This package provides a decorator is a function that receives a function, extends its behaviour, and returned the altered function. Any caller that uses the decorated function uses the same interface as it were the original, undecorated function. Decorators serve two primary uses: (1) Enhancing the response of a function as it sends data to a second component; (2) Supporting multiple optional behaviours. An example of the first use is a timer decorator that runs a function, outputs its execution time on the console, and returns the original function's result. An example of the second use is input type validation decorator that during running time tests whether the caller has passed input arguments of a particular class. Decorators can reduce execution time, say by memoization, or reduce bugs by adding defensive programming routines.

The goal of dndR is to provide a suite of Dungeons & Dragons related functions. This package is meant to be useful both to players and Dungeon Masters (DMs). Some functions apply to many tabletop role-playing games (e.g., dice rolling), but others are focused on Fifth Edition (a.k.a. "5e") and where possible both the 2014 and 2024 versions are supported.

Microsoft Word docx files provide an XML structure that is fairly straightforward to navigate, especially when it applies to Word tables and comments. Tools are provided to determine table count/structure, comment count and also to extract/clean tables and comments from Microsoft Word docx documents. There is also nascent support for .doc and .pptx files.

Visualize one-factor data frame. Beads plot consists of diamonds of each factor of each data series. A diamond indicates average and range. Look over a data frame with many numeric columns and a factor column.

DataSHIELD is an infrastructure and series of R packages that enables the remote and non-disclosive analysis of sensitive research data. This DataSHIELD Interface implementation is for analyzing datasets living in the current R session. The purpose of this is primarily for lightweight DataSHIELD analysis package development.

We consider a multiple testing procedure used in many modern applications which is the q-value method proposed by Storey and Tibshirani (2003), <doi:10.1073/pnas.1530509100>. The q-value method is based on the false discovery rate (FDR), hence versions of the q-value method can be defined depending on which estimator of the proportion of true null hypotheses, p0, is plugged in the FDR estimator. We implement the q-value method based on two classical pi0 estimators, and furthermore, we propose and implement three versions of the q-value method for homogeneous discrete uniform P-values based on pi0 estimators which take into account the discrete distribution of the P-values.

This dataset includes Background and Pathway data used in package DysPIA'.

Displays a terrible joke, the kind only dads crack.

This package provides tools for fitting Bayesian Distributed Lag Models (DLMs) to longitudinal response data that is a count or binary. Count data is fit using negative binomial regression and binary is fit using quantile regression. The contribution of the lags are fit via b-splines. In addition, infers the predictor inclusion uncertainty. Multimomial models are not supported. Based on Dempsey and Wyse (2025) <doi:10.48550/arXiv.2403.03646>.

This package implements maximum likelihood methods for evaluating the durability of vaccine efficacy in a randomized, placebo-controlled clinical trial with staggered enrollment of participants and potential crossover of placebo recipients before the end of the trial. Lin, D. Y., Zeng, D., and Gilbert, P. B. (2021) <doi:10.1093/cid/ciab226> and Lin, D. Y., Gu, Y., Zeng, D., Janes, H. E., and Gilbert, P. B. (2021) <doi:10.1093/cid/ciab630>.

This package provides methods for valuation of life insurance premiums and reserves (including variable-benefit and fractional coverage) based on "Actuarial Mathematics" by Bowers, H.U. Gerber, J.C. Hickman, D.A. Jones and C.J. Nesbitt (1997, ISBN: 978-0938959465), "Actuarial Mathematics for Life Contingent Risks" by Dickson, David C. M., Hardy, Mary R. and Waters, Howard R (2009) <doi:10.1017/CBO9780511800146> and "Life Contingencies" by Jordan, C. W (1952) <doi:10.1017/S002026810005410X>. It also contains functions for equivalent interest and discount rate calculation, present and future values of annuities, and loan amortization schedule.

Estimation of the average treatment effect when controlling for high-dimensional confounders using debiased inverse propensity score weighting (DIPW). DIPW relies on the propensity score following a sparse logistic regression model, but the regression curves are not required to be estimable. Despite this, our package also allows the users to estimate the regression curves and take the estimated curves as input to our methods. Details of the methodology can be found in Yuhao Wang and Rajen D. Shah (2020) "Debiased Inverse Propensity Score Weighting for Estimation of Average Treatment Effects with High-Dimensional Confounders" <arXiv:2011.08661>. The package relies on the optimisation software MOSEK <https://www.mosek.com/> which must be installed separately; see the documentation for Rmosek'.

Facilitate the analysis of teams in a corporate setting: assess the diversity per grade and job, present the results, search for bias (in hiring and/or promoting processes). It also provides methods to simulate the effect of bias, random team-data, etc. White paper: Philippe J.S. De Brouwer (2021) <http://www.de-brouwer.com/assets/div/div-white-paper.pdf>. Book (chapter 36): Philippe J.S. De Brouwer (2020, ISBN:978-1-119-63272-6) and Philippe J.S. De Brouwer (2020) <doi:10.1002/9781119632757>.

