Framework for creating and orchestrating data pipelines. Organize, orchestrate, and monitor multiple pipelines in a single project. Use tags to decorate functions with scheduling parameters and configuration.

Fits the Poisson-Tweedie generalized linear mixed model described in Signorelli et al. (2021, <doi:10.1177/1471082X20936017>). Likelihood approximation based on adaptive Gauss Hermite quadrature rule.

Statically determine and visualize the function dependencies within and across packages. This may be useful for managing function dependencies across a code base of multiple R packages.

Class definitions and constructors for pseudo-vectors containing all permutations, combinations and subsets of objects taken from a vector. Simplifies working with structures commonly encountered in combinatorics.

Differential analysis of tumor tissue immune cell type abundance based on RNA-seq gene-level expression from The Cancer Genome Atlas (TCGA; <https://pancanatlas.xenahubs.net>) database.

Density, distribution function, quantile function, and random generating function of the Unit-Garima distribution based on Ayuyuen, S., & Bodhisuwan, W. (2024)<doi:10.18187/pjsor.v20i1.4307>.

RAJA offers portable, parallel loop execution by providing building blocks that extend the generally-accepted parallel for idiom. RAJA relies on standard C++14 features.

An integrated solution to perform a series of text mining tasks such as importing and cleaning a corpus, and analyses like terms and documents counts, lexical summary, terms co-occurrences and documents similarity measures, graphs of terms, correspondence analysis and hierarchical clustering. Corpora can be imported from spreadsheet-like files, directories of raw text files, as well as from Dow Jones Factiva', LexisNexis', Europresse and Alceste files.

This package implements various Riemannian metrics for symmetric positive definite matrices, including AIRM (Affine Invariant Riemannian Metric, <doi:10.1007/s11263-005-3222-z>), Log-Euclidean (<doi:10.1002/mrm.20965>), Euclidean, Log-Cholesky (<doi:10.1137/18M1221084>), and Bures-Wasserstein metrics (<doi:10.1016/j.exmath.2018.01.002>). Provides functions for computing logarithmic and exponential maps, vectorization, and statistical operations on the manifold of positive definite matrices.

The goal of readsdr is to bridge the design capabilities from specialised System Dynamics software with the powerful numerical tools offered by R libraries. The package accomplishes this goal by parsing XMILE files ('Vensim and Stella') models into R objects to construct networks (graph theory); ODE functions for Stan'; and inputs to simulate via deSolve as described in Duggan (2016) <doi:10.1007/978-3-319-34043-2>.

Takes matched and unmatched data and calculates Rosenbaum bounds for the treatment effect. Calculates bounds for binary outcome data, Hodges-Lehmann point estimates, Wilcoxon signed-rank test for matched data and matched IV estimators, Wilcoxon sum rank test, and for data with multiple matched controls. The sensitivity analysis methods in this package are documented in Rosenbaum (2002) Observational Studies, <doi:10.1007/978-1-4757-3692-2>, Springer-Verlag.

Streamlines the interaction with the RCSB Protein Data Bank ('PDB') <https://www.rcsb.org/>. This interface offers an intuitive and powerful tool for searching and retrieving a diverse range of data types from the PDB'. It includes advanced functionalities like BLAST and sequence motif queries. Built upon the existing XML-based API of the PDB', it simplifies the creation of custom requests, thereby enhancing usability and flexibility for researchers.

Value-calibrated color ramps can be useful to emphasize patterns in data from complex distributions. Colors can be tied to specific values, and the association can be expanded into full color ramps that also include the relationship between colors and values. Such ramps can be used in a variety of cases when heatmap-type plots are necessary, including the visualization of vector and raster spatial data, such as topographies.

The metrics() function calculates measures of scholarly impact. These include conventional measures, such as the number of publications and the total citations to all publications, as well as modern and robust metrics based on the vector of citations associated with each publication, such as the h index and many of its variants or rivals. These methods are described in Ruscio et al. (2012) <DOI: 10.1080/15366367.2012.711147>.

This package provides a user-friendly interface for managing PostgreSQL database connection settings. The package supplies helper functions to create, edit and load connection and option configuration files stored in a user-specific directory using the odbc and RPostgres back ends. These helpers make it easy to construct a reproducible connection string from a configuration file, either by reading user-defined YAML files or by parsing an environment variable.

Robustness -- eXperimental', eXtraneous', or eXtraordinary Functionality for Robust Statistics. Hence methods which are not well established, often related to methods in package robustbase'. Amazingly, BACON()', originally by Billor, Hadi, and Velleman (2000) <doi:10.1016/S0167-9473(99)00101-2> has become established in places. The "barrow wheel" `rbwheel()` is from Stahel and Mächler (2009) <doi:10.1111/j.1467-9868.2009.00706.x>.

Use trend filtering, a type of regularized nonparametric regression, to estimate the instantaneous reproduction number, also called Rt. This value roughly says how many new infections will result from each new infection today. Values larger than 1 indicate that an epidemic is growing while those less than 1 indicate decline. For more details about this methodology, see Liu, Cai, Gustafson, and McDonald (2024) <doi:10.1371/journal.pcbi.1012324>.

This package computes model II simple linear regression using ordinary least squares (OLS), major axis (MA), standard major axis (SMA), and ranged major axis (RMA).

This lightweight package that adds progress bar to vectorized R functions apply. The implementation can easily be added to functions where showing the progress is useful e.g. bootstrap.

Alabama stands for Augmented Lagrangian Adaptive Barrier Minimization Algorithm; it is used for optimizing smooth nonlinear objective functions with constraints. Linear or nonlinear equality and inequality constraints are allowed.

This package implements the R version of the log4j package. It offers hierarchic loggers, multiple handlers per logger, level based filtering, space handling in messages and custom formatting.

This package provides tools to create and modify network objects. The network class can represent a range of relational data types, and supports arbitrary vertex/edge/graph attributes.

This package lets you easily use Bootstrap icons inside Shiny apps and R Markdown documents. More generally, icons can be inserted in any htmltools document through inline SVG.

This package fits generalized linear models efficiently using RcppEigen'. The iteratively reweighted least squares implementation utilizes the step-halving approach of Marschner to help safeguard against convergence issues.