Provides maximum likelihood estimation methods for eight modified Weibull-type distributions. It returns parameter estimates, log-likelihood, AIC, and BIC, and also supports model fitting, validation, and comparison across different distributional forms. These methods can be applied to reliability, survival, and lifetime data analysis, making the package useful for researchers and practitioners in statistics, engineering, and medicine. The following distributions are included: Rangoli2023, Peng2014, Lai2003, Xie1996, Sarhan2009, Rangoli2025, Mustafa2012, and Alwasel2009.

Implements reversal association pattern analysis for categorical data. Detects sub-tables exhibiting reversal associations in contingency tables, provides visualization tools, and supports simulation-based validation for complex I × J tables.

The main goal is to make descriptive evaluations easier to create bigger and more complex outputs in less time with less code. Introducing format containers with multilabels <https:[https://documentation.sas.com/doc/en/pgmsascdc/v_067/proc/p06ciqes4eaqo6n0zyqtz9p21nfb.htm]>, a more powerful summarise which is capable to output every possible combination of the provided grouping variables in one go <https:[https://documentation.sas.com/doc/en/pgmsascdc/v_067/proc/p0jvbbqkt0gs2cn1lo4zndbqs1pe.htm]>, tabulation functions which can create any table in different styles <https:[https://documentation.sas.com/doc/en/pgmsascdc/v_067/proc/n1ql5xnu0k3kdtn11gwa5hc7u435.htm]> and other more readable functions. The code is optimized to work fast even with datasets of over a million observations.

Generate citations and references for R packages from CRAN or Bioconductor. Supports RIS and BibTeX formats with automatic DOI retrieval from GitHub repositories and published papers. Includes command-line interface for batch processing.

A tool to perform all different statistical tests and calculations needed by Biological dosimetry Laboratories. Detailed documentation is available in &lt;<a href="https://biodosetools-team.github.io/documentation/" target="_top">https://biodosetools-team.github.io/documentation/</a>&gt;.

Facilitates writing computationally reproducible student theses in PDF format that conform to the American Psychological Association (APA) manuscript guidelines (6th Edition). The package currently provides two R Markdown templates for homework and theses at the Psychology Department of the University of Cologne. The package builds on the package 'papaja' but is tailored to the requirements of student theses and omits features for simplicity.

Autoregressive distributed lag (A[R]DL) models (and their reparameterized equivalent, the Generalized Error-Correction Model [GECM]) (see De Boef and Keele 2008 &lt;<a href="https://doi.org/10.1111%2Fj.1540-5907.2007.00307.x" target="_top">doi:10.1111/j.1540-5907.2007.00307.x</a>&gt;) are the workhorse models in uncovering dynamic inferences. ADL models are simple to estimate; this is what makes them attractive. Once these models are estimated, what is less clear is how to uncover a rich set of dynamic inferences from these models. We provide tools for recovering those inferences in three forms: causal inferences from ADL models, traditional time series quantities of interest (short- and long-run effects), and dynamic conditional relationships.

An implementation that combines trait data and a phylogenetic tree (or trees) into a single object of class 'treedata.table'. The resulting object can be easily manipulated to simultaneously change the trait- and tree-level sampling. Currently implemented functions allow users to use a 'data.table' syntax when performing operations on the trait dataset within the 'treedata.table' object. For more details see Roman-Palacios et al. (2021) &lt;<a href="https://doi.org/10.7717%2Fpeerj.12450" target="_top">doi:10.7717/peerj.12450</a>&gt;.

Dual interfaces, graphical and programmatic, designed for intuitive applications of Multilevel Regression and Poststratification (MRP). Users can apply the method to a variety of datasets, from electronic health records to sample survey data, through an end-to-end Bayesian data analysis workflow. The package provides robust tools for data cleaning, exploratory analysis, flexible model building, and insightful result visualization. For more details, see Si et al. (2020) &lt;<a href="https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2020002/article/00003-eng.pdf?st=iF1_Fbrh" target="_top">https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2020002/article/00003-eng.pdf?st=iF1_Fbrh</a>&gt; and Si (2025) &lt;<a href="https://doi.org/10.1214%2F24-STS932" target="_top">doi:10.1214/24-STS932</a>&gt;.

Supports eigenvalue block-averaging p-values (Foldnes, Grønneberg, 2018) &lt;<a href="https://doi.org/10.1080%2F10705511.2017.1373021" target="_top">doi:10.1080/10705511.2017.1373021</a>&gt;, penalized eigenvalue block-averaging p-values (Foldnes, Moss, Grønneberg, 2024) &lt;<a href="https://doi.org/10.1080%2F10705511.2024.2372028" target="_top">doi:10.1080/10705511.2024.2372028</a>&gt;, penalized regression p-values (Foldnes, Moss, Grønneberg, 2024) &lt;<a href="https://doi.org/10.1080%2F10705511.2024.2372028" target="_top">doi:10.1080/10705511.2024.2372028</a>&gt;, as well as traditional p-values such as Satorra-Bentler. All p-values can be calculated using unbiased or biased gamma estimates (Du, Bentler, 2022) &lt;<a href="https://doi.org/10.1080%2F10705511.2022.2063870" target="_top">doi:10.1080/10705511.2022.2063870</a>&gt; and two choices of chi square statistics.

Fit penalized splines mixed-effects models (a special case of additive models) for large longitudinal datasets. The package includes a psme() function that (1) relies on package 'mgcv' for constructing population and subject smooth functions as penalized splines, (2) transforms the constructed additive model to a linear mixed-effects model, (3) exploits package 'lme4' for model estimation and (4) backtransforms the estimated linear mixed-effects model to the additive model for interpretation and visualization. See Pedersen et al. (2019) &lt;<a href="https://doi.org/10.7717%2Fpeerj.6876" target="_top">doi:10.7717/peerj.6876</a>&gt; and Bates et al. (2015) &lt;<a href="https://doi.org/10.18637%2Fjss.v067.i01" target="_top">doi:10.18637/jss.v067.i01</a>&gt; for an introduction. Unlike the gamm() function in 'mgcv', the psme() function is fast and memory-efficient, able to handle datasets with millions of observations.