This package provides functions to access survey results directly into R using the Qualtrics API. Qualtrics <https://www.qualtrics.com/about/> is an online survey and data collection software platform. See <https://api.qualtrics.com/> for more information about the Qualtrics API. This package is community-maintained and is not officially supported by Qualtrics'.

Molecular descriptors and outcomes for several public domain data sets.

The modeling and prediction of graph-associated time series(GATS) based on continuous time quantum walk. This software is mainly used for feature extraction, modeling, prediction and result evaluation of GATS, including continuous time quantum walk simulation, feature selection, regression analysis, time series prediction, and series fit calculation. A paper is attached to the package for reference.

Developed to perform the estimation and inference for regression coefficient parameters in longitudinal marginal models using the method of quadratic inference functions. Like generalized estimating equations, this method is also a quasi-likelihood inference method. It has been showed that the method gives consistent estimators of the regression coefficients even if the correlation structure is misspecified, and it is more efficient than GEE when the correlation structure is misspecified. Based on Qu, A., Lindsay, B.G. and Li, B. (2000) <doi:10.1093/biomet/87.4.823>.

These functions use data augmentation and Bayesian techniques for the assessment of single-member and incomplete ensembles of climate projections. It provides unbiased estimates of climate change responses of all simulation chains and of all uncertainty variables. It additionally propagates uncertainty due to missing information in the estimates. - Evin, G., B. Hingray, J. Blanchet, N. Eckert, S. Morin, and D. Verfaillie. (2019) <doi:10.1175/JCLI-D-18-0606.1>.

This is the implementation of quantile regression forests for the fast random forest package ranger'.

Routines in qtl2 to study allele patterns in quantitative trait loci (QTL) mapping over a chromosome. Useful in crosses with more than two alleles to identify how sets of alleles, genetically different strands at the same locus, have different response levels. Plots show profiles over a chromosome. Can handle multiple traits together. See <https://github.com/byandell/qtl2pattern>.

This package implements the robust algorithm for fitting finite mixture models based on quantile regression proposed by Emir et al., 2017 (unpublished).

Create surface forms from matrix or raster data for flexible plotting and conversion to other mesh types. The functions quadmesh or triangmesh produce a continuous surface as a mesh3d object as used by the rgl package. This is used for plotting raster data in 3D (optionally with texture), and allows the application of a map projection without data loss and many processing applications that are restricted by inflexible regular grid rasters. There are discrete forms of these continuous surfaces available with dquadmesh and dtriangmesh functions.

Translate SQL SELECT statements into lists of R expressions.

Dynamically generate tabset panels <https://quarto.org/docs/output-formats/html-basics.html#tabsets> in Quarto HTML documents using a data frame as input.

This package provides tools for (automated and manual) quality control of the results of Epigenome-Wide Association Studies.

Estimate quadratic vector autoregression models with the strong hierarchy using the Regularization Algorithm under Marginality Principle (RAMP) by Hao et al. (2018) <doi:10.1080/01621459.2016.1264956>, compare the performance with linear models, and construct networks with partial derivatives.

This package provides functions for making run charts [Anhoej, Olesen (2014) <doi:10.1371/journal.pone.0113825>] and basic Shewhart control charts [Mohammed, Worthington, Woodall (2008) <doi:10.1136/qshc.2004.012047>] for measure and count data. The main function, qic(), creates run and control charts and has a simple interface with a rich set of options to control data analysis and plotting, including options for automatic data aggregation by subgroups, easy analysis of before-and-after data, exclusion of one or more data points from analysis, and splitting charts into sequential time periods. Missing values and empty subgroups are handled gracefully.

Quick Response codes (QR codes) are a type of matrix bar code and can be used to authenticate transactions, provide access to multi-factor authentication services and enable general data transfer in an image. QR codes use four standardized encoding modes (numeric, alphanumeric, byte/binary, and kanji) to efficiently store data. Matrix barcode generation is performed efficiently in C via the included libqrencoder library created by Kentaro Fukuchi.

Syntax for defining complex filtering expressions in a programmatic way. A filtering query, built as a nested list configuration, can be easily stored in other formats like YAML or JSON'. What's more, it's possible to convert such configuration to a valid expression that can be applied to popular dplyr package operations.

Web-based interactive charts (using D3.js) for the analysis of experimental crosses to identify genetic loci (quantitative trait loci, QTL) contributing to variation in quantitative traits. Broman (2015) <doi:10.1534/genetics.114.172742>.

This package provides functionality for working with raster-like quadtrees (also called â region quadtreesâ ), which allow for variable-sized cells. The package allows for flexibility in the quadtree creation process. Several functions defining how to split and aggregate cells are provided, and custom functions can be written for both of these processes. In addition, quadtrees can be created using other quadtrees as â templatesâ , so that the new quadtree's structure is identical to the template quadtree. The package also includes functionality for modifying quadtrees, querying values, saving quadtrees to a file, and calculating least-cost paths using the quadtree as a resistance surface.

An easy framework to set a quality control workflow on a dataset. Includes a various range of functions that allow to establish an adaptable data quality control.

This package provides seamless access to the QGIS (<https://qgis.org>) processing toolbox using the standalone qgis_process command-line utility. Both native and third-party (plugin) processing providers are supported. Beside referring data sources from file, also common objects from sf', terra and stars are supported. The native processing algorithms are documented by QGIS.org (2024) <https://docs.qgis.org/latest/en/docs/user_manual/processing_algs/>.

Upload raster data and easily create interactive quantitative risk analysis QRA visualizations. Select from numerous color palettes, base-maps, and different configurations.

This package provides tools for (automated and manual) quality control of the results of Genome Wide Association Studies.

The Ensemble Quadratic and Affine Invariant Markov chain Monte Carlo algorithms provide an efficient way to perform Bayesian inference in difficult parameter space geometries. The Ensemble Quadratic Monte Carlo algorithm was developed by Militzer (2023) <doi:10.3847/1538-4357/ace1f1>. The Ensemble Affine Invariant algorithm was developed by Goodman and Weare (2010) <doi:10.2140/camcos.2010.5.65> and it was implemented in Python by Foreman-Mackey et al (2013) <doi:10.48550/arXiv.1202.3665>. The Quadratic Monte Carlo method was shown to perform better than the Affine Invariant method in the paper by Militzer (2023) <doi:10.3847/1538-4357/ace1f1> and the Quadratic Monte Carlo method is the default method used. The Chen-Shao Highest Posterior Density Estimation algorithm is used for obtaining credible intervals and the potential scale reduction factor diagnostic is used for checking the convergence of the chains.

This package implements an adaptively weighted group Lasso procedure for simultaneous variable selection and structure identification in varying coefficient quantile regression models and additive quantile regression models with ultra-high dimensional covariates. The methodology, grounded in a strong sparsity condition, establishes selection consistency under certain weight conditions. To address the challenge of tuning parameter selection in practice, a BIC-type criterion named high-dimensional information criterion (HDIC) is proposed. The Lasso procedure, guided by HDIC-determined tuning parameters, maintains selection consistency. Theoretical findings are strongly supported by simulation studies. (Toshio Honda, Ching-Kang Ing, Wei-Ying Wu, 2019, <DOI:10.3150/18-BEJ1091>).