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>.

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.

DNAfusion can identify gene fusions such as EML4-ALK based on paired-end sequencing results. This package was developed using position deduplicated BAM files generated with the AVENIO Oncology Analysis Software. These files are made using the AVENIO ctDNA surveillance kit and Illumina Nextseq 500 sequencing. This is a targeted hybridization NGS approach and includes ALK-specific but not EML4-specific probes.

This package provides functions to visualise webs and calculate a series of indices commonly used to describe pattern in (ecological) webs. It focuses on webs consisting of only two levels (bipartite), e.g. pollination webs or predator-prey-webs. Visualisation is important to get an idea of what we are actually looking at, while the indices summarise different aspects of the web's topology.

This package provides tools for fitting possibly high dimensional penalized regression models. The penalty structure can be any combination of an L1 penalty (lasso and fused lasso), an L2 penalty (ridge) and a positivity constraint on the regression coefficients. The supported regression models are linear, logistic and Poisson regression and the Cox Proportional Hazards model. Cross-validation routines allow optimization of the tuning parameters.

This package provides tools to compute and represent gene set enrichment or depletion from your data based on pre-saved maps from the Atlas of Cancer Signalling Networks (ACSN) or user imported maps. The gene set enrichment can be run with hypergeometric test or Fisher exact test, and can use multiple corrections. Visualization of data can be done either by barplots or heatmaps.

This package provides functions to work with date-times and time-spans: fast and user friendly parsing of date-time data, extraction and updating of components of a date-time (years, months, days, hours, minutes, and seconds), algebraic manipulation on date-time and time-span objects. The lubridate package has a consistent and memorable syntax that makes working with dates easy and fun.

The rcs package utilizes the inclusion of RCS supplied data in LaTeX documents. In particular, you can easily access values of every RCS field in your document put the checkin date on the titlepage or put RCS fields in a footline. You can also typeset revision logs. You can also configure the rcs package easily to do special things for any keyword.

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.

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.

The bupaverse is an open-source, integrated suite of R-packages for handling and analysing business process data, developed by the Business Informatics research group at Hasselt University, Belgium. Profoundly inspired by the tidyverse package, the bupaverse package is designed to facilitate the installation and loading of multiple bupaverse packages in a single step. Learn more about bupaverse at the <https://bupar.net> homepage.

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.

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>.

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).

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>.

The Poisson-lognormal model and variants (Chiquet, Mariadassou and Robin, 2021 <doi:10.3389/fevo.2021.588292>) can be used for a variety of multivariate problems when count data are at play, including principal component analysis for count data, discriminant analysis, model-based clustering and network inference. Implements variational algorithms to fit such models accompanied with a set of functions for visualization and diagnostic.

Tests one hypothesis with several test statistics, correcting for multiple testing. The central function in the package is testtwice(). In a sensitivity analysis, the method has the largest design sensitivity of its component tests. The package implements the method and examples in Rosenbaum, P. R. (2012) <doi:10.1093/biomet/ass032> Testing one hypothesis twice in observational studies. Biometrika, 99(4), 763-774.

Attaches a set of packages commonly used for spatial plotting with tmap'. It includes tmap and its extensions ('tmap.glyphs', tmap.networks', tmap.cartogram', tmap.mapgl'), as well as supporting spatial data packages ('sf', stars', terra') and cols4all for exploring color palettes. The collection is designed for thematic mapping workflows and does not include the full set of packages from the R-spatial ecosystem.

Construct a Canonical Variate Analysis Biplot via the Generalised Singular Value Decomposition, for cases when the number of samples is less than the number of variables. For more information on biplots, see Gower JC, Lubbe SG, Le Roux NJ (2011) <doi:10.1002/9780470973196> and for more information on the generalised singular value decomposition, see Edelman A, Wang Y (2020) <doi:10.1137/18M1234412>.

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.