The Flow Analysis Summary Statistics Tool for R, fasstr', provides various functions to tidy and screen daily stream discharge data, calculate and visualize various summary statistics and metrics, and compute annual trending and volume frequency analyses. It features useful function arguments for filtering of and handling dates, customizing data and metrics, and the ability to pull daily data directly from the Water Survey of Canada hydrometric database (<https://collaboration.cmc.ec.gc.ca/cmc/hydrometrics/www/>).

Generalized PCA and related matrix decompositions in weighted inner-product spaces. Methods are based on Allen, G. I., Grosenick, L., and Taylor, J. (2014) <doi:10.1080/01621459.2013.852978>, "A generalized least-square matrix decomposition", Journal of the American Statistical Association, 109(505), 145-159; and Abdi, H. (2007), "Singular value decomposition (SVD) and generalized singular value decomposition" <https://personal.utdallas.edu/~herve/Abdi-SVD2007-pretty.pdf>, in "Encyclopedia of Measurement and Statistics", 907-912.

Create animated biplots that enables dynamic visualisation of temporal or sequential changes in multivariate data by animating a single biplot across the levels of a time variable. It builds on objects from the biplotEZ package, Lubbe S, le Roux N, Nienkemper-Swanepoel J, Ganey R, Buys R, Adams Z, Manefeldt P (2024) <doi:10.32614/CRAN.package.biplotEZ>, allowing users to create animated biplots that reveal how both samples and variables evolve over time.

This package provides simple start/stop memory tracking functions tic_mem() and toc_mem() that can be nested, inspired by the tictoc package. Track RAM usage during code execution with support for logging, custom messages, nested tracking blocks, and parallel worker monitoring. Features continuous background polling to estimate peak memory usage across main process and workers. Integrates with the future package ecosystem for automatic worker detection. Designed for monitoring memory consumption in parallel workflows.

The optimal level of significance is calculated based on a decision-theoretic approach. The optimal level is chosen so that the expected loss from hypothesis testing is minimized. A range of statistical tests are covered, including the test for the population mean, population proportion, and a linear restriction in a multiple regression model. The details are covered in Kim and Choi (2020) <doi:10.1111/abac.12172>, and Kim (2021) <doi:10.1080/00031305.2020.1750484>.

Based on the structure of the SPSS version of the Swiss Household Panel (SHP) data, provides a function seqFromWaves() that seeks the data of variables specified by the user in each of the wave files and collects them as sequences. The function also matches the sequences with variables from other files such as the master files of persons (MP) and households (MH), and social origins (SO). It can also match with activity calendar data (CA).

Allows forecasting time series using nearest neighbors regression Francisco Martinez, Maria P. Frias, Maria D. Perez-Godoy and Antonio J. Rivera (2019) <doi:10.1007/s10462-017-9593-z>. When the forecasting horizon is higher than 1, two multi-step ahead forecasting strategies can be used. The model built is autoregressive, that is, it is only based on the observations of the time series. The nearest neighbors used in a prediction can be consulted and plotted.

This package provides a tool to help create shiny apps for selecting and annotating elements of images. Users must supply images, questions, and answer choices. The user interface is a dynamic shiny app, that displays the images and questions and answer choices. The data generated can be saved to a file that can be used for subsequent analysis. The original purpose was to annotate still images from tennis video for face recognition and emotion detection purposes.

Draw images easily, set up a plot with an image, specify where that image should be placed. Image plot by default reflects the index of the image data itself, or can be specified in simple extent terms xmin,xmax,ymin,ymax'. Numeric matrices, integer arrays, byte arrays, character arrays, and native rasters are (or will be) supported. A combination of image() and rasterImage() from the graphics package with their good features in one place.

This package provides a fast, correct, safe, and ergonomic YAML 1.2 parser and generator written in Rust'. Convert between YAML and simple R objects with full support for multi-document streams, tags, anchors, and aliases. Offers opt-in handlers for custom tag behavior and round-trips common R data structures. Implements the YAML 1.2.2 specification from the YAML Language Development Team (2021) <https://yaml.org/spec/1.2.2/>. Proudly supported by Posit.

This package provides a replacement and extension of the optim function to call to several function minimization codes in R in a single statement. These methods handle smooth, possibly box constrained functions of several or many parameters. Note that the function optimr was prepared to simplify the incorporation of minimization codes going forward. This package also implements some utility codes and some extra solvers, including safeguarded Newton methods. Many methods previously separate are now included here.

The grammar of graphics as shown in ggplot2 has provided an expressive API for users to build plots. This package ggside extends ggplot2 by allowing users to add graphical information about one of the main panel's axis using a familiar ggplot2 style API with tidy data. This package is particularly useful for visualizing metadata on a discrete axis, or summary graphics on a continuous axis such as a boxplot or a density distribution.

