Convert text (and text in R objects) to Mocking SpongeBob case <https://knowyourmeme.com/memes/mocking-spongebob> and show them off in fun ways. CoNVErT TexT (AnD TeXt In r ObJeCtS) To MOCkINg SpoNgebOb CAsE <https://knowyourmeme.com/memes/mocking-spongebob> aND shOw tHem OFf IN Fun WayS.

This package implements a suite of sensitivity analysis tools that extends the traditional omitted variable bias framework and makes it easier to understand the impact of omitted variables in regression models, as discussed in Cinelli, C. and Hazlett, C. (2020), "Making Sense of Sensitivity: Extending Omitted Variable Bias." Journal of the Royal Statistical Society, Series B (Statistical Methodology) <doi:10.1111/rssb.12348>.

Construct sketches of data via random subspace embeddings. For more details, see the following papers. Lee, S. and Ng, S. (2022). "Least Squares Estimation Using Sketched Data with Heteroskedastic Errors," Proceedings of the 39th International Conference on Machine Learning (ICML22), 162:12498-12520. Lee, S. and Ng, S. (2020). "An Econometric Perspective on Algorithmic Subsampling," Annual Review of Economics, 12(1): 45â 80.

This package provides an R interface for interacting with the Tableau Server. It allows users to perform various operations such as publishing workbooks, refreshing data extracts, and managing users using the Tableau REST API (see <https://help.tableau.com/current/api/rest_api/en-us/REST/rest_api_ref.htm> for details). Additionally, it includes functions to perform manipulations on local Tableau workbooks.

Bayesian wavelet denoising using a resolution-adaptive spike-and-slab prior whose continuous slab is a mixture of a compactly supported Wendland-type density and the semicircle density. The package provides Gaussian and Laplace working-likelihood versions, empirical-Bayes fitting of resolution-specific slab weights, robust noise-scale estimation, posterior-mean coefficient shrinkage, reconstruction by the inverse discrete wavelet transform, and diagnostic tools.

This package performs robust estimation and inference when using covariate adjustment and/or covariate-adaptive randomization in randomized controlled trials. This package is trimmed to reduce the dependencies and validated to be used across industry. See "FDA's final guidance on covariate adjustment"<https://www.regulations.gov/docket/FDA-2019-D-0934>, Tsiatis (2008) <doi:10.1002/sim.3113>, Bugni et al. (2018) <doi:10.1080/01621459.2017.1375934>, Ye, Shao, Yi, and Zhao (2023)<doi:10.1080/01621459.2022.2049278>, Ye, Shao, and Yi (2022)<doi:10.1093/biomet/asab015>, Rosenblum and van der Laan (2010)<doi:10.2202/1557-4679.1138>, Wang et al. (2021)<doi:10.1080/01621459.2021.1981338>, Ye, Bannick, Yi, and Shao (2023)<doi:10.1080/24754269.2023.2205802>, and Bannick, Shao, Liu, Du, Yi, and Ye (2024)<doi:10.48550/arXiv.2306.10213>.

The ropenblas package (<https://prdm0.github.io/ropenblas/>) is useful for users of any GNU/Linux distribution. It will be possible to download, compile and link the OpenBLAS library (<https://www.openblas.net/>) with the R language, always by the same procedure, regardless of the GNU/Linux distribution used. With the ropenblas package it is possible to download, compile and link the latest version of the OpenBLAS library even the repositories of the GNU/Linux distribution used do not include the latest versions of OpenBLAS'. If of interest, older versions of the OpenBLAS library may be considered. Linking R with an optimized version of BLAS (<https://netlib.org/blas/>) may improve the computational performance of R code. The OpenBLAS library is an optimized implementation of BLAS that can be easily linked to R with the ropenblas package.

bioassayR is a computational tool that enables simultaneous analysis of thousands of bioassay experiments performed over a diverse set of compounds and biological targets. Unique features include support for large-scale cross-target analyses of both public and custom bioassays, generation of high throughput screening fingerprints (HTSFPs), and an optional preloaded database that provides access to a substantial portion of publicly available bioactivity data.

This package provides tools to convert plot function calls (using expression or formula) to grob or ggplot objects that are compatible with the grid and ggplot2 environment. With this package, we are able to e.g. use cowplot to align plots produced by base graphics, grid, lattice, vcd etc. by converting them to ggplot objects.

This package provides a minimal, unifying API for scripts and packages to report progress updates from anywhere including when using parallel processing. The package is designed such that the developer can to focus on what progress should be reported on without having to worry about how to present it. The end user has full control of how, where, and when to render these progress updates.

This package provides a graphical user interface for interactive Markov chain Monte Carlo (MCMC) diagnostics and plots and tables helpful for analyzing a posterior sample. The interface is powered by the Shiny web application framework and works with the output of MCMC programs written in any programming language (and has extended functionality for Stan models fit using the rstan and rstanarm packages).

