High-throughput analysis of growth curves and fluorescence data using three methods: linear regression, growth model fitting, and smooth spline fit. Analysis of dose-response relationships via smoothing splines or dose-response models. Complete data analysis workflows can be executed in a single step via user-friendly wrapper functions. The results of these workflows are summarized in detailed reports as well as intuitively navigable R data containers. A shiny application provides access to all features without requiring any programming knowledge. The package is described in further detail in Wirth et al. (2023) <doi:10.1038/s41596-023-00850-7>.

This package provides a collection of functions is provided by this package to fit quantiles regression models for censored residual lifetimes. It provides various options for regression parameters estimation: the induced smoothing approach (smooth), and L1-minimization (non-smooth). It also implements the estimation methods for standard errors of the regression parameters estimates based on an efficient partial multiplier bootstrap method and robust sandwich estimator. Furthermore, a simultaneous procedure of estimating regression parameters and their standard errors via an iterative updating procedure is implemented (iterative). For more details, see Kim, K. H., Caplan, D. J., & Kang, S. (2022), "Smoothed quantile regression for censored residual life", Computational Statistics, 1-22 <doi:10.1007/s00180-022-01262-z>.

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.

Provide a variety of Q-matrix validation methods for the generalized cognitive diagnosis models, including the method based on the generalized deterministic input, noisy, and gate model (G-DINA) by de la Torre (2011) <DOI:10.1007/s11336-011-9207-7> discrimination index (the GDI method) by de la Torre and Chiu (2016) <DOI:10.1007/s11336-015-9467-8>, the Hull method by Najera et al. (2021) <DOI:10.1111/bmsp.12228>, the stepwise Wald test method (the Wald method) by Ma and de la Torre (2020) <DOI:10.1111/bmsp.12156>, the multiple logistic regressionâ based Qâ matrix validation method (the MLR-B method) by Tu et al. (2022) <DOI:10.3758/s13428-022-01880-x>, the beta method based on signal detection theory by Li and Chen (2024) <DOI:10.1111/bmsp.12371> and Q-matrix validation based on relative fit index by Chen et al. (2013) <DOI:10.1111/j.1745-3984.2012.00185.x>. Different research methods and iterative procedures during Q-matrix validating are available <DOI:10.3758/s13428-024-02547-5>.

Full text, in data frames containing one row per verse, of the Qur'an in Arabic (with and without vowels) and in English (the Yusuf Ali and Saheeh International translations), formatted to be convenient for text analysis.

Run lapply() calls in parallel by submitting them to gridengine clusters using the qsub command.

This package implements the Quantitative Classification-based on Association Rules (QCBA) algorithm (<doi:10.1007/s10489-022-04370-x>). QCBA postprocesses rule classification models making them typically smaller and in some cases more accurate. Supported are CBA implementations from rCBA', arulesCBA and arc packages, and CPAR', CMAR', FOIL2 and PRM implementations from arulesCBA package and SBRL implementation from the sbrl package. The result of the post-processing is an ordered CBA-like rule list.

This package implements moving-blocks bootstrap and extended tapered-blocks bootstrap, as well as smooth versions of each, for quantile regression in time series. This package accompanies the paper: Gregory, K. B., Lahiri, S. N., & Nordman, D. J. (2018). A smooth block bootstrap for quantile regression with time series. The Annals of Statistics, 46(3), 1138-1166.

This package implements indices of qualitative variation proposed by Wilcox (1973).

This package provides three Quarto website templates as an R project, which are commonly used by academics. Templates for personal websites and course/workshop websites are included, as well as a template with minimal content for customization.

Select optimal functional regression or dichotomized quantile predictors for survival/logistic/numeric outcome and perform optimistic bias correction for any optimally dichotomized numeric predictor(s), as in Yi, et. al. (2023) <doi:10.1016/j.labinv.2023.100158>.