Studies including both microbiome and metabolomics data are becoming more common. Often, it would be helpful to integrate both datasets in order to see if they corroborate each others patterns. All vs all association is imprecise and likely to yield spurious associations. This package takes a knowledge-based approach to constrain association search space, only considering metabolite-function pairs that have been recorded in a pathway database. This package also provides a framework to assess differential association.

This package is here to support legacy usages of it, but it should not be used for new code development. It provides a single function, plotScreen, for visualising data in microtitre plate or slide format. As a better alternative for such functionality, please consider the platetools package on CRAN (https://cran.r-project.org/package=platetools and https://github.com/Swarchal/platetools), or ggplot2 (geom_raster, facet_wrap) as exemplified in the vignette of this package.

Constructs sparse adjacency matrices from spatial coordinates, feature measurements, class labels, and temporal indices. Supports nearest-neighbor graphs, heat-kernel weights, graph Laplacians, diffusion operators, and bilateral smoothers for graph-based data analysis, following spectral graph methods in von Luxburg (2007) <doi:10.1007/s11222-007-9033-z>, diffusion maps in Coifman and Lafon (2006) <doi:10.1016/j.acha.2006.04.006>, and bilateral filtering in Tomasi and Manduchi (1998) <doi:10.1109/ICCV.1998.710815>.

This package provides tools for working with BIDS (Brain Imaging Data Structure) formatted neuroimaging datasets. The package provides functionality for reading and querying BIDS'-compliant projects, creating mock BIDS datasets for testing, and extracting preprocessed data from fMRIPrep derivatives. It supports searching and filtering BIDS files by various entities such as subject, session, task, and run to streamline neuroimaging data workflows. See Gorgolewski et al. (2016) <doi:10.1038/sdata.2016.44> for the BIDS specification.

The Bootstrap framework lets you add some JavaScript functionality to your web site by adding attributes to your HTML tags - Bootstrap takes care of the JavaScript <https://getbootstrap.com/docs/3.3/javascript/>. If you are using R Markdown or Shiny, you can use these functions to create collapsible sections, accordion panels, modals, tooltips, popovers, and an accordion sidebar framework (not described at Bootstrap site). Please note this package was designed for Bootstrap 3.3.

This package provides an Markov-Chain-Monte-Carlo algorithm for Bayesian t-tests on the effect size. The underlying Gibbs sampler is based on a two-component Gaussian mixture and approximates the posterior distributions of the effect size, the difference of means and difference of standard deviations. A posterior analysis of the effect size via the region of practical equivalence is provided, too. For more details about the Gibbs sampler see Kelter (2019) <arXiv:1906.07524>.

This package provides a unified interface for exporting figures created with base graphics, ggplot2', grid', pheatmap', and ComplexHeatmap to PDF, PNG, JPEG, and TIFF files. Selects an appropriate rendering strategy based on the plot object and supports exporting a figure to multiple formats in one call. Also provides font configuration and diagnostics, reusable figure presets, publication-oriented themes, and colour palettes. The ggplot2 graphics framework is described by Wickham (2016, ISBN:978-3-319-24277-4).

This package provides functions designed to simulate data that conform to basic unidimensional IRT models (for now 3-parameter binary response models and graded response models) along with Post-Hoc CAT simulations of those models given various item selection methods, ability estimation methods, and termination criteria. See Wainer (2000) <doi:10.4324/9781410605931>, van der Linden & Pashley (2010) <doi:10.1007/978-0-387-85461-8_1>, and Eggen (1999) <doi:10.1177/01466219922031365> for more details.

This package provides a tool to easily run and visualise supervised and unsupervised state of the art customer segmentation. It is built like a pipeline covering the 3 main steps in a segmentation project: pre-processing, modelling, and plotting. Users can either run the pipeline as a whole, or choose to run any one of the three individual steps. It is equipped with a supervised option (tree optimisation) and an unsupervised option (k-clustering) as default models.

This package provides a Shiny'-based interactive platform for end-to-end data science workflows. Provides modules for data import (CSV, Excel', RDS, TXT), data preprocessing (missing value imputation, encoding, scaling, outlier removal), exploratory data analysis with interactive plots and normality tests, supervised learning (regression and classification each with eight algorithms), and unsupervised learning (k-means, hierarchical clustering, density-based spatial clustering of applications with noise). Designed for students and practitioners in data science and artificial intelligence.

This is a method for Allele-specific DNA Copy Number Profiling using Next-Generation Sequencing. Given the allele-specific coverage at the variant loci, this program segments the genome into regions of homogeneous allele-specific copy number. It requires, as input, the read counts for each variant allele in a pair of case and control samples. For detection of somatic mutations, the case and control samples can be the tumor and normal sample from the same individual.

Promote access to the GESLA <https://gesla787883612.wordpress.com> (Global Extreme Sea Level Analysis) dataset, a higher-frequency sea-level record data from all over the world. It provides functions to download it entirely, or query subsets directly into R, without the need of downloading the full dataset. Also, it provides a built-in web-application, so that users can apply basic filters to select the data of interest, generating informative plots, and showing the selected sites.