This package provides functions to build tables with advanced layout elements such as row spanners, column spanners, table spanners, zebra striping, and more. While allowing advanced layout, the underlying CSS-structure is simple in order to maximize compatibility with word processors such as LibreOffice. The package also contains a few text formatting functions that help outputting text compatible with HTML or LaTeX.

This package provides ggplot2 geoms filled with various patterns. It includes a patterned version of every ggplot2 geom that has a region that can be filled with a pattern. It provides a suite of ggplot2 aesthetics and scales for controlling pattern appearances. It supports over a dozen builtin patterns (every pattern implemented by gridpattern) as well as allowing custom user-defined patterns.

OOMPA offers R packages for gene expression and proteomics analysis. OOMPA uses S4 classes to construct object-oriented tools with a consistent user interface. All higher level analysis tools in OOMPA are compatible with the eSet classes defined in BioConductor. The lower level processing tools offer an alternative to parts of BioConductor, but can also be used to enhance existing BioConductor packages.

Provides implementations of functions which have been introduced in R since version 3.0.0. The backports are conditionally exported which results in R resolving the function names to the version shipped with R (if available) and uses the implemented backports as fallback. This way package developers can make use of the new functions without worrying about the minimum required R version.

RawTherapee is a raw image processing suite. It comprises a subset of image editing operations specifically aimed at non-destructive raw photo post-production and is primarily focused on improving a photographer's workflow by facilitating the handling of large numbers of images. Most raw formats are supported, including Pentax Pixel Shift, Canon Dual-Pixel, and those from Foveon and X-Trans sensors.

RegulonDB has collected, harmonized and centralized data from hundreds of experiments for nearly two decades and is considered a point of reference for transcriptional regulation in Escherichia coli K12. Here, we present the regutools R package to facilitate programmatic access to RegulonDB data in computational biology. regutools provides researchers with the possibility of writing reproducible workflows with automated queries to RegulonDB. The regutools package serves as a bridge between RegulonDB data and the Bioconductor ecosystem by reusing the data structures and statistical methods powered by other Bioconductor packages. We demonstrate the integration of regutools with Bioconductor by analyzing transcription factor DNA binding sites and transcriptional regulatory networks from RegulonDB. We anticipate that regutools will serve as a useful building block in our progress to further our understanding of gene regulatory networks.

This package provides functions for the Bayesian analysis of extreme value models. The rust package <https://cran.r-project.org/package=rust> is used to simulate a random sample from the required posterior distribution. The functionality of revdbayes is similar to the evdbayes package <https://cran.r-project.org/package=evdbayes>, which uses Markov Chain Monte Carlo ('MCMC') methods for posterior simulation. In addition, there are functions for making inferences about the extremal index, using the models for threshold inter-exceedance times of Suveges and Davison (2010) <doi:10.1214/09-AOAS292> and Holesovsky and Fusek (2020) <doi:10.1007/s10687-020-00374-3>. Also provided are d,p,q,r functions for the Generalised Extreme Value ('GEV') and Generalised Pareto ('GP') distributions that deal appropriately with cases where the shape parameter is very close to zero.

Estimates heterogeneous effects in factorial (and conjoint) models. The methodology employs a Bayesian finite mixture of regularized logistic regressions, where moderators can affect each observation's probability of group membership and a sparsity-inducing prior fuses together levels of each factor while respecting ANOVA-style sum-to-zero constraints. Goplerud, Imai, and Pashley (2024) <doi:10.48550/ARXIV.2201.01357> provide further details.

Estimate natural mortality (M) throughout the life history for organisms, mainly fish and invertebrates, based on gnomonic interval approach proposed by Caddy (1996) <doi:10.1051/alr:1996023> and Martinez-Aguilar et al. (2005) <doi:10.1016/j.fishres.2004.04.008>. It includes estimation of duration of each gnomonic interval (life stage), the constant probability of death (G), and some basic plots.

