This function aims to calculate risk of developing cardiovascular disease of individual patients in next 10 years. This unofficial package was based on published open-sourced free risk prediction algorithm QRISK3-2017 <https://qrisk.org/src.php>.

Programmatically access the Quickbase JSON API <https://developer.quickbase.com>. You supply parameters for an API call, qbr delivers an http request to the API endpoint and returns its response. Outputs follow tidyverse philosophy.

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 functions for constructing near-optimal generalized full matching. Generalized full matching is an extension of the original full matching method to situations with more intricate study designs. The package is made with large data sets in mind and derives matches more than an order of magnitude quicker than other methods.

Molecular descriptors and outcomes for several public domain data sets.

This package provides tools for an automated identification of diagnostic molecular characters, i.e. such columns in a given nucleotide or amino acid alignment that allow to distinguish taxa from each other. These characters can then be used to complement the formal descriptions of the taxa, which are often based on morphological and anatomical features. Especially for morphologically cryptic species, this will be helpful. QUIDDICH distinguishes between four different types of diagnostic characters. For more information, see "Kuehn, A.L., Haase, M. 2019. QUIDDICH: QUick IDentification of DIagnostic CHaracters.".

Supports risk assessors in performing the entry step of the quantitative Pest Risk Assessment. It allows the estimation of the amount of a plant pest entering a risk assessment area (in terms of founder populations) through the calculation of the imported commodities that could be potential pathways of pest entry, and the development of a pathway model. Two Shiny apps based on the functionalities of the package are included, that simplify the process of assessing the risk of entry of plant pests. The approach is based on the work of the European Food Safety Authority (EFSA PLH Panel et al., 2018) <doi:10.2903/j.efsa.2018.5350>.

Enables the user to calculate Value at Risk (VaR) and Expected Shortfall (ES) by means of various types of historical simulation. Currently plain-, age-, volatility-weighted- and filtered historical simulation are implemented in this package. Volatility weighting can be carried out via an exponentially weighted moving average model (EWMA) or other GARCH-type models. The performance can be assessed via Traffic Light Test, Coverage Tests and Loss Functions. The methods of the package are described in Gurrola-Perez, P. and Murphy, D. (2015) <https://EconPapers.repec.org/RePEc:boe:boeewp:0525> as well as McNeil, J., Frey, R., and Embrechts, P. (2015) <https://ideas.repec.org/b/pup/pbooks/10496.html>.

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

Quantile regression (QR) for Nonlinear Mixed-Effects Models via the asymmetric Laplace distribution (ALD). It uses the Stochastic Approximation of the EM (SAEM) algorithm for deriving exact maximum likelihood estimates and full inference result is for the fixed-effects and variance components. It also provides prediction and graphical summaries for assessing the algorithm convergence and fitting results.

General purpose toolbox for simulating quantum versions of game theoretic models (Flitney and Abbott 2002) <arXiv:quant-ph/0208069>. Quantum (Nielsen and Chuang 2010, ISBN:978-1-107-00217-3) versions of models that have been handled are: Penny Flip Game (David A. Meyer 1998) <arXiv:quant-ph/9804010>, Prisoner's Dilemma (J. Orlin Grabbe 2005) <arXiv:quant-ph/0506219>, Two Person Duel (Flitney and Abbott 2004) <arXiv:quant-ph/0305058>, Battle of the Sexes (Nawaz and Toor 2004) <arXiv:quant-ph/0110096>, Hawk and Dove Game (Nawaz and Toor 2010) <arXiv:quant-ph/0108075>, Newcomb's Paradox (Piotrowski and Sladkowski 2002) <arXiv:quant-ph/0202074> and Monty Hall Problem (Flitney and Abbott 2002) <arXiv:quant-ph/0109035>.

Simulating and estimating peer effect models including the quantile-based specification (Houndetoungan, 2025 <doi:10.48550/arXiv.2506.12920>), and the models with Constant Elasticity of Substitution (CES)-based social norm (Boucher et al., 2024 <doi:10.3982/ECTA21048>).

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.

Uses the fst package to store genotype probabilities on disk for the qtl2 package. These genotype probabilities are a central data object for mapping quantitative trait loci (QTL), but they can be quite large. The facilities in this package enable the genotype probabilities to be stored on disk, leading to reduced memory usage with only a modest increase in computation time.

Translate SQL SELECT statements into lists of R expressions.

Nomograms are constructed to predict the cumulative incidence rate which is calculated after adjusting for competing causes to the event of interest. K-fold cross-validation is implemented to validate predictive accuracy using a competing-risk version of the concordance index. Methods are as described in: Kattan MW, Heller G, Brennan MF (2003).

This package provides functions to plot QTL (quantitative trait loci) analysis results and related diagnostics. Part of qtl2', an upgrade of the qtl package to better handle high-dimensional data and complex cross designs.

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).

Evaluates moments of ratios (and products) of quadratic forms in normal variables, specifically using recursive algorithms developed by Bao and Kan (2013) <doi:10.1016/j.jmva.2013.03.002> and Hillier et al. (2014) <doi:10.1017/S0266466613000364>. Also provides distribution, quantile, and probability density functions of simple ratios of quadratic forms in normal variables with several algorithms. Originally developed as a supplement to Watanabe (2023) <doi:10.1007/s00285-023-01930-8> for evaluating average evolvability measures in evolutionary quantitative genetics, but can be used for a broader class of statistics. Generating functions for these moments are also closely related to the top-order zonal and invariant polynomials of matrix arguments.

Efficiently solve quadratic problems with linear inequality, equality and box constraints. The method used is outlined in D. Goldfarb, and A. Idnani (1983) <doi:10.1007/BF02591962>.

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'.

Construct message-passing style objects with types and features. Q7 types uses composition instead of inheritance in creating derived types, allowing defining any code segment as feature and associating any feature to any object. Compared to R6, Q7 is simpler and more flexible, and is more friendly in syntax.

This software provides tools for quantitative trait mapping in populations such as advanced intercross lines where relatedness among individuals should not be ignored. It can estimate background genetic variance components, impute missing genotypes, simulate genotypes, perform a genome scan for putative quantitative trait loci (QTL), and plot mapping results. It also has functions to calculate identity coefficients from pedigrees, especially suitable for pedigrees that consist of a large number of generations, or estimate identity coefficients from genotypic data in certain circumstances.

This package provides functions for estimating ploidy levels and detecting aneuploidy in individuals using allele intensities or allele count data from high-throughput genotyping platforms, including single nucleotide polymorphism (SNP) arrays and sequencing-based technologies. Implements an extended version of the PennCNV signal standardization method by Wang et al. (2007) <doi:10.1101/gr.6861907> for higher ploidy levels. Computes B-allele frequencies (BAF), z-scores, and identifies copy number variation patterns.