This package provides convenience utilities for using DuckDB directly over datasets stored in Azure Data Lake Storage Gen2 (ADLS Gen2, abfss://'). Opens connections configured for Azure-backed Delta Lake and Parquet data, registers Azure credentials as DuckDB secrets, and supports optional repository mirrors for restricted networks. Integrates well with DBI for SQL workflows and with dplyr and dbplyr for lazy table queries.

Various data sets (stocks, stock indices, constituent data, FX, zero-coupon bond yield curves, volatility, commodities) for Quantitative Risk Management practice.

This package provides tools for exploratory statistical analysis of quantum-hardware calibration time series. The package provides simulators for random telegraph noise (RTN), power-law noise, and Ornstein-Uhlenbeck dephasing; Welch and sine-multitaper power spectral density estimators; a lightweight two-state hidden Markov model for switching signals; cumulative sum (CUSUM) and binary-segmentation diagnostics for calibration drift; residual-quantile interval forecasts; and filter-function calculations for illustrative coherence curves. The package includes a reproducible generator of simulated superconducting-qubit calibration records; it does not retrieve authenticated live provider data. Methodological background is provided by Welch (1967) <doi:10.1109/TAU.1967.1161901>, Thomson (1982) <doi:10.1109/PROC.1982.12433>, Rabiner (1989) <doi:10.1109/5.18626>, Page (1954) <doi:10.1093/biomet/41.1-2.100>, Paladino et al. (2014) <doi:10.1103/RevModPhys.86.361>, and Cywinski et al. (2008) <doi:10.1103/PhysRevB.77.174509>.

This package implements the Quantile Autoregressive Distributed Lag (QARDL) model of Cho, Kim and Shin (2015) <doi:10.1016/j.jeconom.2015.01.003>. Estimates quantile-specific long-run (beta), short-run autoregressive (phi), and impact (gamma) parameters. Features include BIC-based automatic lag selection, Error Correction Model (ECM) parameterization, Wald tests for parameter constancy across quantiles, rolling/recursive QARDL estimation, Monte Carlo simulation, and publication-ready output tables.

Helps to perform linear regression analysis by reducing manual effort. Reduces the independent variables based on specified p-value and Variance Inflation Factor (VIF) level.

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.

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.

Density, distribution function, quantile function and random generation for the q-gaussian distribution with parameters mu and sig.

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.

Datasets for the book, A Guide to QTL Mapping with R/qtl. Broman and Sen (2009) <doi:10.1007/978-0-387-92125-9>.

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>).

These functions apply an analysis of variance to incomplete ensembles of climate projections. It provides estimates of climate change responses of all simulation chains and of all uncertainty variables. It has been applied to different ensembles of projections simulated to study the impact of climate change: for climate indicators in Evin et al. (2019) <doi:10.1175/JCLI-D-18-0606.1>; seasonal precipitation and temperature in Evin, Somot and Hingray (2021) <doi:10.5194/esd-12-1543-2021>; hydrological variables in Evin et al. (2026) <doi:10.5194/hess-30-1023-2026>; photovoltaic energy in Bichet et al. (2019) <doi:10.1088/1748-9326/ab500a>.

Given inputs A,B and C, this package solves the matrix equation A*X^2 - B*X - C = 0.

This package implements the Bayesian quantile regression model for binary longitudinal data (QBLD) developed in Rahman and Vossmeyer (2019) <DOI:10.1108/S0731-90532019000040B009>. The model handles both fixed and random effects and implements both a blocked and an unblocked Gibbs sampler for posterior inference.

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

This package implements the Quantile-on-Quantile (QQ) regression methodology developed by Sim and Zhou (2015) <doi:10.1016/j.jbankfin.2015.01.013>. QQ regression estimates the effect that quantiles of one variable have on quantiles of another, capturing the dependence between distributions. The package provides functions for QQ regression estimation, 3D surface visualization with MATLAB'-style color schemes ('Jet', Viridis', Plasma'), heatmaps, contour plots, and quantile correlation analysis. Uses quantreg for quantile regression and plotly for interactive visualizations. Particularly useful for examining relationships between financial variables, oil prices, and stock returns under different market conditions.

Fits non-crossing regression quantiles as a function of linear covariates and multiple smooth terms, including varying coefficients, via B-splines with L1-norm difference penalties. Random intercepts and variable selection are allowed via the lasso penalties. The smoothing parameters are estimated as part of the model fitting, see Muggeo and others (2021) <doi:10.1177/1471082X20929802>. Monotonicity and concavity constraints on the fitted curves are allowed, see Muggeo and others (2013) <doi:10.1007/s10651-012-0232-1>, and also <doi:10.13140/RG.2.2.12924.85122> or <doi:10.13140/RG.2.2.29306.21445> some code examples.

Compute various quantitative genetics parameters from a Generalised Linear Mixed Model (GLMM) estimates. Especially, it yields the observed phenotypic mean, phenotypic variance and additive genetic variance.

This package provides a shiny application for teaching introductory quantitative genetics and plant breeding through interactive simulations. The application relies on established plant breeding and quantitative genetic theory found in Falconer and Mackay (1996, ISBN:0582243025) and Bernardo (2010, ISBN:978-0972072427).

This package provides functions for quickly writing (and reading back) a data.frame to file in SQLite format. The name stands for *Store Tables using SQLite'*, or alternatively for *Quick Store Tables* (either way, it could be pronounced as *Quest*). For data.frames containing the supported data types it is intended to work as a drop-in replacement for the write_*() and read_*() functions provided by similar packages.

Qiita is a technical knowledge sharing and collaboration platform for programmers. See <https://qiita.com/api/v2/docs> for more information.

This package provides a quantum computer simulator framework with up to 24 qubits. It allows to define general single qubit gates and general controlled single qubit gates. For convenience, it currently provides the most common gates (X, Y, Z, H, Z, S, T, Rx, Ry, Rz, CNOT, SWAP, Toffoli or CCNOT, Fredkin or CSWAP). qsimulatR also implements noise models. qsimulatR supports plotting of circuits and is able to export circuits to Qiskit <https://qiskit.org/>, a python package which can be used to run on IBM's hardware <https://quantum-computing.ibm.com/>.

Joint estimation of quantile specific intercept and slope parameters in a linear regression setting.

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