This package provides enhancements on the Sweave() function in the base package. In particular a facility for caching code chunk results is included.

Cicero computes putative cis-regulatory maps from single-cell chromatin accessibility data. It also extends the monocle package for use in chromatin accessibility data.

This package contains class definitions for two-color spotted microarray data. It also includes functions for data input, diagnostic plots, normalization and quality checking.

This package provides tools to fit a variety of latent variable models, including confirmatory factor analysis, structural equation modeling and latent growth curve models.

This package is a compatibility wrapper to replace the orphaned package by Romain Francois. New applications should use the openssl or base64enc package instead.

This package provides a Tidy implementation of grouping sets', rollup and cube - extensions of the group_by clause that allow for computing multiple group_by clauses in a single statement. For more detailed information on these functions, please refer to "Enhanced Aggregation, Cube, Grouping and Rollup" <https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C+Grouping+and+Rollup>.

This package provides a collection of fast statistical and utility functions for data analysis. Functions for regression, maximum likelihood, column-wise statistics and many more have been included. C++ has been utilized to speed up the functions. References: Tsagris M., Papadakis M. (2018). Taking R to its limits: 70+ tips. PeerJ Preprints 6:e26605v1 <doi:10.7287/peerj.preprints.26605v1>.

Create rich and fully interactive 3D visualizations of molecular data. Visualizations can be included in Shiny apps and R markdown documents, or viewed from the R console and RStudio Viewer. r3dmol includes an extensive API to manipulate the visualization after creation, and supports getting data out of the visualization into R. Based on the 3dmol.js and the htmlwidgets R package.

This package provides functions to retrieve data and metadata from providers that disseminate data by means of SDMX web services. SDMX (Statistical Data and Metadata eXchange) is a standard that has been developed with the aim of simplifying the exchange of statistical information. More about the SDMX standard and the SDMX Web Services can be found at: <https://sdmx.org>.

recoup calculates and plots signal profiles created from short sequence reads derived from Next Generation Sequencing technologies. The profiles provided are either sumarized curve profiles or heatmap profiles. Currently, recoup supports genomic profile plots for reads derived from ChIP-Seq and RNA-Seq experiments. The package uses ggplot2 and ComplexHeatmap graphics facilities for curve and heatmap coverage profiles respectively.

Non-linear inversion for hypocenter estimation and analysis of seismic data collected continuously, or in trigger mode. The functions organize other functions from RSEIS and GEOmap to help researchers pick, locate, and store hypocenters for detailed seismic investigation. Error ellipsoids and station influence are estimated via jackknife analysis. References include Iversen, E. S., and J. M. Lees (1996)<doi:10.1785/BSSA0860061853>.

This package provides a computational resource designed to accurately detect microbial nucleic acids while filtering out contaminants and false-positive taxonomic assignments from standard transcriptomic sequencing of mammalian tissues. For more details, see Ghaddar (2023) <doi:10.1038/s43588-023-00507-1>. This implementation leverages the polars package for fast and systematic microbial signal recovery and denoising from host tissue genomic sequencing.

For instructions, check <https://github.com/Hzhang-ouce/ARTofR>. This is a wrapper of bannerCommenter', for inserting neat comments, headers and dividers.

Create a hierarchical acoustic event species classifier out of multiple call type detectors as described in Rankin et al (2017) <doi:10.1111/mms.12381>.

This package provides tools for assessing data quality, performing exploratory analysis, and semi-automatic preprocessing of messy data with change tracking for integral dataset cleaning.

Integrated differential expression (DE) and differential co-expression (DC) analysis on gene expression data based on DECODE (DifferEntial CO-expression and Differential Expression) algorithm.

Wrapper for the ggplot2 package that creates a variety of common charts (e.g. bar, line, area, ROC, waterfall, pie) while aiming to reduce typing.

The cointegration based support vector regression model enables researchers to use data obtained from the cointegrating vector as input in the support vector regression model.

Four graph-based tests are provided for testing whether two samples are from the same distribution. It works for both continuous data and discrete data.

Design of group sequential trials, including non-binding futility analysis at multiple time points (Gallo, Mao, and Shih, 2014, <doi:10.1080/10543406.2014.932285>).

This package provides a set of fast, chainable image-processing operations which are applicable to images of two, three or four dimensions, particularly medical images.

This package provides datasets for the book "Introduction to Statistical Data Analysis for the Life Sciences, Second edition" by Ekstrøm and Sørensen (2014).

Introduces in-sample, out-of-sample, pseudo out-of-sample, and benchmark model forecast tests and a new class for working with forecast data, Forecast.

Performant interactive scatterplot for ~ 1 million points. Zoom, pan, and pick points. Includes tooltips, labels, a grid overlay, legend, and coupled interactions across multiple plots.