This package provides tools for spectral clustering of weighted directed networks using motif adjacency matrices. Methods perform well on large and sparse networks, and random sampling methods for generating weighted directed networks are also provided. Based on methodology detailed in Underwood, Elliott and Cucuringu (2020) <arXiv:2004.01293>.

Perform classic chi-squared tests and Ripol et al(1999) binomial confidence interval approach for autopolyploid dominant markers. Also, dominant markers may be generated for families of offspring where either one or both of the parents possess the marker. Missing values and misclassified markers may be generated at random.

Dictionary-like reference for computing scoring rules in a wide range of situations. Covers both parametric forecast distributions (such as mixtures of Gaussians) and distributions generated via simulation. Further details can be found in the package vignettes <doi:10.18637/jss.v090.i12>, <doi:10.18637/jss.v110.i08>.

This package provides a comprehensive logging framework for R applications that provides hierarchical logging levels, database integration, and contextual logging capabilities. The package supports SQLite storage for persistent logs, provides colour-coded console output for better readability, includes parallel processing support, and implements structured error reporting with JSON formatting.

This package adductomicsR processes data generated by the second stage of mass spectrometry (MS2) to identify potentially adducted peptides from spectra that has been corrected for mass drift and retention time drift and quantifies level mass spectral peaks from first stage of mass spectrometry (MS1) data.

Functionality for performing a principled reference analysis in the Bayesian normal-normal hierarchical model used for Bayesian meta-analysis, as described in Ott, Plummer and Roos (2021) <doi:10.1002/sim.9076>. Computes a reference posterior, induced by a minimally informative improper reference prior for the between-study (heterogeneity) standard deviation. Determines additional proper anti-conservative (and conservative) prior benchmarks. Includes functions for reference analyses at both the posterior and the prior level, which, given the data, quantify the informativeness of a heterogeneity prior of interest relative to the minimally informative reference prior and the proper prior benchmarks. The functions operate on data sets which are compatible with the bayesmeta package.

Given the parameters of a distribution, the package uses the concept of alpha-outliers by Davies and Gather (1993) to flag outliers in a data set. See Davies, L.; Gather, U. (1993): The identification of multiple outliers, JASA, 88 423, 782-792, <doi:10.1080/01621459.1993.10476339> for details.

Enables creation of visualizations using the CanvasXpress framework in R. CanvasXpress is a standalone JavaScript library for reproducible research with complete tracking of data and end-user modifications stored in a single PNG image that can be played back. See <https://www.canvasxpress.org> for more information.

This package provides functions for obtaining estimates of the parameter of the niche preemption model (also known as the geometric series), in particular a maximum likelihood estimator (Graffelman, 2021) <doi:10.1101/2021.01.27.428381>. The niche preemption model is a widely used model in ecology and biodiversity studies.

Utilize the CDF penalty function to estimate a penalized linear model. It enables you to display some graphical representations and determine whether the Karush-Kuhn-Tucker conditions are met. For more details about the theory, please refer to Cuntrera, D., Augugliaro, L., & Muggeo, V. M. (2022) <arXiv:2212.08582>.

Provide various functions and tools to help fit models for estimating treatment effects in stepped wedge cluster randomized trials. Implements methods described in Kenny, Voldal, Xia, and Heagerty (2022) "Analysis of stepped wedge cluster randomized trials in the presence of a time-varying treatment effect", <doi:10.1002/sim.9511>.

Troubleshooting reactive data in shiny can be difficult. These functions will convert reactive data frames into functions and load all assigned objects into your local environment. If you create a dummy input object, as the function will suggest, you will be able to test your server and ui functions interactively.

This package provides a convenient interface for formatting SQL queries directly within R'. It acts as a wrapper around the sql_format Rust crate. The package allows you to format SQL code with customizable options, including indentation, case formatting, and more, ensuring your SQL queries are clean, readable, and consistent.

TidyTuesday is a project by the Data Science Learning Community in which they post a weekly dataset in a public data repository (<https://github.com/rfordatascience/tidytuesday>) for people to analyze and visualize. This package provides the tools to easily download this data and the description of the source.

Bioconductor has a rich ecosystem of metadata around packages, usage, and build status. This package is a simple collection of functions to access that metadata from R. The goal is to expose metadata for data mining and value-added functionality such as package searching, text mining, and analytics on packages.

A workflow is a combination of a model and preprocessors (e.g, a formula, recipe, etc.). In order to try different combinations of these, an object can be created that contains many workflows. There are functions to create workflows en masse as well as training them and visualizing the results.

This package provides a string allocator for allocating many write-once strings. This library is primarily useful for parsing where you need to repeatedly build many strings, use them, and then throw them away. Instead of allocating many independent strings, this library will put them all in the same buffer.

Fast generators and iterators for permutations, combinations, integer partitions and compositions. The arrangements are in lexicographical order and generated iteratively in a memory efficient manner. It has been demonstrated that arrangements outperforms most existing packages of similar kind. Benchmarks could be found at <https://randy3k.github.io/arrangements/articles/benchmark.html>.

Estimation of average treatment effects (ATE) of point interventions on time-to-event outcomes with K competing risks (K can be 1). The method uses propensity scores and inverse probability weighting for emulation of baseline randomization, which is described in Charpignon et al. (2022) <doi:10.1038/s41467-022-35157-w>.

Pull raw and pre-cleaned versions of national and state-level COVID-19 time-series data from covid19india.org <https://www.covid19india.org>. Easily obtain and merge case count data, testing data, and vaccine data. Also assists in calculating the time-varying effective reproduction number with sensible parameters for COVID-19.

This package provides methods for estimating causal effects in the presence of interference described in B. Saul and M. Hugdens (2017) <doi:10.18637/jss.v082.i02>. Currently it implements the inverse-probability weighted (IPW) estimators proposed by E.J. Tchetgen Tchetgen and T.J. Vanderweele (2012) <doi:10.1177/0962280210386779>.

Package to carry out merged block randomization (Van der Pas (2019), <doi:10.1177/1740774519827957>), a restricted randomization method designed for small clinical trials (at most 100 subjects) or trials with small strata, for example in multicentre trials. It can be used for more than two groups or unequal randomization ratios.

Exports two functions implementing multi-way clustering using the method suggested by Cameron, Gelbach, & Miller (2011) and cluster (or block) bootstrapping for estimating variance-covariance matrices. Normal one and two-way clustering matches the results of other common statistical packages. Missing values are handled transparently and rudimentary parallelization support is provided.

The ESTIMATE package infers tumor purity from expression data as a function of immune and stromal infiltrate, but requires writing of intermediate files, is un-pipeable, and performs poorly when presented with modern datasets with current gene symbols. tidyestimate a fast, tidy, modern reimagination of ESTIMATE (2013) <doi:10.1038/ncomms3612>.