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

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.

Adds an LLM-powered chatbot to your shiny app, that can turn your users natural language questions into SQL queries that run against your data, and return the result as a reactive data frame. Use it to drive reactive calculations, visualizations, downloads, and more.

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.

Molecular descriptors and outcomes for several public domain data sets.

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>.

Estimation methods for optimal treatment regimes under three different criteria, namely marginal quantile, marginal mean, and mean absolute difference. For the first two criteria, both one-stage and two-stage estimation method are implemented. A doubly robust estimator for estimating the quantile-optimal treatment regime is also included.

Web-based interactive charts (using D3.js) for the analysis of experimental crosses to identify genetic loci (quantitative trait loci, QTL) contributing to variation in quantitative traits. Broman (2015) <doi:10.1534/genetics.114.172742>.

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>.

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.

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.

Run lapply() calls in parallel by submitting them to gridengine clusters using the qsub command.

This package provides a high-level plotting system, compatible with `ggplot2` objects, maps from `sf`, `terra`, `raster`, `sp`. It is built primarily on the grid package. The objective of the package is to provide a plotting system that is built for speed and modularity. This is useful for quick visualizations when testing code and for plotting multiple figures to the same device from independent sources that may be independent of one another (i.e., different function or modules the create the visualizations).

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.

Calculates the number of four-taxon subtrees consistent with a pair of cladograms, calculating the symmetric quartet distance of Bandelt & Dress (1986), Reconstructing the shape of a tree from observed dissimilarity data, Advances in Applied Mathematics, 7, 309-343 <doi:10.1016/0196-8858(86)90038-2>, and using the tqDist algorithm of Sand et al. (2014), tqDist: a library for computing the quartet and triplet distances between binary or general trees, Bioinformatics, 30, 2079â 2080 <doi:10.1093/bioinformatics/btu157> for pairs of binary trees.

Implementation of a computationally efficient method for simulating queues with arbitrary arrival and service times. Please see Ebert, Wu, Mengersen & Ruggeri (2020, <doi:10.18637/jss.v095.i05>) for further details.

This function produces both the numerical and graphical summaries of the QTL hotspot detection in the genomes that are available on the worldwide web including the flanking markers of QTLs.

Given a dataset, the user is invited to utilize the Empirical Cumulative Distribution Function (ECDF) to guess interactively the mean and the mean deviation. Thereafter, using the quadratic curve the user can guess the Root Mean Squared Deviation (RMSD) and visualize the standard deviation (SD). For details, see Sarkar and Rashid (2019)<doi:10.3126/njs.v3i0.25574>, Have You Seen the Standard Deviaton?, Nepalese Journal of Statistics, Vol. 3, 1-10.

Resources, tutorials, and code snippets dedicated to exploring the intersection of quantum computing and artificial intelligence (AI) in the context of analyzing Cluster of Differentiation 4 (CD4) lymphocytes and optimizing antiretroviral therapy (ART) for human immunodeficiency virus (HIV). With the emergence of quantum artificial intelligence and the development of small-scale quantum computers, there's an unprecedented opportunity to revolutionize the understanding of HIV dynamics and treatment strategies. This project leverages the R package qsimulatR (Ostmeyer and Urbach, 2023, <https://CRAN.R-project.org/package=qsimulatR>), a quantum computer simulator, to explore these applications in quantum computing techniques, addressing the challenges in studying CD4 lymphocytes and enhancing ART efficacy.

Helper functions for Qualitative Comparative Analysis: evaluate and plot Boolean formulae on fuzzy set score data, apply Boolean operations, compute consistency and coverage measures.

This package provides helper functions to work programmatically within a quarto document. It allows the user to create section headers, tabsets, divs, and spans, and formats these objects into quarto syntax when printed into a document.

Basic functions for building parsers, with an application to PC-AXIS format files.

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

This package provides functions to Simultaneously Infer Causal Graphs and Genetic Architecture. Includes acyclic and cyclic graphs for data from an experimental cross with a modest number (<10) of phenotypes driven by a few genetic loci (QTL). Chaibub Neto E, Keller MP, Attie AD, Yandell BS (2010) Causal Graphical Models in Systems Genetics: a unified framework for joint inference of causal network and genetic architecture for correlated phenotypes. Annals of Applied Statistics 4: 320-339. <doi:10.1214/09-AOAS288>.