Retrieve protein information from the UniProtKB REST API (see <https://www.uniprot.org/help/api_queries>).

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.

This package provides a set of functions for taking qualitative GIS data, hand drawn on a map, and converting it to a simple features object. These tools are focused on data that are drawn on a map that contains some type of polygon features. For each area identified on the map, the id numbers of these polygons can be entered as vectors and transformed using qualmap.

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/>.

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.

Test whether equality and order constraints hold for all individuals simultaneously by comparing Bayesian mixed models through Bayes factors. A tutorial style vignette and a quickstart guide are available, via vignette("manual", "quid"), and vignette("quickstart", "quid") respectively. See Haaf and Rouder (2017) <doi:10.1037/met0000156>; Haaf, Klaassen and Rouder (2019) <doi:10.31234/osf.io/a4xu9>; and Rouder & Haaf (2021) <doi:10.5334/joc.131>.

This package provides a toolkit for analysis and visualization of data from fluorophore-assisted seed amplification assays, such as Real-Time Quaking-Induced Conversion (RT-QuIC) and Fluorophore-Assisted Protein Misfolding Cyclic Amplification (PMCA). QuICSeedR addresses limitations in existing software by automating data processing, supporting large-scale analysis, and enabling comparative studies of analysis methods. It incorporates methods described in Henderson et al. (2015) <doi:10.1099/vir.0.069906-0>, Li et al. (2020) <doi:10.1038/s41598-021-96127-8>, Rowden et al. (2023) <doi:10.3390/pathogens12020309>, Haley et al. (2013) <doi:10.1371/journal.pone.0081488>, and Mair and Wilcox (2020) <doi:10.3758/s13428-019-01246-w>. Please refer to the original publications for details.

Univariate and multivariate SQC tools that completes and increases the SQC techniques available in R. Apart from integrating different R packages devoted to SQC ('qcc','MSQC'), provides nonparametric tools that are highly useful when Gaussian assumption is not met. This package computes standard univariate control charts for individual measurements, X-bar', S', R', p', np', c', u', EWMA and CUSUM'. In addition, it includes functions to perform multivariate control charts such as Hotelling T2', MEWMA and MCUSUM'. As representative feature, multivariate nonparametric alternatives based on data depth are implemented in this package: r', Q and S control charts. In addition, Phase I and II control charts for functional data are included. This package also allows the estimation of the most complete set of capability indices from first to fourth generation, covering the nonparametric alternatives, and performing the corresponding capability analysis graphical outputs, including the process capability plots. See Flores et al. (2021) <doi:10.32614/RJ-2021-034>.

This package provides functions to compute Euclidean minimum spanning trees using single-, sesqui-, and dual-tree Boruvka algorithms. Thanks to K-d trees, they are fast in spaces of low intrinsic dimensionality. Mutual reachability distances (used in the definition of the HDBSCAN* algorithm) are also supported. The package also features relatively fast fallback minimum spanning tree and nearest-neighbours algorithms for spaces of higher dimensionality. The Python version of quitefastmst is available via PyPI'.

This package performs tuning of clustering models, methods and algorithms including the problem of determining an appropriate number of clusters. Validation of cluster analysis results is performed via quadratic scoring using resampling methods, as in Coraggio, L. and Coretto, P. (2023) <doi:10.1016/j.jmva.2023.105181>.

Simplifies output suppression logic in R packages, as it's common to develop some form of it in R. quietR intends to simplify that problem and allow a set of simple toggle functions to be used to suppress console output.

Presents an explanatory animation of normal quantile-quantile plots based on a water-filling analogy. The animation presents a normal QQ plot as the parametric plot of the water levels in vases defined by two distributions. The distributions decorate the axes in the normal QQ plot and are optionally shown as vases adjacent to the plot. The package draws QQ plots for several distributions, either as samples or continuous functions.

This package provides a Quantile Rank-score based test for the identification of expression quantitative trait loci.