This package implements the Quantification Evidence Standard algorithm for computing Bayesian evidence sufficiency from binary evidence matrices. It provides posterior estimates, credible intervals, percentiles, and optional visual summaries. The method is universal, reproducible, and independent of any specific clinical or rule based framework. For details see The Quantitative Omics Epidemiology Group et al. (2025) <doi:10.64898/2025.12.02.25341503>.

Modifies the distance matrix obtained from data with batch effects, so as to improve the performance of sample pattern detection, such as clustering, dimension reduction, and construction of networks between subjects. The method has been published in Bioinformatics (Fei et al, 2018, <doi:10.1093/bioinformatics/bty117>). Also available on GitHub <https://github.com/tengfei-emory/QuantNorm>.

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.

Converts R scripts (.R) into Quarto markdown documents (.qmd) with automatic formatting. Recognizes RStudio code sections, preserves comments as narrative text, extracts metadata from special comments, and provides both programmatic functions and an interactive RStudio add-in for easy conversion.

This package provides a collection of tools associated with the qdap package that may be useful outside of the context of text analysis.

Molecular descriptors and outcomes for several public domain data sets.

The QRI_func() function performs quantile regression analysis using age and sex as predictors to calculate the Quantile Regression Index (QRI) score for each individualâ s regional brain imaging metrics and then averages across the regional scores to generate an average tissue specific score for each subject. The QRI_plot() is used to plot QRI and generate the normative curves for individual measurements.

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.

Integration of the units and errors packages for a complete quantity calculus system for R vectors, matrices and arrays, with automatic propagation, conversion, derivation and simplification of magnitudes and uncertainties. Documentation about units and errors is provided in the papers by Pebesma, Mailund & Hiebert (2016, <doi:10.32614/RJ-2016-061>) and by Ucar, Pebesma & Azcorra (2018, <doi:10.32614/RJ-2018-075>), included in those packages as vignettes; see citation("quantities") for details.

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.

The queueing model of visual search models the accuracy and response time data in a visual search experiment using queueing models with finite customer population and stopping criteria of completing the service for finite number of customers. It implements the conceptualization of a hybrid model proposed by Moore and Wolfe (2001), in which visual stimuli enter the processing one after the other and then are identified in parallel. This package provides functions that simulate the specified queueing process and calculate the Wasserstein distance between the empirical response times and the model prediction.

Provide access to QUANTkiosk platform of regulatory and corporate data relevant to financial markets including point-in-time fundamentals, ownership and symbology.

This package implements the Quantile Autoregressive Distributed Lag (QADF) unit root test proposed by Koenker and Xiao (2004) <doi:10.1198/016214504000001114>. The test examines unit root behaviour across the conditional distribution of a time series using quantile regression, providing a richer characterisation of persistence than standard ADF tests. Critical values follow Hansen (1995) <doi:10.1017/S0266466600009713>. Lag order selection is supported via AIC, BIC, or the t-statistic sequential testing approach.

The computation of quadratic form (QF) distributions is often not trivial and it requires numerical routines. The package contains functions aimed at evaluating the exact distribution of quadratic forms (QFs) and ratios of QFs. In particular, we propose to evaluate density, quantile and distribution functions of positive definite QFs and ratio of independent positive QFs by means of an algorithm based on the numerical inversion of Mellin transforms.

This package provides comprehensive methods for testing, estimating, and conducting uniform inference on quantile treatment effects (QTEs) in sharp regression discontinuity (RD) designs, incorporating covariates and implementing robust bias correction methods of Qu, Yoon, Perron (2024) <doi:10.1162/rest_a_01168>.

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

Automates many of the tasks associated with quantitative discourse analysis of transcripts containing discourse including frequency counts of sentence types, words, sentences, turns of talk, syllables and other assorted analysis tasks. The package provides parsing tools for preparing transcript data. Many functions enable the user to aggregate data by any number of grouping variables, providing analysis and seamless integration with other R packages that undertake higher level analysis and visualization of text. This affords the user a more efficient and targeted analysis. qdap is designed for transcript analysis, however, many functions are applicable to other areas of Text Mining/ Natural Language Processing.

