dominoSignal is a package developed to analyze cell signaling through ligand - receptor - transcription factor networks in scRNAseq data. It takes as input information transcriptomic data, requiring counts, z-scored counts, and cluster labels, as well as information on transcription factor activation (such as from SCENIC) and a database of ligand and receptor pairings (such as from CellPhoneDB). This package creates an object storing ligand - receptor - transcription factor linkages by cluster and provides several methods for exploring, summarizing, and visualizing the analysis.

tidyFlowCore bridges the gap between flow cytometry analysis using the flowCore Bioconductor package and the tidy data principles advocated by the tidyverse. It provides a suite of dplyr-, ggplot2-, and tidyr-like verbs specifically designed for working with flowFrame and flowSet objects as if they were tibbles; however, your data remain flowCore data structures under this layer of abstraction. tidyFlowCore enables intuitive and streamlined analysis workflows that can leverage both the Bioconductor and tidyverse ecosystems for cytometry data.

Facilitates retrieval, transformation and analysis of the data from the Barcode of Life Data Systems (BOLD) database <https://boldsystems.org/>. This package allows both public and private user data to be easily downloaded into the R environment using a variety of inputs such as: IDs (processid, sampleid), BINs, dataset codes, project codes, taxonomy, geography etc. It provides frictionless data conversion into formats compatible with other R-packages and third-party tools, as well as functions for sequence alignment & clustering, biodiversity analysis and spatial mapping.

Streamlines the creation of high-quality labels for insect pinning. By taking a dataset as input, the package allow to generate printable labels in LaTeX and PDF format, helping researchers and entomologists maintain accurate and standardized specimen records. Requires a compatible installation of pdflatex (e.g. <https://www.tug.org/texlive/>). For enhanced accessibility, the package includes a user-friendly shiny application (accessible online <https://nicolas-moiroux.shinyapps.io/InsectLabelR/>), which provides a graphical interface for generating labels without requiring programming expertise.

This package provides two functions: Group_function() and Parallel_Tree(). Group_function() applies a given function (e.g., mean()) to input variable(s) by group across levels of a multilevel data structure, with additional data management options. Parallel_Tree() uses ggplot2 to create parallel coordinate plots (technically a facsimile of parallel coordinate plots in a Cartesian coordinate system). Used in combination, these functions can create parallel tree plots, a variant of parallel coordinate plots useful for visualizing multilevel data.

This package provides a re-implementation of quantile kriging. Quantile kriging was described by Plumlee and Tuo (2014) <doi:10.1080/00401706.2013.860919>. With computational savings when dealing with replication from the recent paper by Binois, Gramacy, and Ludovski (2018) <doi:10.1080/10618600.2018.1458625> it is now possible to apply quantile kriging to a wider class of problems. In addition to fitting the model, other useful tools are provided such as the ability to automatically perform leave-one-out cross validation.

This package provides tools for checking whether an R session is in a clean state, including the global environment, attached packages, loaded namespaces, attached environments, session run time, R options, locale settings, and system environment variables. Intended as a safer replacement for the common rm(list = ls()) idiom: rather than silently wiping the global environment, sessioncheck() surfaces problems so the user can make an informed decision. The package also supplies tools for documenting the session state, to aid in the overall process.

RStudio is an integrated development environment (IDE) for the R programming language. Some of its features include: Customizable workbench with all of the tools required to work with R in one place (console, source, plots, workspace, help, history, etc.); syntax highlighting editor with code completion; execute code directly from the source editor (line, selection, or file); full support for authoring Sweave and TeX documents. RStudio can also be run as a server, enabling multiple users to access the RStudio IDE using a web browser.

GNU Emacs is an extensible and highly customizable text editor. It is based on an Emacs Lisp interpreter with extensions for text editing. Emacs has been extended in essentially all areas of computing, giving rise to a vast array of packages supporting, e.g., email, IRC and XMPP messaging, spreadsheets, remote server editing, and much more. Emacs includes extensive documentation on all aspects of the system, from basic editing to writing large Lisp programs. It has full Unicode support for nearly all human languages.

