Enhances dynamic effect plots as suggested in Freyaldenhoven and Hansen (2026) <https://simonfreyaldenhoven.github.io/papers/Plausible_bounds.pdf>. Data-driven smoothing delivers a smooth estimated path with potentially improved point estimation properties and confidence regions covering a surrogate that can be substantially tighter than conventional pointwise or uniform bands.

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.

Using spatial or bulk gene expression data, estimates abundance of mixed cell types within each observation. Based on "Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data", Danaher (2022). Designed for use with the NanoString GeoMx platform, but applicable to any gene expression data.

This package is developed for facilitating parallel computing in R. It is capable to create an R object in the shared memory space and share the data across multiple R processes. It avoids the overhead of memory dulplication and data transfer, which make sharing big data object across many clusters possible.

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>.

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.

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>.

The dentomedical package provides a comprehensive suite of tools for medical and dental research. It includes automated descriptive statistics, bivariate analysis with intelligent test selection, logistic regression, and diagnostic accuracy assessment. All functions generate publication-ready tables using flextable', ensuring reproducibility and clarity suitable for manuscripts, reports, and clinical research workflows.

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>.

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.

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.

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>.

This package provides scalogram based wavelet tools for time series analysis: wavelet power spectrum, scalogram, windowed scalogram, windowed scalogram difference (see Bolos et al. (2017) <doi:10.1016/j.amc.2017.05.046>), scale index and windowed scale index (Benitez et al. (2010) <doi:10.1016/j.camwa.2010.05.010>).

This package provides an interface to vinecopulib', a C++ library for vine copula modeling. The rvinecopulib package implements the core features of the popular VineCopula package, in particular inference algorithms for both vine copula and bivariate copula models. Advantages over VineCopula are a sleeker and more modern API, improved performances, especially in high dimensions, nonparametric and multi-parameter families, and the ability to model discrete variables. The rvinecopulib package includes vinecopulib as header-only C++ library (currently version 0.7.2). Thus users do not need to install vinecopulib itself in order to use rvinecopulib'. Since their initial releases, vinecopulib is licensed under the MIT License, and rvinecopulib is licensed under the GNU GPL version 3.

Emacs Org Roam is a solution for taking non-hierarchical notes with Org mode. Notes are captured without hierarchy and are connected by tags. Notes can be found and created quickly. Org Roam should also work as a plug-and-play solution for anyone already using Org mode for their personal wiki.

Emacs Org Roam is a solution for taking non-hierarchical notes with Org mode. Notes are captured without hierarchy and are connected by tags. Notes can be found and created quickly. Org Roam should also work as a plug-and-play solution for anyone already using Org mode for their personal wiki.

The pip-run command provides on-demand temporary package installation for a single interpreter run. It replaces this series of commands:

$ virtualenv --python pythonX.X --system-site-packages /tmp/env
$ /tmp/env/bin/pip install pkg1 pkg2 -r reqs.txt
$ /tmp/env/bin/python ...
$ rm -rf /tmp/env

CalibraCurve is a computational tool designed to generate calibration curves for targeted mass spectrometry-based quantitative data. It is applicable to various omics disciplines, including proteomics, lipidomics, and metabolomics. The package also offers functionalities for data and calibration curve visualization and concentration prediction from new datasets based on the established curves.

MetaboSignal is an R package that allows merging, analyzing and customizing metabolic and signaling KEGG pathways. It is a network-based approach designed to explore the topological relationship between genes (signaling- or enzymatic-genes) and metabolites, representing a powerful tool to investigate the genetic landscape and regulatory networks of metabolic phenotypes.

NormalyzerDE provides screening of normalization methods for LC-MS based expression data. It calculates a range of normalized matrices using both existing approaches and a novel time-segmented approach, calculates performance measures and generates an evaluation report. Furthermore, it provides an easy utility for Limma- or ANOVA- based differential expression analysis.