This package implements likelihood-based estimation and diagnostics for multi-type recurrent event data with dynamic risk that depends on prior events and accommodates terminating events. Methods are described in Ghosh, Chan, Younes and Davis (2023) "A Dynamic Risk Model for Multitype Recurrent Events" <doi:10.1093/aje/kwac213>.

Some functions for performing non-negative matrix factorization, non-negative CANDECOMP/PARAFAC (CP) decomposition, non-negative Tucker decomposition, and generating toy model data. See Andrzej Cichock et al (2009) and the reference section of GitHub README.md <https://github.com/rikenbit/nnTensor>, for details of the methods.

This package provides a shiny application for visualizing high-dimensional data using non-linear dimensionality reduction (NLDR) techniques such as t-SNE and UMAP. It provides an interactive platform to explore high-dimensional datasets, diagnose the quality of the embeddings using the quollr package, and compare different NLDR methods.

It creates a lattice plot to visualize panel or longitudinal data. The observed values are plotted as dots and the fitted values as lines, both against time. The plot is customizable and easy to edit, even if you do not know how to construct a lattice plot from scratch.

An implementation of sparse Gaussian Markov random field mixtures presented by Ide et al. (2016) <doi:10.1109/ICDM.2016.0119>. It provides a novel anomaly detection method for multivariate noisy sensor data. It can automatically handle multiple operational modes. And it can also compute variable-wise anomaly scores.

Extension of funHDDC Schmutz et al. (2018) <doi:10.1007/s00180-020-00958-4> for cases including outliers by fitting t-distributions for robust groups. TFunHDDC can cluster univariate or multivariate data produced by the fda package for data using a b-splines or Fourier basis.

Testing, Implementation, and Forecasting of the THETA-SVM hybrid model. The THETA-SVM hybrid model combines the distinct strengths of the THETA model and the Support Vector Machine (SVM) model for time series forecasting.For method details see Bhattacharyya et al. (2022) <doi:10.1007/s11071-021-07099-3>.

Method to estimate the effect of the trend in predictor variables on the observed trend of the response variable using mixed models with temporal autocorrelation. See Fernández-Martà nez et al. (2017 and 2019) <doi:10.1038/s41598-017-08755-8> <doi:10.1038/s41558-018-0367-7>.

This package provides functions for the retrieval, manipulation, and visualization of geospatial data, with an aim towards producing 3D landscape visualizations in the Unity 3D rendering engine. Functions are also provided for retrieving elevation data and base map tiles from the USGS National Map <https://apps.nationalmap.gov/services/>.

PADRINO houses textual representations of Integral Projection Models which can be converted from their table format into full kernels to reproduce or extend an already published analysis. Rpadrino is an R interface to this database. For more information on Integral Projection Models, see Easterling et al. (2000) <doi:10.1890/0012-9658(2000)081[0694:SSSAAN]2.0.CO;2>, Merow et al. (2013) <doi:10.1111/2041-210X.12146>, Rees et al. (2014) <doi:10.1111/1365-2656.12178>, and Metcalf et al. (2015) <doi:10.1111/2041-210X.12405>. See Levin et al. (2021) for more information on ipmr', the engine that powers model reconstruction <doi:10.1111/2041-210X.13683>.

REDCap Data Management - REDCap (Research Electronic Data CAPture; <https://projectredcap.org>) is a web application developed at Vanderbilt University, designed for creating and managing online surveys and databases and the REDCap API is an interface that allows external applications to connect to REDCap remotely, and is used to programmatically retrieve or modify project data or settings within REDCap, such as importing or exporting data. REDCapDM is an R package that allows users to manage data exported directly from REDCap or using an API connection. This package includes several functions designed for pre-processing data, generating reports of queries such as outliers or missing values, and following up on previously identified queries.

RAD is package which defines schemas for the Nancy Grace Roman Space Telescope shared attributes for processing and archive. These schemas are schemas for the ASDF file file format, which are used by ASDF to serialize and deserialize data for the Nancy Grace Roman Space Telescope.

This package supports data management of large-scale whole-genome sequencing variant calls with thousands of individuals: genotypic data (e.g., SNVs, indels and structural variation calls) and annotations in SeqArray GDS files are stored in an array-oriented and compressed manner, with efficient data access using the R programming language.

The semantic comparisons of Gene Ontology (GO) annotations provide quantitative ways to compute similarities between genes and gene groups, and have became important basis for many bioinformatics analysis approaches. GOSemSim is an R package for semantic similarity computation among GO terms, sets of GO terms, gene products and gene clusters.

This package is used for demultiplexing single-cell sequencing experiments of pooled cells. These cells are labeled with barcode oligonucleotides. The package implements methods to fit regression mixture models for a probabilistic classification of cells, including multiplet detection. Demultiplexing error rates can be estimated, and methods for quality control are provided.

This package provides functions that:

1. find the minimum/maximum of a linear or quadratic function,

2. sample an underdetermined or overdetermined system,

3. solve a linear system Ax=B for the unknown x.

It includes banded and tridiagonal linear systems. The package calls Fortran functions from LINPACK.

In S3 generics, it's useful to take ... so that methods can have additional arguments. But this flexibility comes at a cost: misspelled arguments will be silently ignored. The ellipsis package is an experiment that allows a generic to warn if any arguments passed in ... are not used.

CiteFuse pacakage implements a suite of methods and tools for CITE-seq data from pre-processing to integrative analytics, including doublet detection, network-based modality integration, cell type clustering, differential RNA and protein expression analysis, ADT evaluation, ligand-receptor interaction analysis, and interactive web-based visualisation of the analyses.

TCGA processed RNA-Seq data for 9264 tumor and 741 normal samples across 24 cancer types and made them available as GEO accession [GSE62944](http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE62944). GSE62944 data have been parsed into a SummarizedExperiment object available in ExperimentHub.

Useful functions to work with sequence motifs in the analysis of genomics data. These include methods to annotate genomic regions or sequences with predicted motif hits and to identify motifs that drive observed changes in accessibility or expression. Functions to produce informative visualizations of the obtained results are also provided.

Enables a user to consume the BambooHR API endpoints using R. The actual URL of the API will depend on your company domain, and will be handled by the package automatically once you setup the config file. The API documentation can be found here <https://documentation.bamboohr.com/docs>.

This package implements the First Fit Decreasing algorithm to achieve one dimensional heuristic bin packing. Runtime is of order O(n log(n)) where n is the number of items to pack. See "The Art of Computer Programming Vol. 1" by Donald E. Knuth (1997, ISBN: 0201896834) for more details.

Computes genomic breeding values using external information on the markers. The package fits a linear mixed model with heteroscedastic random effects, where the random effect variance is fitted using a linear predictor and a log link. The method is described in Mouresan, Selle and Ronnegard (2019) <doi:10.1101/636746>.

Write executable specifications in a natural language that describes how your code should behave. Write specifications in feature files using Gherkin language and execute them using functions implemented in R. Use them as an extension to your testthat tests to provide a high level description of how your code works.