This package extends the fitdistr function of the MASS package with several functions to help the fit of a parametric distribution to non-censored or censored data. Censored data may contain left-censored, right-censored and interval-censored values, with several lower and upper bounds. In addition to maximum likelihood estimation (MLE), the package provides moment matching (MME), quantile matching (QME) and maximum goodness-of-fit estimation (MGE) methods (available only for non-censored data). Weighted versions of MLE, MME and QME are available.

Penalized variable selection tools for the Cox proportional hazards model with interval censored and possibly left truncated data. It performs variable selection via penalized nonparametric maximum likelihood estimation with an adaptive lasso penalty. The optimal thresholding parameter can be searched by the package based on the profile Bayesian information criterion (BIC). The asymptotic validity of the methodology is established in Li et al. (2019 <doi:10.1177/0962280219856238>). The unpenalized nonparametric maximum likelihood estimation for interval censored and possibly left truncated data is also available.

This package provides a unified framework for detecting change points in univariate time series using multiple statistical methods, including Pettitt's test, Buishand Range test, Buishand U test, and the Standard Normal Homogeneity Test (SNHT). The package summarizes individual test results, determines a consensus change point using majority, median, or weighted agreement approaches, exports results with graphical comparisons of observations for before and after the detected change point. The methodology is further described in Laasya et al. (2026) <doi:10.1007/s11069-025-07783-2>.

This package produces a publication-ready table that includes all effect estimates necessary for full reporting effect modification and interaction analysis as recommended by Knol and Vanderweele (2012) [<doi:10.1093/ije/dyr218>]. It also estimates confidence interval for the trio of additive interaction measures using the delta method (see Hosmer and Lemeshow (1992), [<doi:10.1097/00001648-199209000-00012>]), variance recovery method (see Zou (2008), [<doi:10.1093/aje/kwn104>]), or percentile bootstrapping (see Assmann et al. (1996), [<doi:10.1097/00001648-199605000-00012>]).

An open source platform for validation and process control. Tools to analyze data from internal validation of forensic short tandem repeat (STR) kits are provided. The tools are developed to provide the necessary data to conform with guidelines for internal validation issued by the European Network of Forensic Science Institutes (ENFSI) DNA Working Group, and the Scientific Working Group on DNA Analysis Methods (SWGDAM). A front-end graphical user interface is provided. More information about each function can be found in the respective help documentation.

Set of tools for evaluating pooled high-throughput screening experiments, typically employing CRISPR/Cas9 or shRNA expression cassettes. Contains methods for interrogating library and cassette behavior within an experiment, identifying differentially abundant cassettes, aggregating signals to identify candidate targets for empirical validation, hypothesis testing, and comprehensive reporting. Version 2.0 extends these applications to include a variety of tools for contextualizing and integrating signals across many experiments, incorporates extended signal enrichment methodologies via the "sparrow" package, and streamlines many formal requirements to aid in interpretablity.

Implementation in R of the alpha-shape of a finite set of points in the three-dimensional space. The alpha-shape generalizes the convex hull and allows to recover the shape of non-convex and even non-connected sets in 3D, given a random sample of points taken into it. Besides the computation of the alpha-shape, this package provides users with functions to compute the volume of the alpha-shape, identify the connected components and facilitate the three-dimensional graphical visualization of the estimated set.

This package provides a varied array of mathematical derivations from various titrimetric and colorimetric methods for analyzing water quality parameters were condensed and integrated for the better physicochemical analysis. It is indispensable for managing any aquatic ecosystem, including aquaculture facilities. By substituting titrant and spectrophotometric absorbance readings, accurate determination of the concentrations of critical parameters such as Dissolved Oxygen, Free Carbon Dioxide, Total Alkalinity, Water Hardness, Hydrogen Sulfide, Total Ammonia Nitrogen, Nitrite, Nitrate, Chlorinity, Salinity, Inorganic Phosphate, and Transparency can be facilitated APHA(2017,ISBN:9780875532875).

