2-test for equivalence. The T^2-test for equivalence is a multivariate two-sample equivalence test. Distance measure of the test is the Mahalanobis distance. For multivariate normally distributed data the T^2-test for equivalence is exact and UMPI. The function T2EQ() implements the T^2-test for equivalence according to Wellek (2010) <DOI:10.1201/ebk1439808184>. The function T2EQ.dissolution.profiles.hoffelder() implements a variant of the T^2-test for equivalence according to Hoffelder (2016) <http://www.ecv.de/suse_item.php?suseId=Z|pi|8430> for the equivalence comparison of highly variable dissolution profiles.

This package provides a calculator for the two-dimensional clinical Disease Activity index for Psoriatic Arthritis (TwoDcDAPSA), a principal component-derived measure that complements the conventional clinical DAPSA score. The TwoDcDAPSA captures residual variation in patient-reported outcomes (pain and patient global assessment) and joint counts (swollen and tender) after adjusting for standardized cDAPSA using natural spline coefficients derived from published models. Residuals are standardized and combined with fixed principal component loadings to yield a continuous PROs-Joint Contrast (PJC) score and quartile groupings. The package applies pre-specified coefficients and loadings to new datasets but does not estimate spline models or principal components itself.

Tidying functions built on data.table to provide quick and efficient data manipulation with minimal overhead.

Identifies clusters of individual longitudinal trajectories. In the spirit of Leffondre et al. (2004), the procedure involves identifying each trajectory to a point in the space of measures. In this context, a measure is a quantity meant to capture a certain characteristic feature of the trajectory. The points in the space of measures are then clustered using a version of spectral clustering.

Computes treatment patterns within a given cohort using the Observational Medical Outcomes Partnership (OMOP) common data model (CDM). As described in Markus, Verhamme, Kors, and Rijnbeek (2022) <doi:10.1016/j.cmpb.2022.107081>.

Covers k-table control analysis using multivariate control charts for qualitative variables using fundamentals of multiple correspondence analysis and multiple factor analysis. The graphs can be shown in a flat or interactive way, in the same way all the outputs can be shown in an interactive shiny panel.

This package provides a collection of tools for trade practitioners, including the ability to calibrate different consumer demand systems and simulate the effects of tariffs and quotas under different competitive regimes. These tools are derived from Anderson et al. (2001) <doi:10.1016/S0047-2727(00)00085-2> and Froeb et al. (2003) <doi:10.1016/S0304-4076(02)00166-5>.

STARMA (Space-Time Autoregressive Moving Average) models are commonly utilized in modeling and forecasting spatiotemporal time series data. However, the intricate nonlinear dynamics observed in many space-time rainfall patterns often exceed the capabilities of conventional STARMA models. This R package enables the fitting of Time Delay Spatio-Temporal Neural Networks, which are adept at handling such complex nonlinear dynamics efficiently. For detailed methodology, please refer to Saha et al. (2020) <doi:10.1007/s00704-020-03374-2>.

This package provides tools that stem and lemmatize text. Stemming is a process that removes endings such as affixes. Lemmatization is the process of grouping inflected forms together as a single base form.

This package provides tools to deploy TensorFlow <https://www.tensorflow.org/> models across multiple services. Currently, it provides a local server for testing cloudml compatible services.

Utils for basic statistical experiments, that can be used for teaching introductory statistics. Each experiment generates a tibble. Dice rolls and coin flips are simulated using sample(). The properties of the dice can be changed, like the number of sides. A coin flip is simulated using a two sided dice. Experiments can be combined with the pipe-operator.

Detection of outliers in time series following the Chen and Liu (1993) <DOI:10.2307/2290724> procedure. Innovational outliers, additive outliers, level shifts, temporary changes and seasonal level shifts are considered.

Determine the path of the executing script. Compatible with several popular GUIs: Rgui', RStudio', Positron', VSCode', Jupyter', Emacs', and Rscript (shell). Compatible with several functions and packages: source()', sys.source()', debugSource() in RStudio', compiler::loadcmp()', utils::Sweave()', box::use()', knitr::knit()', plumber::plumb()', shiny::runApp()', package:targets', and testthat::source_file()'.

Converts coefficients, standard errors, significance stars, and goodness-of-fit statistics of statistical models into LaTeX tables or HTML tables/MS Word documents or to nicely formatted screen output for the R console for easy model comparison. A list of several models can be combined in a single table. The output is highly customizable. New model types can be easily implemented. Details can be found in Leifeld (2013), JStatSoft <doi:10.18637/jss.v055.i08>.).

Simulate genotypes for case-parent triads, case-control, and quantitative trait samples with realistic linkage diequilibrium structure and allele frequency distribution. For studies of epistasis one can simulate models that involve specific SNPs at specific sets of loci, which we will refer to as "pathways". TriadSim generates genotype data by resampling triad genotypes from existing data. The details of the method is described in the manuscript under preparation "Simulating Autosomal Genotypes with Realistic Linkage Disequilibrium and a Spiked in Genetic Effect" Shi, M., Umbach, D.M., Wise A.S., Weinberg, C.R.

Parsing (R)Markdown files with numerous regular expressions can be fraught with peril, but it does not have to be this way. Converting (R)Markdown files to XML using the commonmark package allows in-memory editing via of markdown elements via XPath through the extensible R6 class called yarn'. These modified XML representations can be written to (R)Markdown documents via an xslt stylesheet which implements an extended version of GitHub'-flavoured markdown so that you can tinker to your hearts content.

Greedy optimal subset selection for transformation models (Hothorn et al., 2018, <doi:10.1111/sjos.12291> ) based on the abess algorithm (Zhu et al., 2020, <doi:10.1073/pnas.2014241117> ). Applicable to models from packages tram and cotram'. Application to shift-scale transformation models are described in Siegfried et al. (2024, <doi:10.1080/00031305.2023.2203177>).

This package provides a shiny app that generates plots and summary tables from repeat-dose toxicology study results to facilitate holistic evaluation of the drug safety of active pharmaceutical ingredients (API) prior to initiation of clinical trials.

Type hints are special comments within a function body indicating the intended nature of the function's arguments in terms of data types, dimensions and permitted values. The actual parameters with which the function is called are evaluated against these type hint comments at run-time.