Extensions of ggplot2 Q-Q plot functionalities.

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.

Function that implements the Quantum Genetic Algorithm, first proposed by Han and Kim in 2000. This is an R implementation of the python application developed by Lahoz-Beltra (<https://github.com/ResearchCodesHub/QuantumGeneticAlgorithms>). Each optimization problem is represented as a maximization one, where each solution is a sequence of (qu)bits. Following the quantum paradigm, these qubits are in a superposition state: when measuring them, they collapse in a 0 or 1 state. After measurement, the fitness of the solution is calculated as in usual genetic algorithms. The evolution at each iteration is oriented by the application of two quantum gates to the amplitudes of the qubits: (1) a rotation gate (always); (2) a Pauli-X gate (optionally). The rotation is based on the theta angle values: higher values allow a quicker evolution, and lower values avoid local maxima. The Pauli-X gate is equivalent to the classical mutation operator and determines the swap between alfa and beta amplitudes of a given qubit. The package has been developed in such a way as to permit a complete separation between the engine, and the particular problem subject to combinatorial optimization.

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>.

This package provides an infrastructure for efficient processing of large-scale genetic and phenotypic data including core functions for: 1) fitting linear mixed models, 2) constructing marker-based genomic relationship matrices, 3) estimating genetic parameters (heritability and correlation), 4) performing genomic prediction and genetic risk profiling, and 5) single or multi-marker association analyses. Rohde et al. (2019) <doi:10.1101/503631>.

This package provides tools for AI-assisted qualitative data coding using large language models ('LLMs') via the ellmer package, supporting providers including OpenAI', Anthropic', Google', Azure', and local models via Ollama'. Provides a codebook'-based workflow for defining coding instructions and applying them to texts, images, and other data. Includes built-in codebooks for common applications such as sentiment analysis and policy coding, and functions for creating custom codebooks for specific research questions. Supports systematic replication across models and settings, computing inter-coder reliability statistics including Krippendorff's alpha (Krippendorff 2019, <doi:10.4135/9781071878781>) and Fleiss kappa (Fleiss 1971, <doi:10.1037/h0031619>), as well as gold-standard validation metrics including accuracy, precision, recall, and F1 scores following Sokolova and Lapalme (2009, <doi:10.1016/j.ipm.2009.03.002>). Provides audit trail functionality for documenting coding workflows following Lincoln and Guba's (1985, ISBN:0803924313) framework for establishing trustworthiness in qualitative research.

This package implements quantile-based discriminant analysis (QuanDA) for imbalanced classification in high-dimensional, low-sample-size settings. The method fits penalized quantile regression directly on discrete class labels and tunes the quantile level to reflect class imbalance.

An extensive set of functions to perform Qualitative Comparative Analysis: crisp sets ('csQCA'), temporal ('tQCA'), multi-value ('mvQCA') and fuzzy sets ('fsQCA'). QCA is a methodology that bridges the qualitative and quantitative divide in social science research. It uses a Boolean minimization algorithm, resulting in a minimal causal configuration associated with a given phenomenon.

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.

This package provides a sigmoidal quantile function estimator based on a newly defined generalized expectile function. The generalized sigmoidal quantile function can estimate quantiles beyond the range of the data, which is important for certain applications given smaller sample sizes. The package is based on the method introduced in Hutson (2024) <doi:10.1080/03610918.2022.2032161>.

This package provides methods for detecting structural breaks, determining the number of breaks, and estimating break locations in linear quantile regression, using one or multiple quantiles, based on Qu (2008) and Oka and Qu (2011). Applicable to both time series and repeated cross-sectional data. The main function is rq.break(). . References for detailed theoretical and empirical explanations: . (1) Qu, Z. (2008). "Testing for Structural Change in Regression Quantiles." Journal of Econometrics, 146(1), 170-184 <doi:10.1016/j.jeconom.2008.08.006> . (2) Oka, T., and Qu, Z. (2011). "Estimating Structural Changes in Regression Quantiles." Journal of Econometrics, 162(2), 248-267 <doi:10.1016/j.jeconom.2011.01.005>.

Design of QTL (quantitative trait locus) experiments involves choosing which strains to cross, the type of cross, genotyping strategies, phenotyping strategies, and the number of progeny to raise and phenotype. This package provides tools to help make such choices. Sen and others (2007) <doi:10.1007/s00335-006-0090-y>.

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>.

Quantile-based estimators (Q-estimators) can be used to fit any parametric distribution, using its quantile function. Q-estimators are usually more robust than standard maximum likelihood estimators. The method is described in: Sottile G. and Frumento P. (2022). Robust estimation and regression with parametric quantile functions. <doi:10.1016/j.csda.2022.107471>.

Textual statistics functions formerly in the quanteda package. Textual statistics for characterizing and comparing textual data. Includes functions for measuring term and document frequency, the co-occurrence of words, similarity and distance between features and documents, feature entropy, keyword occurrence, readability, and lexical diversity. These functions extend the quanteda package and are specially designed for sparse textual data.

This package provides functions for simulation, estimation, and model selection of finite mixtures of Tukey g-and-h distributions.

Create quantile binned and conditional plots for Exploratory Data Analysis. The package provides several plotting functions that are all based on quantile binning. The plots are created with ggplot2 and patchwork and can be further adjusted.

To construct a model in 2-D space from 2-D nonlinear dimension reduction data and then lift it to the high-dimensional space. Additionally, provides tools to visualise the model overlay the data in 2-D and high-dimensional space. Furthermore, provides summaries and diagnostics to evaluate the nonlinear dimension reduction layout.

PKG_DESC.

This package provides a set of functions of increasing complexity allows users to (1) convert QuadKey-identified datasets, based on Microsoft's Bing Maps Tile System', into Simple Features data frames, (2) transform Simple Features data frames into rasters, and (3) process multiple Meta ('Facebook') QuadKey-identified human mobility files directly into raster files. For more details, see Dâ Andrea et al. (2024) <doi:10.21105/joss.06500>.

Parametric modeling of quantile regression coefficient functions.

This package provides methods to determine, smooth and plot quantile periodograms for univariate and multivariate time series. See Kley (2016) <doi:10.18637/jss.v070.i03> for a description and tutorial.

Collect your data on digital marketing campaigns from Quora Ads using the Windsor.ai API <https://windsor.ai/api-fields/>.

An implementation of two interaction indices between extractive activity and groundwater resources based on hazard and vulnerability parameters used in the assessment of natural hazards. One index is based on a discrete choice model and the other is relying on an artificial neural network.