The superdiag package provides a comprehensive test suite for testing Markov Chain nonconvergence. It integrates five standard empirical MCMC convergence diagnostics (Gelman-Rubin, Geweke, Heidelberger-Welch, Raftery-Lewis, and Hellinger distance) and plotting functions for trace plots and density histograms. The functions of the package can be used to present all diagnostic statistics and graphs at once for conveniently checking MCMC nonconvergence.

Determine the path of the executing script. Compatible with several popular GUIs: Rgui', RStudio', Positron', VSCode', Jupyter', Emacs', and Rscript (shell). Compatible with several functions and packages: source()', sys.source()', debugSource() in RStudio', compiler::loadcmp()', utils::Sweave()', box::use()', knitr::knit()', plumber::plumb()', shiny::runApp()', package:targets', and testthat::source_file()'.

This package contains several utility functions for manipulating tensor-valued data (centering, multiplication from a single mode etc.) and the implementations of the following blind source separation methods for tensor-valued data: tPCA', tFOBI', tJADE', k-tJADE', tgFOBI', tgJADE', tSOBI', tNSS.SD', tNSS.JD', tNSS.TD.JD', tPP and tTUCKER'.

This package provides a collection of high-performance functions for the triangular distribution that consists of the probability density function, cumulative distribution function, quantile function, random variate generator, moment generating function, characteristic function, and expected shortfall function. References: Samuel Kotz, Johan Ren Van Dorp (2004) <doi:10.1142/5720> and Acerbi, Carlo and Tasche, Dirk. (2002) <doi:10.1111/1468-0300.00091>.

Efficient tabulation with Stata-like output. For each unique value of the variable, it shows the number of observations with that value, proportion of observations with that value, and cumulative proportion, in descending order of frequency. Accepts data.table, tibble, or data.frame as input. Efficient with big data: if you give it a data.table, tab() uses data.table syntax.

Non- and semiparametric regression for generalized additive, partial linear, and varying coefficient models as well as their combinations via smoothed backfitting. Based on Roca-Pardinas J and Sperlich S (2010) <doi:10.1007/s11222-009-9130-2>; Mammen E, Linton O and Nielsen J (1999) <doi:10.1214/aos/1017939138>; Lee YK, Mammen E, Park BU (2012) <doi:10.1214/12-AOS1026>.

This package contains infrastructure for benchmarking analysis methods and access to single cell mixture benchmarking data. It provides a framework for organising analysis methods and testing combinations of methods in a pipeline without explicitly laying out each combination. It also provides utilities for sampling and filtering SingleCellExperiment objects, constructing lists of functions with varying parameters, and multithreaded evaluation of analysis methods.

`orthogene` is an R package for easy mapping of orthologous genes across hundreds of species. It pulls up-to-date gene ortholog mappings across **700+ organisms**. It also provides various utility functions to aggregate/expand common objects (e.g. data.frames, gene expression matrices, lists) using **1:1**, **many:1**, **1:many** or **many:many** gene mappings, both within- and between-species.

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.

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.

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.

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.

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>.

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.

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>.

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.