This package provides tools for planning and simulating recurrent event trials with overdispersed count endpoints analyzed using negative binomial (or Poisson) rate models. Implements sample size and power calculations for fixed designs with variable accrual, dropout, maximum follow-up, and event gaps, including methods of Zhu and Lakkis (2014) <doi:10.1002/sim.5947> and Friede and Schmidli (2010) <doi:10.3414/ME09-02-0060>. Supports group sequential designs by adding calendar-time analysis schedules compatible with the gsDesign package and by estimating blinded information at interim looks. Includes simulation utilities for recurrent events (including seasonal rates), interim data truncation, and Wald-based inference for treatment rate ratios.

This package provides diagnostic graphic tools for GLMs, beta-binomial regression model (estimated by VGAM package), beta regression model (estimated by betareg package) and negative binomial regression model (estimated by MASS package). Since most of functions implemented in glmxdiag already exist in other packages, the aim is to provide the user unique functions that work on almost all regression models previously specified. Details about some of the implemented functions can be found in Brown (1992) <doi:10.2307/2347617>, Dunn and Smyth (1996) <doi:10.2307/1390802>, O'Hara Hines and Carter (1993) <doi:10.2307/2347405>, Wang (1985) <doi:10.2307/1269708>.

Efficient computation of likelihoods in design-based choice response time models, including the Decision Diffusion Model, is supported. The package enables rapid evaluation of likelihood functions for both single- and multi-subject models across trial-level data. It also offers fast initialisation of starting parameters for genetic sampling with many Markov chains, facilitating estimation in complex models typically found in experimental psychology and behavioural science. These optimisations help reduce computational overhead in large-scale model fitting tasks.

This package provides tools for studying genotype-phenotype maps for bi-allelic loci underlying quantitative phenotypes. The 0.1 version is released in connection with the publication of Gjuvsland et al (2013) and implements basic line plots and the monotonicity measures for GP maps presented in the paper. Reference: Gjuvsland AB, Wang Y, Plahte E and Omholt SW (2013) Monotonicity is a key feature of genotype-phenotype maps. Frontier in Genetics 4:216 <doi:10.3389/fgene.2013.00216>.

When comparing discrete data mini bubble plots allow displaying more information than traditional bubble plots via colour, shape or labels. Exact overlapping coordinates will be transformed so they surround the original point circularly without overlapping. This is implemented as a position_surround() function for ggplot2'.

The Global Biodiversity Information Facility ('GBIF', <https://www.gbif.org>) sources data from an international network of data providers, known as nodes'. Several of these nodes - the "living atlases" (<https://living-atlases.gbif.org>) - maintain their own web services using software originally developed by the Atlas of Living Australia ('ALA', <https://www.ala.org.au>). galah enables the R community to directly access data and resources hosted by GBIF and its partner nodes.

Fit penalized multivariable linear mixed models with a single random effect to control for population structure in genetic association studies. The goal is to simultaneously fit many genetic variants at the same time, in order to select markers that are independently associated with the response. Can also handle prior annotation information, for example, rare variants, in the form of variable weights. For more information, see the website below and the accompanying paper: Bhatnagar et al., "Simultaneous SNP selection and adjustment for population structure in high dimensional prediction models", 2020, <DOI:10.1371/journal.pgen.1008766>.

Give advice about good practices when building R packages. Advice includes functions and syntax to avoid, package structure, code complexity, code formatting, etc.

An extension of ggplot2 that makes it easy to add raw grid output, such as customised annotations, to a ggplot2 plot.

Offers tools for data formatting, anomaly detection, and classification of tree-ring data using spatial comparisons and cross-correlation. Supports flexible detrending and climateâ growth modeling via generalized additive mixed models (Wood 2017, ISBN:978-1498728331) and the mgcv package (<https://CRAN.R-project.org/package=mgcv>), enabling robust analysis of non-linear trends and autocorrelated data. Provides standardized visual reporting, including summaries, diagnostics, and model performance. Compatible with .rwl files and tailored for the Canadian Forest Service Tree-Ring Data (CFS-TRenD) repository (Girardin et al. (2021) <doi:10.1139/er-2020-0099>), offering a comprehensive and adaptable framework for dendrochronologists working with large and complex datasets.

This package provides a collection of custom ggplot2'-based visualizations for data exploration and analysis. Each function handles data preprocessing and returns a object that can be further customized using standard ggplot2 syntax.

Discrete scales for the colorblind-friendly Okabe-Ito palette, including color', fill', and edge_colour'. ggokabeito provides ggplot2 and ggraph scales to easily use the Okabe-Ito palette in your data visualizations.

Offers a generalization of the scatterplot matrix based on the recognition that most datasets include both categorical and quantitative information. Traditional grids of scatterplots often obscure important features of the data when one or more variables are categorical but coded as numerical. The generalized pairs plot offers a range of displays of paired combinations of categorical and quantitative variables. Emerson et al. (2013) <DOI:10.1080/10618600.2012.694762>.

