Enables gene regulatory network (GRN) analysis on single cell clusters, using the GRN analysis software ANANSE', Xu et al.(2021) <doi:10.1093/nar/gkab598>. Export data from Seurat objects, for GRN analysis by ANANSE implemented in snakemake'. Finally, incorporate results for visualization and interpretation.

The goal of equatiomatic is to reduce the pain associated with writing LaTeX formulas from fitted models. The primary function of the package, extract_eq(), takes a fitted model object as its input and returns the corresponding LaTeX code for the model.

Human names are complicated and nonstandard things. Humaniformat, which is based on Anthony Ettinger's humanparser project (https://github.com/ chovy/humanparser) provides functions for parsing human names, making a best- guess attempt to distinguish sub-components such as prefixes, suffixes, middle names and salutations.

This package provides beginner friendly framework to analyse population genetic data. Based on adegenet objects it uses knitr to create comprehensive reports on spatial genetic data. For detailed information how to use the package refer to the comprehensive tutorials or visit <http://www.popgenreport.org/>.

Manipulates invertible functions from a finite set to itself. Can transform from word form to cycle form and back. To cite the package in publications please use Hankin (2020) "Introducing the permutations R package", SoftwareX, volume 11 <doi:10.1016/j.softx.2020.100453>.

This package provides a general-purpose implementation of synthetic control methods that accounts for potential spillover effects between units. Based on the methodology of Cao and Dowd (2019) <doi:10.48550/arXiv.1902.07343> "Estimation and Inference for Synthetic Control Methods with Spillover Effects".

Fast multi-trait and multi-trail Genome Wide Association Studies (GWAS) following the method described in Zhou and Stephens. (2014), <doi:10.1038/nmeth.2848>. One of a series of statistical genetic packages for streamlining the analysis of typical plant breeding experiments developed by Biometris.

This package defines S4 classes for single-cell genomic data and associated information, such as dimensionality reduction embeddings, nearest-neighbor graphs, and spatially-resolved coordinates. It provides data access methods and R-native hooks to ensure the Seurat object is familiar to other R users.

The Mechanize library is used for automating interaction with websites. Mechanize automatically stores and sends cookies, follows redirects, and can follow links and submit forms. Form fields can be populated and submitted. Mechanize also keeps track of the sites that you have visited as a history.

This package provides tools for data frame summaries, cross-tabulations, weight-enabled frequency tables and common univariate statistics in concise tables available in a variety of formats (plain ASCII, Markdown and HTML). A good point-of-entry for exploring data, both for experienced and new R users.

This is a package for parameter description and operations in optimization, tuning and machine learning. Parameters can be described (type, constraints, defaults, etc.), combined to parameter sets and can in general be programmed on. A useful OptPath object (archive) to log function evaluations is also provided.

This package provides implementations of apply(), eapply(), lapply(), Map(), mapply(), replicate(), sapply(), tapply(), and vapply() that can be resolved using any future-supported backend, e.g. parallel on the local machine or distributed on a compute cluster.

This package provides an extensible framework for automatically placing direct labels onto multicolor plots. Label positions are described using positioning methods that can be re-used across several different plots. There are heuristics for examining trellis and ggplot objects and inferring an appropriate positioning method.

Mass spectrometry (MS) data backend supporting import and export of MS/MS spectra data from Mascot Generic Format (mgf) files. Objects defined in this package are supposed to be used with the Spectra Bioconductor package. This package thus adds mgf file support to the Spectra package.

This package implements adaptive gPCA, as described in: Fukuyama, J. (2017) <arXiv:1702.00501>. The package also includes functionality for applying the method to phyloseq objects so that the method can be easily applied to microbiome data and a shiny app for interactive visualization.

This package provides methods for piecewise smooth regression. A piecewise smooth signal is estimated by applying a bootstrapped test recursively (binary segmentation approach). Each bootstrapped test decides whether the underlying signal is smooth on the currently considered subsegment or contains at least one further change-point.

Identifies genome-related molecular traits with significant evidence of genetic regulation and performs a bootstrap procedure to correct estimated effect sizes for over-estimation present in cis-QTL mapping studies (The "Winner's Curse"), described in Huang QQ *et al.* 2018 <doi: 10.1093/nar/gky780>.

Compute duration curves of daily flow series, both real and modeled, to be compared through indexes of flow duration curves. The package functions include comparative plots and goodness of fit tests. Flow duration curve indexes are based on: Yilmaz et al., (2008) <DOI:10.1029/2007WR006716>.

We consider the non-parametric maximum likelihood estimation of the underlying distribution function, assuming log-concavity, based on mixed-case interval-censored data. The algorithm implemented is base on Chi Wing Chu, Hok Kan Ling and Chaoyu Yuan (2024, <doi:10.48550/arXiv.2411.19878>).

Simple result caching in R based on R.cache. The global environment is not considered when caching results simplifying moving files between multiple instances of R. Relies on more base functions than R.cache (e.g. cached results are saved using saveRDS() and readRDS()).

This package provides tools for Genotype by Environment Interaction (GEI) analysis, using statistical models and visualizations to assess genotype performance across environments. It helps researchers explore interaction effects, stability, and adaptability in multi-environment trials, identifying the best-performing genotypes in different conditions. Which Win Where!

Allows users to analyze text and classify emotions such as happiness, sadness, anger, fear, and neutrality. It combines text preprocessing, TF-IDF (Term Frequency-Inverse Document Frequency) feature extraction, and Random Forest classification to predict emotions and map them to corresponding emojis for enhanced sentiment visualization.

This package provides a system for personalized exercise plan recommendations for T2D (Type 2 Diabetes) patients based on the primary outcome of HbA1c (Glycated Hemoglobin). You provide the individual's information, and T2DFitTailor details the exercise plan and predicts the intervention's effectiveness.

Perform a regression analysis, generate a regression table, create a scatter plot, and download the results. It uses stargazer for generating regression tables and ggplot2 for creating plots. With just two lines of code, you can perform a regression analysis, visualize the results, and save the output. It is part of my make R easy project where one doesn't need to know how to use various packages in order to get results and makes it easily accessible to beginners. This is a part of my make R easy project. Help from ChatGPT was taken. References were Wickham (2016) <doi:10.1007/978-3-319-24277-4>.