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 a quantum computer simulator, to explore these applications in quantum computing techniques, addressing the challenges in studying CD4 lymphocytes and enhancing ART efficacy.

This package provides a high-level pipeline that simplifies text classification into three streamlined steps: preprocessing, model training, and standardized prediction. It unifies the interface for multiple algorithms (including glmnet', ranger', xgboost', and naivebayes') and memory-efficient sparse matrix vectorization methods (Bag-of-Words, Term Frequency, TF-IDF, and Binary). Users can go from raw text to a fully evaluated sentiment model, complete with ROC-optimized thresholds, in just a few function calls. The resulting model artifact automatically aligns the vocabulary of new datasets during the prediction phase, safely appending predicted classes and probability matrices directly to the user's original dataframe to preserve metadata.

Computes normalized cycle threshold (Ct) values (delta Ct) from raw quantitative polymerase chain reaction (qPCR) Ct values and conducts test of significance using t.test(). Plots expression values based from log2(2^(-1*delta delta Ct)) across groups per gene of interest. Methods for calculation of delta delta Ct and relative expression (2^(-1*delta delta Ct)) values are described in: Livak & Schmittgen, (2001) <doi:10.1006/meth.2001.1262>.

In the spirit of Anscombe's quartet, this package includes datasets that demonstrate the importance of visualizing your data, the importance of not relying on statistical summary measures alone, and why additional assumptions about the data generating mechanism are needed when estimating causal effects. The package includes "Anscombe's Quartet" (Anscombe 1973) <doi:10.1080/00031305.1973.10478966>, D'Agostino McGowan & Barrett (2023) "Causal Quartet" <doi:10.48550/arXiv.2304.02683>, "Datasaurus Dozen" (Matejka & Fitzmaurice 2017), "Interaction Triptych" (Rohrer & Arslan 2021) <doi:10.1177/25152459211007368>, "Rashomon Quartet" (Biecek et al. 2023) <doi:10.48550/arXiv.2302.13356>, and Gelman "Variation and Heterogeneity Causal Quartets" (Gelman et al. 2023) <doi:10.48550/arXiv.2302.12878>.

This package provides methods for statistical analysis of count data and quantal data. For the analysis of count data an implementation of the Closure Principle Computational Approach Test ("CPCAT") is provided (Lehmann, R et al. (2016) <doi:10.1007/s00477-015-1079-4>), as well as an implementation of a "Dunnett GLM" approach using a Quasi-Poisson regression (Hothorn, L, Kluxen, F (2020) <doi:10.1101/2020.01.15.907881>). For the analysis of quantal data an implementation of the Closure Principle Fisherâ Freemanâ Halton test ("CPFISH") is provided (Lehmann, R et al. (2018) <doi:10.1007/s00477-017-1392-1>). P-values and no/lowest observed (adverse) effect concentration values are calculated. All implemented methods include further functions to evaluate the power and the minimum detectable difference using a bootstrapping approach.

Manages, builds and computes statistics and datasets for the construction of quarterly (sub-annual) life tables by exploiting micro-data from either a general or an insured population. References: Pavà a and Lledó (2022) <doi:10.1111/rssa.12769>. Pavà a and Lledó (2023) <doi:10.1017/asb.2023.16>. Pavà a and Lledó (2025) <doi:10.1371/journal.pone.0315937>. Acknowledgements: The authors wish to thank Conselleria de Educación, Universidades y Empleo, Generalitat Valenciana (grants AICO/2021/257; CIAICO/2024/031), Ministerio de Ciencia e Innovación (grant PID2021-128228NB-I00) and Fundación Mapfre (grant Modelización espacial e intra-anual de la mortalidad en España. Una herramienta automática para el calculo de productos de vida') for supporting this research.

Researchers working with Qualitative Comparative Analysis (QCA) can use the package to estimate power of a sufficient term using permutation tests. A term can be anything: A condition, conjunction or disjunction of any combination of these. The package further allows users to plot the estimation results and to estimate the number of cases required to achieve a certain level of power, given a prespecified null and alternative hypothesis. Reference for the article introducing power estimation for QCA is: Rohlfing, Ingo (2018) <doi:10.1017/pan.2017.30> (ungated version: <doi:10.17605/OSF.IO/PC4DF>).