This package provides statistical and graphical tools for the analysis of hydroponic crop production data. The package includes functions for descriptive statistical analysis, data visualization, correlation analysis, heatmap generation, and graphical summaries of plant growth and nutrient-related variables. These tools support researchers, students, and practitioners in evaluating crop performance and environmental conditions in hydroponic cultivation systems. The package utilizes standard statistical methods implemented in R for data exploration and visualization. Methods are described in James et al. (2021, ISBN:9781071614172) and Wickham (2016, ISBN:9783319242750).

This package provides software to facilitate the design, testing, and operation of computer models. It focuses particularly on tools that make it easy to construct and edit a customized graphical user interface ('GUI'). Although our simplified GUI language depends heavily on the R interface to the Tcl/Tk package, a user does not need to know Tcl/Tk'. Examples illustrate models built with other R packages, including PBSmapping', PBSddesolve', and BRugs'. A complete user's guide PBSmodelling-UG.pdf shows how to use this package effectively.

This package provides a transparent and modular implementation of Partial Least Squares Structural Equation Modeling (PLS-SEM) focused on reflective measurement models (Mode A). The package separates estimation, bootstrap inference, and predictive evaluation into independent components, emphasising algorithmic transparency, reproducibility, and researcher-controlled analysis. Methods are based on Tenenhaus, Esposito Vinzi, Chatelin & Lauro (2005) <doi:10.1016/j.csda.2004.03.005>, Hair, Risher, Sarstedt & Ringle (2019) <doi:10.1108/EBR-11-2018-0203>, and Henseler, Ringle & Sarstedt (2015) <doi:10.1007/s11747-014-0403-8>.

This package provides tools for processing, analyzing, and visualizing spectral data collected from 3D laser-based scanning systems. Supports applications in agriculture, forestry, environmental monitoring, industrial quality control, and biomedical research. Enables evaluation of plant growth, productivity, resource efficiency, disease management, and pest monitoring. Includes statistical methods for extracting insights from multispectral and hyperspectral data and generating publication-ready visualizations. See Zieschank & Junker (2023) <doi:10.3389/fpls.2023.1141554> and Saric et al. (2022) <doi:10.1016/J.TPLANTS.2021.12.003> for related work.

This package provides a collection of parametric quantile regression models for bounded data. At present, the package provides 13 parametric quantile regression models. It can specify regression structure for any quantile and shape parameters. It also provides several S3 methods to extract information from fitted model, such as residual analysis, prediction, plotting, and model comparison. For more computation efficient the [dpqr]'s, likelihood, score and hessian functions are written in C++. For further details see Mazucheli et. al (2022) <doi:10.1016/j.cmpb.2022.106816>.

Bayesian models are used to estimate effect sizes (e.g., gene expression changes, protein abundance differences, drug response effects) while accounting for uncertainty, small sample sizes, and complex experimental designs. However, Bayesian posteriors of models with many parameters are often difficult to interpret at a glance. One way to quickly identify important biological changes based on frequentist analysis are volcano plots (using fold-changes and p-values). Bayesian volcano plots bring together the explicit treatment of uncertainty in Bayesian models and the familiar visualization of volcano plots.

Provided data containing an outcome variable, compositional variables and additional covariates (optional); linearly regress the outcome variable on an isometric log ratio (ilr) transformation of the linearly dependent compositional variables. The package provides predictions (with confidence intervals) in the change (delta) in the outcome/response variable based on the multiple linear regression model and evenly spaced reallocations of the compositional values. The compositional data analysis approach implemented is outlined in Dumuid et al. (2017a) <doi:10.1177/0962280217710835> and Dumuid et al. (2017b) <doi:10.1177/0962280217737805>.