All datasets and functions required for the examples and exercises of the book "Data Science for Psychologists" (by Hansjoerg Neth, Konstanz University, 2025, <doi:10.5281/zenodo.7229812>), freely available at <https://bookdown.org/hneth/ds4psy/>. The book and corresponding courses introduce principles and methods of data science to students of psychology and other biological or social sciences. The ds4psy package primarily provides datasets, but also functions for data generation and manipulation (e.g., of text and time data) and graphics that are used in the book and its exercises. All functions included in ds4psy are designed to be explicit and instructive, rather than efficient or elegant.

Calculates expected values, variance, different moments (kth moment, truncated mean), stop-loss, mean excess loss, Value-at-Risk (VaR) and Tail Value-at-Risk (TVaR) as well as some density and cumulative (survival) functions of continuous, discrete and compound distributions. This package also includes a visual Shiny component to enable students to visualize distributions and understand the impact of their parameters. This package is intended to expand the stats package so as to enable students to develop an intuition for probability.

Distributed Online Covariance Matrix Tests Docovt is a powerful tool designed to efficiently process and analyze distributed datasets. It enables users to perform covariance matrix tests in an online, distributed manner, making it highly suitable for large-scale data analysis. By leveraging advanced computational techniques, Docovt ensures robust and scalable solutions for statistical analysis, particularly in scenarios where data is dispersed across multiple nodes or sources. This package is ideal for researchers and practitioners working with high-dimensional data, providing a flexible and efficient framework for covariance matrix estimation and hypothesis testing. The philosophy of Docovt is described in Guo G.(2025) <doi:10.1016/j.physa.2024.130308>.

Tool to print out the value of R objects/expressions while running an R script. Outputs can be made dependent on user-defined conditions/criteria. Debug messages only appear when a global option for debugging is set. This way, debugr code can even remain in the debugged code for later use without any negative effects during normal runtime.

Implementation of different statistical tools for the description and analysis of gene expression data based on the concept of data depth, namely, the scale curves for visualizing the dispersion of one or various groups of samples (e.g. types of tumors), a rank test to decide whether two groups of samples come from a single distribution and two methods of supervised classification techniques, the DS and TAD methods. All these techniques are based on the Modified Band Depth, which is a recent notion of depth with a low computational cost, what renders it very appropriate for high dimensional data such as gene expression data.

This package provides a tool developed with the Golem framework which provides an easier way to check cells differences between two data frames. The user provides two data frames for comparison, selects IDs variables identifying each row of input data, then clicks a button to perform the comparison. Several R package functions are used to describe the data and perform the comparison in the server of the application. The main ones are comparedf() from arsenal and skim() from skimr'. For more details see the description of comparedf() from the arsenal package and that of skim() from the skimr package.

This package provides a single function that supports the installation of all packages belonging to the dartRverse'. The dartRverse is a set of packages that work together to analyse SNP (single nuclear polymorphism) data. All packages aim to have a similar look and feel and are based on the same type of data structure ('genlight'), with additional metadata for loci and individuals (samples). For more information visit the GitHub pages <https://github.com/green-striped-gecko/dartRverse>.

This package provides a set of tools for relational and event analysis, including two- and one-mode network brokerage and structural measures, and helper functions optimized for relational event analysis with large datasets, including creating relational risk sets, computing network statistics, estimating relational event models, and simulating relational event sequences. For more information on relational event models, see Butts (2008) <doi:10.1111/j.1467-9531.2008.00203.x>, Lerner and Lomi (2020) <doi:10.1017/nws.2019.57>, Bianchi et al. (2024) <doi:10.1146/annurev-statistics-040722-060248>, and Butts et al. (2023) <doi:10.1017/nws.2023.9>. In terms of the structural measures in this package, see Leal (2025) <doi:10.1177/00491241251322517>, Burchard and Cornwell (2018) <doi:10.1016/j.socnet.2018.04.001>, and Fujimoto et al. (2018) <doi:10.1017/nws.2018.11>. This package was developed with support from the National Science Foundationâ s (NSF) Human Networks and Data Science Program (HNDS) under award number 2241536 (PI: Diego F. Leal). Any opinions, findings, and conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF.

Efficient procedures for fitting the DD-PCA (Ke et al., 2019, <arXiv:1906.00051>) by decomposing a large covariance matrix into a low-rank matrix plus a diagonally dominant matrix. The implementation of DD-PCA includes the convex approach using the Alternating Direction Method of Multipliers (ADMM) and the non-convex approach using the iterative projection algorithm. Applications of DD-PCA to large covariance matrix estimation and global multiple testing are also included in this package.

Constructs confidence regions without the need to know the sampling distribution of bivariate data. The method was proposed by Zhiqiu Hu & Rong-cai Yang (2013) <doi:10.1371/journal.pone.0081179.g001>.

This package provides a robust identification of differential binding sites method for analyzing ChIP-seq (Chromatin Immunoprecipitation Sequencing) comparing two samples that considers an ensemble of finite mixture models combined with a local false discovery rate (fdr) allowing for flexible modeling of data. Methods for Differential Identification using Mixture Ensemble (DIME) is described in: Taslim et al., (2011) <doi:10.1093/bioinformatics/btr165>.