Two-Step Lasso (TS-Lasso) and compound minimum methods to recover the abundance of missing peaks in mass spectrum analysis. TS-Lasso is an imputation method that handles various types of missing peaks simultaneously. This package provides the procedure to generate missing peaks (or data) for simulation study, as well as a tool to estimate and visualize the proportion of missing at random.

Fast scalable Gaussian process approximations, particularly well suited to spatial (aerial, remote-sensed) and environmental data, described in more detail in Katzfuss and Guinness (2017) <arXiv:1708.06302>. Package also contains a fast implementation of the incomplete Cholesky decomposition (IC0), based on Schaefer et al. (2019) <arXiv:1706.02205> and MaxMin ordering proposed in Guinness (2018) <arXiv:1609.05372>.

This package implements a one-sector Armington-CES gravity model with general equilibrium (GE) effects. This model is designed to analyze international and domestic trade by capturing the impacts of trade costs and policy changes within a general equilibrium framework. Additionally, it includes a local parameter to run simulations on productivity. The package provides functions for calibration, simulation, and analysis of the model.

Computes bilateral and multilateral index numbers. It has support for many standard bilateral indexes as well as multilateral index number methods such as GEKS, GEKS-Tornqvist (or CCDI), Geary-Khamis and the weighted time product dummy (for details on these methods see Diewert and Fox (2020) <doi:10.1080/07350015.2020.1816176>). It also supports updating of multilateral indexes using several splicing methods.

An implementation of corrected sandwich variance (CSV) estimation method for making inference of marginal hazard ratios (HR) in inverse probability weighted (IPW) Cox model without and with clustered data, proposed by Shu, Young, Toh, and Wang (2019) in their paper under revision for Biometrics. Both conventional inverse probability weights and stabilized weights are implemented. Logistic regression model is assumed for propensity score model.

Analysis of DNA copy number in single cells using custom genome-wide targeted DNA sequencing panels for the Mission Bio Tapestri platform. Users can easily parse, manipulate, and visualize datasets produced from the automated Tapestri Pipeline', with support for normalization, clustering, and copy number calling. Functions are also available to deconvolute multiplexed samples by genotype and parsing barcoded reads from exogenous lentiviral constructs.

Stanford CoreNLP annotation client. Stanford CoreNLP <https://stanfordnlp.github.io/CoreNLP/index.html> integrates all NLP tools from the Stanford Natural Language Processing Group, including a part-of-speech (POS) tagger, a named entity recognizer (NER), a parser, and a coreference resolution system, and provides model files for the analysis of English. More information can be found in the README.

Splits initial strata into refined strata that optimize covariate balance. For more information, please email the author for a copy of the accompanying manuscript. To solve the linear program, the Gurobi commercial optimization software is recommended, but not required. The gurobi R package can be installed following the instructions at <https://www.gurobi.com/documentation/9.1/refman/ins_the_r_package.html>.

Generates predicted stage change days for an insect, based on daily temperatures and development rate parameters, as developed by Pollard (2014) <http://mural.maynoothuniversity.ie/view/ethesisauthor/Pollard=3ACiaran_P=2E=3A=3A.html>. A few example datasets are included and implemented for P. vulgatissima, the blue willow beetle, but the approach can be readily applied to other species that display similar behaviour.

This package contains statistical inference tools applied to Partial Linear Regression (PLR) models. Specifically, point estimation, confidence intervals estimation, bandwidth selection, goodness-of-fit tests and analysis of covariance are considered. Kernel-based methods, combined with ordinary least squares estimation, are used and time series errors are allowed. In addition, these techniques are also implemented for both parametric (linear) and nonparametric regression models.

We innovatively defined a pathway mutation accumulate perturbation score (PMAPscore) to reflect the position and the cumulative effect of the genetic mutations at the pathway level. Based on the PMAPscore of pathways, identified prognosis-related pathways altered by somatic mutation and predict immunotherapy efficacy by constructing a multiple-pathway-based risk model (Tarca, Adi Laurentiu et al (2008) <doi:10.1093/bioinformatics/btn577>).

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.