This package provides different specifications of a Quadrilateral Dissimilarity Model which can be used to fit same-different judgments in order to get a predicted matrix that satisfies regular minimality [Colonius & Dzhafarov, 2006, Measurement and representations of sensations, Erlbaum]. From such a matrix, Fechnerian distances can be computed.

This package provides functions for constructing near-optimal generalized full matching. Generalized full matching is an extension of the original full matching method to situations with more intricate study designs. The package is made with large data sets in mind and derives matches more than an order of magnitude quicker than other methods.

Scaling models and classifiers for sparse matrix objects representing textual data in the form of a document-feature matrix. Includes original implementations of Laver', Benoit', and Garry's (2003) <doi:10.1017/S0003055403000698>, Wordscores model, the Perry and Benoit (2017) <doi:10.48550/arXiv.1710.08963> class affinity scaling model, and the Slapin and Proksch (2008) <doi:10.1111/j.1540-5907.2008.00338.x> wordfish model, as well as methods for correspondence analysis, latent semantic analysis, and fast Naive Bayes and linear SVMs specially designed for sparse textual data.

Produce quantile-based box-and-whisker plot(s).

For QTL mapping, this package comprises several functions designed to execute diverse tasks, such as simulating or analyzing data, calculating significance thresholds, and visualizing QTL mapping results. The single-QTL or multiple-QTL method, which enables the fitting and comparison of various statistical models, is employed to analyze the data for estimating QTL parameters. The models encompass linear regression, permutation tests, normal mixture models, and truncated normal mixture models. The Gaussian stochastic process is utilized to compute significance thresholds for QTL detection on a genetic linkage map within experimental populations. Two types of data, complete genotyping, and selective genotyping data from various experimental populations, including backcross, F2, recombinant inbred (RI) populations, and advanced intercrossed (AI) populations, are considered in the QTL mapping analysis. For QTL hotspot detection, statistical methods can be developed based on either utilizing individual-level data or summarized data. We have proposed a statistical framework capable of handling both individual-level data and summarized QTL data for QTL hotspot detection. Our statistical framework can overcome the underestimation of thresholds resulting from ignoring the correlation structure among traits. Additionally, it can identify different types of hotspots with minimal computational cost during the detection process. Here, we endeavor to furnish the R codes for our QTL mapping and hotspot detection methods, intended for general use in genes, genomics, and genetics studies. The QTL mapping methods for the complete and selective genotyping designs are based on the multiple interval mapping (MIM) model proposed by Kao, C.-H. , Z.-B. Zeng and R. D. Teasdale (1999) <doi: 10.1534/genetics.103.021642> and H.-I Lee, H.-A. Ho and C.-H. Kao (2014) <doi: 10.1534/genetics.114.168385>, respectively. The QTL hotspot detection analysis is based on the method by Wu, P.-Y., M.-.H. Yang, and C.-H. Kao (2021) <doi: 10.1093/g3journal/jkab056>.

This package provides a high-level wrapper that simplifies text classification into three streamlined steps: preprocessing, model training, and prediction. It unifies the interface for multiple algorithms (including glmnet', ranger', and xgboost') and vectorization methods (Bag-of-Words, Term Frequency-Inverse Document Frequency (TF-IDF)), allowing users to go from raw text to a trained sentiment model in two function calls. The resulting model artifact automatically handles preprocessing for new datasets in the third step, ensuring consistent prediction pipelines.

Quality control of chromatin immunoprecipitation libraries (ChIP-seq) by quantitative polymerase chain reaction (qPCR). This function calculates Enrichment value with respect to reference for each histone modification (specific to Vii7 software <http://www.thermofisher.com/ca/en/home/life-science/pcr/real-time-pcr/real-time-pcr-instruments/viia-7-real-time-pcr-system/viia-7-software.html>). This function is applicable to full panel of histone modifications described by International Human Epigenomic Consortium (IHEC).

Property based testing, inspired by the original QuickCheck'. This package builds on the property based testing framework provided by hedgehog and is designed to seamlessly integrate with testthat'.

This package provides functions for estimating the potential dispersal of tree species using regeneration densities and dispersal distances to nearest seed trees. A quantile regression is implemented to determine the dispersal potential. Spatial prediction can be used to identify natural regeneration potential for forest restoration as described in Axer et al (2021) <doi:10.1016/j.foreco.2020.118802>.

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

Adds an LLM-powered chatbot to your shiny app, that can turn your users natural language questions into SQL queries that run against your data, and return the result as a reactive data frame. Use it to drive reactive calculations, visualizations, downloads, and more.

