This package implements the Quantile Composite-based Path Modeling approach (Davino and Vinzi, 2016 <doi:10.1007/s11634-015-0231-9>; Dolce et al., 2021 <doi:10.1007/s11634-021-00469-0>). The method complements the traditional PLS Path Modeling approach, analyzing the entire distribution of outcome variables and, therefore, overcoming the classical exploration of only average effects. It exploits quantile regression to investigate changes in the relationships among constructs and between constructs and observed variables.

Fit quantile regression neural network models with optional left censoring, partial monotonicity constraints, generalized additive model constraints, and the ability to fit multiple non-crossing quantile functions following Cannon (2011) <doi:10.1016/j.cageo.2010.07.005> and Cannon (2018) <doi:10.1007/s00477-018-1573-6>.

Densitometric evaluation of the photo-archived quantitative thin-layer chromatography (TLC) plates.

Quick Response codes (QR codes) are a type of matrix bar code and can be used to authenticate transactions, provide access to multi-factor authentication services and enable general data transfer in an image. QR codes use four standardized encoding modes (numeric, alphanumeric, byte/binary, and kanji) to efficiently store data. Matrix barcode generation is performed efficiently in C via the included libqrencoder library created by Kentaro Fukuchi.

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.

It provides versatile tools for analysis of birth and death based Markovian Queueing Models and Single and Multiclass Product-Form Queueing Networks. It implements M/M/1, M/M/c, M/M/Infinite, M/M/1/K, M/M/c/K, M/M/c/c, M/M/1/K/K, M/M/c/K/K, M/M/c/K/m, M/M/Infinite/K/K, Multiple Channel Open Jackson Networks, Multiple Channel Closed Jackson Networks, Single Channel Multiple Class Open Networks, Single Channel Multiple Class Closed Networks and Single Channel Multiple Class Mixed Networks. Also it provides a B-Erlang, C-Erlang and Engset calculators. This work is dedicated to the memory of D. Sixto Rios Insua.

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.

This package provides functions/methods to accompany the book Quantitative Risk Management: Concepts, Techniques and Tools by Alexander J. McNeil, Ruediger Frey, and Paul Embrechts.

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.

This package provides a collection of text analysis dictionaries and word lists for use with the qdap package.

Values different types of assets and calibrates discount curves for quantitative financial analysis. It covers fixed coupon assets, floating note assets, interest and cross currency swaps with different payment frequencies. Enables the calibration of spot, instantaneous forward and basis curves, making it a powerful tool for accurate and flexible bond valuation and curve generation. The valuation and calibration techniques presented here are consistent with industry standards and incorporates author's own calculations. Tuckman, B., Serrat, A. (2022, ISBN: 978-1-119-83555-4).

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

This package provides a brms'-like interface for fitting Bayesian regression models using INLA (Integrated Nested Laplace Approximations) and TMB (Template Model Builder). The package offers faster model fitting while maintaining familiar brms syntax and output formats. Supports fixed and mixed effects models, multiple probability distributions, conditional effects plots, and posterior predictive checks with summary methods compatible with brms'. TMB integration provides fast ordinal regression capabilities. Implements methods adapted from emmeans for marginal means estimation and bayestestR for Bayesian inference assessment. Methods are based on Rue et al. (2009) <doi:10.1111/j.1467-9868.2008.00700.x>, Kristensen et al. (2016) <doi:10.18637/jss.v070.i05>, Lenth (2016) <doi:10.18637/jss.v069.i01>, Bürkner (2017) <doi:10.18637/jss.v080.i01>, Makowski et al. (2019) <doi:10.21105/joss.01541>, and Kruschke (2014, ISBN:9780124058880).

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.

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

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

This package provides functions to manipulate dates and count days for quantitative finance analysis. The quantdates package considers leap, holidays and business days for relevant calendars in a financial context to simplify quantitative finance calculations, consistent with International Swaps and Derivatives Association (ISDA) (2006) <https://www.isda.org/book/2006-isda-definitions/> regulations.

It will assist the user to find simple quadratic roots from any quadratic equation.

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>.

An easy framework to set a quality control workflow on a dataset. Includes a various range of functions that allow to establish an adaptable data quality control.

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.

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>.

Functionality to read, recode, and transcode data as used in quantitative language comparison, specifically to deal with multilingual orthographic variation (Moran & Cysouw (2018) <doi:10.5281/zenodo.1296780>) and with the recoding of nominal data.

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.