Interact with the Google Analytics APIs <https://developers.google.com/analytics/>, including the Core Reporting API (v3 and v4), Management API, User Activity API GA4's Data API and Admin API and Multi-Channel Funnel API.

Real-time quantitative polymerase chain reaction (qPCR) data by Guescini et al. (2008) <doi:10.1186/1471-2105-9-326> in tidy format. This package provides two data sets where the amplification efficiency has been modulated: either by changing the amplification mix concentration, or by increasing the concentration of IgG, a PCR inhibitor. Original raw data files: <https://static-content.springer.com/esm/art%3A10.1186%2F1471-2105-9-326/MediaObjects/12859_2008_2311_MOESM1_ESM.xls> and <https://static-content.springer.com/esm/art%3A10.1186%2F1471-2105-9-326/MediaObjects/12859_2008_2311_MOESM5_ESM.xls>.

This package provides a collection of functions for processing Gen5 2.06 exported data. Gen5 is an essential data analysis software for BioTek plate readers <https://www.biotek.com/products/software-robotics-software/gen5-microplate-reader-and-imager-software/>. This package contains functions for data cleaning, modeling and plotting using exported data from Gen5 version 2.06. It exports technically correct data defined in (Edwin de Jonge and Mark van der Loo (2013) <https://cran.r-project.org/doc/contrib/de_Jonge+van_der_Loo-Introduction_to_data_cleaning_with_R.pdf>) for customized analysis. It contains Boltzmann fitting for general kinetic analysis. See <https://www.github.com/yanxianUCSB/gen5helper> for more information, documentation and examples.

This package provides the standard operations for signal processing on graphs: graph Fourier transform, spectral graph wavelet transform, visualization tools. It also implements a data driven method for graph signal denoising/regression, for details see De Loynes, Navarro, Olivier (2019) <arxiv:1906.01882>. The package also provides an interface to the SuiteSparse Matrix Collection, <https://sparse.tamu.edu/>, a large and widely used set of sparse matrix benchmarks collected from a wide range of applications.

Introduces a Copilot'-like completion experience, but it knows how to talk to the objects in your R environment. ellmer chats are integrated directly into your RStudio and Positron sessions, automatically incorporating relevant context from surrounding lines of code and your global environment (like data frame columns and types). Open the package dialog box with a keyboard shortcut, type your request, and the assistant will stream its response directly into your documents.

Simplifies regression modeling in R by integrating multiple modeling and summarization tools into a cohesive, user-friendly interface. Designed to be accessible for researchers, particularly those in Low- and Middle-Income Countries (LMIC). Built upon widely accepted statistical methods, including logistic regression (Hosmer et al. 2013, ISBN:9781118548429), log-binomial regression (Spiegelman and Hertzmark 2005 <doi:10.1093/aje/kwi188>), Poisson and robust Poisson regression (Zou 2004 <doi:10.1093/aje/kwh090>), negative binomial regression (Hilbe 2011, ISBN:9780521179515), and linear regression (Kutner et al. 2005, ISBN:9780071122214). Leverages multiple dependencies to ensure high-quality output and generate reproducible, publication-ready tables in alignment with best practices in epidemiology and applied statistics.

This package provides a simple, opinionated toolkit for visualizing genomic variant data using a ggplot2'-native grammar. Accepts VCF files or plain data frames and produces publication-ready lollipop plots, consequence summaries, mutational spectrum charts, and cohort-level comparisons with minimal code. Designed for both wet-lab biologists and experienced bioinformaticians.

Create correlation heatmaps with ggplot2 and customise them with flexible annotation and clustering. Symmetric heatmaps can use triangular or mixed layouts, removing redundant information or displaying complementary information in the two halves. There is also support for general heatmaps not displaying correlations.

In gene-expression microarray studies, for example, one generally obtains a list of dozens or hundreds of genes that differ in expression between samples and then asks What does all of this mean biologically? Alternatively, gene lists can be derived conceptually in addition to experimentally. For instance, one might want to analyze a group of genes known as housekeeping genes. The work of the Gene Ontology (GO) Consortium <geneontology.org> provides a way to address that question. GO organizes genes into hierarchical categories based on biological process, molecular function and subcellular localization. The role of GoMiner is to automate the mapping between a list of genes and GO, and to provide a statistical summary of the results as well as a visualization.

This package performs variable selection with data from Genome-wide association studies (GWAS), or other high-dimensional data with continuous, binary or survival outcomes, combining in an iterative framework the computational efficiency of the structured screen-and-select variable selection strategy based on some association learning and the parsimonious uncertainty quantification provided by the use of non-local priors (see Sanyal et al., 2019 <DOI:10.1093/bioinformatics/bty472>).

Use the graph-constrained estimation (Grace) procedure (Zhao and Shojaie, 2016 <doi:10.1111/biom.12418>) to estimate graph-guided linear regression coefficients and use the Grace/GraceI/GraceR tests to perform graph-guided hypothesis tests on the association between the response and the predictors.