ExperimentHubData package for the mulea comprehensive overrepresentation and functional enrichment analyser R package. Here we provide ontologies (gene sets) in a data.frame for 27 different organisms, ranging from Escherichia coli to human, all acquired from publicly available data sources. Each ontology is provided with multiple gene and protein identifiers. Please see the NEWS file for a list of changes in each version.

Phantasus is a web-application for visual and interactive gene expression analysis. Phantasus is based on Morpheus – a web-based software for heatmap visualisation and analysis, which was integrated with an R environment via OpenCPU API. Aside from basic visualization and filtering methods, R-based methods such as k-means clustering, principal component analysis or differential expression analysis with limma package are supported.

Leveraging Monte Carlo simulations, this package provides tools for diagnosing regression models. It implements a parametric bootstrap framework to compute statistics, generates diagnostic envelopes to assess goodness-of-fit, and evaluates type I error control for Wald tests. By simulating data under the assumption that the model is true, it helps to identify model mis-specifications and enhances the reliability of the model inferences.

Design and analysis of one- and two-stage binomial clinical phase II trials using Bayes factors. Implements Bayes factors for point-null and directional hypotheses, predictive densities under different hypotheses, and power and sample size calibration. Both one-arm trials with only a single treatment arm and two-arm trials with treatment and control arm are implemented for the one- and two-stage designs.

This package provides methods for the group testing identification problem: 1) Operating characteristics (e.g., expected number of tests) for commonly used hierarchical and array-based algorithms, and 2) Optimal testing configurations for these same algorithms. Methods for the group testing estimation problem: 1) Estimation and inference procedures for an overall prevalence, and 2) Regression modeling for commonly used hierarchical and array-based algorithms.

This package implements Conditional Minimax Regret design rules for pilot-informed experimental assignment, as proposed in Yamin (2026) <doi:10.48550/arXiv.2607.16982>. Includes two-arm, unbounded two-arm, multi-arm, stratified, multiple-outcome, proxy-outcome, and pilot-planning tools. Variance bounds include Maurer and Pontil (2009) <doi:10.48550/arXiv.0907.3740> empirical-Bernstein bounds and exact Bernoulli confidence bounds.

Reads demographic data from a variety of public data sources, extracting and harmonizing variables useful for the study of childfree individuals. The identification of childfree individuals and those with other family statuses uses Neal & Neal's (2024) "A Framework for Studying Adults who Neither have Nor Want Children" <doi:10.1177/10664807231198869>; A pre-print is available at <doi:10.31234/osf.io/fa89m>.

Using these tools to simplify the research process of political science and other social sciences. The current version can create folder system for academic project in political science, calculate psychological trait scores, visualize experimental and spatial data, set up color-blind palette, and test for Type I error (false positives) in Qualitative Comparative Analysis (QCA) for crisp-set, multi-value, and fuzzy-set variants.

Reads European Data Format files EDF and EDF+, see <http://www.edfplus.info>, BioSemi Data Format files BDF, see <http://www.biosemi.com/faq/file_format.htm>, and BDF+ files, see <http://www.teuniz.net/edfbrowser/bdfplus%20format%20description.html>. The files are read in two steps: first the header is read and then the signals (using the header object as a parameter).

Create publication-ready regression tables in multiple formats, including Word', HTML', LaTeX', and PDF', from statistical models. Supports lm() and glm() models. Includes options for marginal effects, control variable grouping, and robust standard errors using methods described in Zeileis (2004) <doi:10.18637/jss.v011.i10>. Tables can be exported to Word via flextable or to LaTeX for PDF output.

Estimation of mixed models including a subject-specific variance which can be time and covariate dependent. In the joint model framework, the package handles left truncation and allows a flexible dependence structure between the competing events and the longitudinal marker. The estimation is performed under the frequentist framework, using the Marquardt-Levenberg algorithm. (Courcoul, Tzourio, Woodward, Barbieri, Jacqmin-Gadda (2023) <arXiv:2306.16785>).

Understanding how features influence a specific response variable becomes crucial in classification problems, with applications ranging from medical diagnosis to customer behavior analysis. This packages provides tools to compute such an influence measure grounded on game theory concepts. In particular, the influence measures presented in Davila-Pena, Saavedra-Nieves, and Casas-Méndez (2024) <doi:10.48550/arXiv.2408.02481> can be obtained.

Advanced methods for a valuable quantitative environmental risk assessment using Bayesian inference of survival Data with toxicokinetics toxicodynamics (TKTD) models. Among others, it facilitates Bayesian inference of the general unified threshold model of survival (GUTS). See models description in Jager et al. (2011) <doi:10.1021/es103092a> and implementation using Bayesian inference in Baudrot and Charles (2019) <doi:10.1038/s41598-019-47698-0>.