Shewhart quality control charts for continuous, attribute and count data. Cusum and EWMA charts. Operating characteristic curves. Process capability analysis. Pareto chart and cause-and-effect chart. Multivariate control charts.

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.

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

This package provides methods for estimation of mean- and quantile-optimal treatment regimes from censored data. Specifically, we have developed distinct functions for three types of right censoring for static treatment using quantile criterion: (1) independent/random censoring, (2) treatment-dependent random censoring, and (3) covariates-dependent random censoring. It also includes a function to estimate quantile-optimal dynamic treatment regimes for independent censored data. Finally, this package also includes a simulation data generative model of a dynamic treatment experiment proposed in literature.

Execute multi-step SQL workflows by leveraging specially formatted comments to define and control execution. This enables users to mix queries, commands, and metadata within a single script. Results are returned as named objects for use in downstream workflows.

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>.

Quantile-frequency analysis (QFA) of time series based on trigonometric quantile regression. Spline quantile regression (SQR) for regression coefficient estimation. References: [1] Li, T.-H. (2012) "Quantile periodograms," Journal of the American Statistical Association, 107, 765â 776, <doi:10.1080/01621459.2012.682815>. [2] Li, T.-H. (2014) Time Series with Mixed Spectra, CRC Press, <doi:10.1201/b15154> [3] Li, T.-H. (2022) "Quantile Fourier transform, quantile series, and nonparametric estimation of quantile spectra," <doi:10.48550/arXiv.2211.05844>. [4] Li, T.-H. (2024) "Quantile crossing spectrum and spline autoregression estimation," <doi:10.48550/arXiv.2412.02513>. [5] Li, T.-H. (2024) "Spline autoregression method for estimation of quantile spectrum," <doi:10.48550/arXiv.2412.17163>. [6] Li, T.-H., and Megiddo, N. (2025) "Spline quantile regression," <doi:10.48550/arXiv.2501.03883>.

This package provides functions to Simultaneously Infer Causal Graphs and Genetic Architecture. Includes acyclic and cyclic graphs for data from an experimental cross with a modest number (<10) of phenotypes driven by a few genetic loci (QTL). Chaibub Neto E, Keller MP, Attie AD, Yandell BS (2010) Causal Graphical Models in Systems Genetics: a unified framework for joint inference of causal network and genetic architecture for correlated phenotypes. Annals of Applied Statistics 4: 320-339. <doi:10.1214/09-AOAS288>.

Evaluates moments of ratios (and products) of quadratic forms in normal variables, specifically using recursive algorithms developed by Bao and Kan (2013) <doi:10.1016/j.jmva.2013.03.002> and Hillier et al. (2014) <doi:10.1017/S0266466613000364>. Also provides distribution, quantile, and probability density functions of simple ratios of quadratic forms in normal variables with several algorithms. Originally developed as a supplement to Watanabe (2023) <doi:10.1007/s00285-023-01930-8> for evaluating average evolvability measures in evolutionary quantitative genetics, but can be used for a broader class of statistics. Generating functions for these moments are also closely related to the top-order zonal and invariant polynomials of matrix arguments.

Quantile correlation-sure independence screening (QC-SIS) and composite quantile correlation-sure independence screening (CQC-SIS) for ultrahigh-dimensional data.

This package provides a high-level plotting system, compatible with `ggplot2` objects, maps from `sf`, `terra`, `raster`, `sp`. It is built primarily on the grid package. The objective of the package is to provide a plotting system that is built for speed and modularity. This is useful for quick visualizations when testing code and for plotting multiple figures to the same device from independent sources that may be independent of one another (i.e., different function or modules the create the visualizations).

Resources, tutorials, and code snippets dedicated to exploring the intersection of quantum computing and artificial intelligence (AI) in the context of analyzing Cluster of Differentiation 4 (CD4) lymphocytes and optimizing antiretroviral therapy (ART) for human immunodeficiency virus (HIV). With the emergence of quantum artificial intelligence and the development of small-scale quantum computers, there's an unprecedented opportunity to revolutionize the understanding of HIV dynamics and treatment strategies. This project leverages the R package qsimulatR (Ostmeyer and Urbach, 2023, <https://CRAN.R-project.org/package=qsimulatR>), a quantum computer simulator, to explore these applications in quantum computing techniques, addressing the challenges in studying CD4 lymphocytes and enhancing ART efficacy.