G-computation for a set of time-fixed exposures with quantile-based basis functions, possibly under linearity and homogeneity assumptions. Effect measure modification in this method is a way to assess how the effect of the mixture varies by a binary, categorical or continuous variable. Reference: Alexander P. Keil, Jessie P. Buckley, Katie M. OBrien, Kelly K. Ferguson, Shanshan Zhao, and Alexandra J. White (2019) A quantile-based g-computation approach to addressing the effects of exposure mixtures; <doi:10.1289/EHP5838>.

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>.

The NOT functions, R tricks and a compilation of some simple quick plus often used R codes to improve your scripts. Improve the quality and reproducibility of R scripts.

Estimates QAPE using bootstrap procedures. The residual, parametric and double bootstrap is used. The test of normality using Cholesky decomposition is added. Y pop is defined.

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>.

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).

Mortality rates are typically provided in an abridged format, i.e., by age groups 0, [1, 5], [5, 10]', [10, 15]', and so on. Some applications necessitate a detailed (single) age description. Despite the large number of proposed approaches in the literature, only a few methods ensure great performance at both younger and higher ages. For example, the 6-term Lagrange interpolation function is well suited to mortality interpolation at younger ages (with irregular intervals), but not at older ages. The Karup-King method, on the other hand, performs well at older ages but is not suitable for younger ones. Interested readers can find a full discussion of the two stated methods in the book Shryock, Siegel, and Associates (1993).The Q2q package combines the two methods to allow for the interpolation of mortality rates across all age groups. It begins by implementing each method independently, and then the resulting curves are linked using a 5-age averaged error between the two partial curves.

Automatic generation of maximally distinct qualitative color palettes, optionally tailored to color deficiency. A set of colors or a subspace of a color space is used as input and a final palette of specified size is generated by picking colors that maximize the minimum pairwise difference among the chosen colors. Adaptations to color vision deficiency, background colors, and white points are supported.

Upload raster data and easily create interactive quantitative risk analysis QRA visualizations. Select from numerous color palettes, base-maps, and different configurations.

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

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.

The approach is based on the closed testing procedure to control familywise error rate in a strong sense. The local tests implemented are Wald-type and rank-score. The method is described in De Santis, et al., (2026), <doi:10.48550/arXiv.2511.07999>.

This package provides a collection of (wrapper) functions the creator found useful for quickly placing data summaries and formatted regression results into .Rnw or .Rmd files. Functions for generating commonly used graphics, such as receiver operating curves or Bland-Altman plots, are also provided by qwraps2'. qwraps2 is a updated version of a package qwraps'. The original version qwraps was never submitted to CRAN but can be found at <https://github.com/dewittpe/qwraps/>. The implementation and limited scope of the functions within qwraps2 <https://github.com/dewittpe/qwraps2/> is fundamentally different from qwraps'.

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.

There are three functions: qol, miss_qol and miss_patient takes input of the data set containing the answers of QOL questionnaire. It will compute the three types of domain based scale scores: Global, Functional, and Symptoms. In case of missing data, the miss_qol and miss_patient functions will make the required changes and then calculate the domain-wise scale scores. Finally, provide an output replacing the question columns with the domain-based scale scores in the original data set.

This package provides routines to create some quaternions splines: Barry-Goldman algorithm, De Casteljau algorithm, and Kochanek-Bartels algorithm. The implementations are based on the Python library splines'. Quaternions splines allow to construct spherical curves. References: Barry and Goldman <doi:10.1145/54852.378511>, Kochanek and Bartels <doi:10.1145/800031.808575>.

Nonlinear machine learning tool for classification, clustering and dimensionality reduction. It integrates 12 q-kernel functions and 15 conditional negative definite kernel functions and includes the q-kernel and conditional negative definite kernel version of density-based spatial clustering of applications with noise, spectral clustering, generalized discriminant analysis, principal component analysis, multidimensional scaling, locally linear embedding, sammon's mapping and t-Distributed stochastic neighbor embedding.

Extensions of ggplot2 Q-Q plot functionalities.

Designed to create and display complex tables with R, the rtables R package allows cells in an rtables object to contain any high-dimensional data structure, which can then be displayed with cell-specific formatting instructions. Additionally, the rtables.officer package supports export formats related to the Microsoft Office software suite, including Microsoft Word ('docx') and Microsoft PowerPoint ('pptx').

High level and easy HTTP client for R'. Provides functions for building HTTP queries, including query parameters, body requests, headers, authentication, and more.

PADRINO houses textual representations of Integral Projection Models which can be converted from their table format into full kernels to reproduce or extend an already published analysis. Rpadrino is an R interface to this database. For more information on Integral Projection Models, see Easterling et al. (2000) <doi:10.1890/0012-9658(2000)081[0694:SSSAAN]2.0.CO;2>, Merow et al. (2013) <doi:10.1111/2041-210X.12146>, Rees et al. (2014) <doi:10.1111/1365-2656.12178>, and Metcalf et al. (2015) <doi:10.1111/2041-210X.12405>. See Levin et al. (2021) for more information on ipmr', the engine that powers model reconstruction <doi:10.1111/2041-210X.13683>.

Screens all .R', .Rmd', and .qmd files to extract the name of packages used in a project. This package detects packages called with library(foo)', require(foo)', foo::bar() and use("foo", "bar") and adds these dependencies in the DESCRIPTION file in the sections Depends, Imports, and Suggests.

Facilities for running simulations from ordinary differential equation ('ODE') models, such as pharmacometrics and other compartmental models. A compilation manager translates the ODE model into C, compiles it, and dynamically loads the object code into R for improved computational efficiency. An event table object facilitates the specification of complex dosing regimens (optional) and sampling schedules. NB: The use of this package requires both C and Fortran compilers, for details on their use with R please see Section 6.3, Appendix A, and Appendix D in the "R Administration and Installation" manual. Also the code is mostly released under GPL. The VODE and LSODA are in the public domain. The information is available in the inst/COPYRIGHTS.