High-throughput analysis of growth curves and fluorescence data using three methods: linear regression, growth model fitting, and smooth spline fit. Analysis of dose-response relationships via smoothing splines or dose-response models. Complete data analysis workflows can be executed in a single step via user-friendly wrapper functions. The results of these workflows are summarized in detailed reports as well as intuitively navigable R data containers. A shiny application provides access to all features without requiring any programming knowledge. The package is described in further detail in Wirth et al. (2023) <doi:10.1038/s41596-023-00850-7>.

An R implementation of quality controlâ based robust LOESS(local polynomial regression fitting) signal correction for metabolomics data analysis, described in Dunn, W., Broadhurst, D., Begley, P. et al. (2011) <doi:10.1038/nprot.2011.335>. The optimisation of LOESS's span parameter using generalized cross-validation (GCV) is provided as an option. In addition to signal correction, qcrlscR includes some utility functions like batch shifting and data filtering.

Calculates the right-tail probability of quadratic forms of Gaussian variables using the skewness-kurtosis ratio matching method, modified Liu-Tang-Zhang method and Satterthwaite-Welch method. The technical details can be found in Hong Zhang, Judong Shen and Zheyang Wu (2022) "A fast and accurate approximation to the distributions of quadratic forms of Gaussian variables" <doi:10.1080/10618600.2021.2000423>.

This package provides functions for assessing and visualizing robustness in Qualitative Comparative Analysis (QCA) workflows built with the QCA package, including calibration thresholds, inclusion cutoffs, frequency cutoffs, case influence, subsample stability, alternative analysis settings, theory-specific condition sets, cluster-specific patterns, and solution summaries. Methods build on Dusa (2019) <doi:10.1007/978-3-319-75668-4> and Ragin (2014, ISBN:9780520280038).

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

The author has retired from academic research. Accordingly, this package should not be considered a validated tool for use in peer-reviewed publications or as the basis for grant applications. Backward compatibility with user-code published in <doi:10.1186/s12859-023-05408-8> and <doi:10.1016/j.labinv.2023.100158> is not maintained in versions >= 0.4.0 (June 2026) of this package. The authors of those publications are the appropriate contacts for reproducibility inquiries.

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 a tool that can be customized to aid in the clean up of ecological data collected using quadrats and can crop quadrats to ensure comparability between quadrats collected under different methodologies.

Fits the solution paths of classical sparse regression models with efficient active set algorithms by solving small sub-problems. Include LASSO, SCAD, MCP, (Sparse) Group-LASSO, Cooperative-LASSO, (Group) LAVA, (Generalized) Fused-Lasso and (Generalized) Elastic-Net. Also provides methods for model selection purpose (information criteria, cross-validation, stability selection).

This package provides functions for the joint analysis of Q sets of p-values obtained for the same list of items. This joint analysis is performed by querying a composite hypothesis, i.e. an arbitrary complex combination of simple hypotheses, as described in Mary-Huard et al. (2021) <doi:10.1093/bioinformatics/btab592> and De Walsche et al.(2025) <doi: 10.1093/nargab/lqaf118>. In this approach, the Q-uplet of p-values associated with each item is distributed as a multivariate mixture, where each of the 2^Q components corresponds to a specific combination of simple hypotheses. The dependence between the p-value series is considered using a Gaussian copula function. A p-value for the composite hypothesis test is derived from the posterior probabilities.

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 a collection of functions for constructing large pairwised comparisons and rating them using Elo rating system with supporting parallel processing. The method of random sample pairs is based on Reservoir Sampling proposed by JVitter (1985) <doi:10.1145/3147.3165>.

Compile R functions annotated with type and shape declarations for extremely fast performance and robust runtime type checking. Supports both just-in-time (JIT) and ahead-of-time (AOT) compilation. Compilation is performed by lowering R code to Fortran.

Reads the versioned exchange files written by the Zotero plugins zotQDA and qdaZ -- coded fragments, code systems, coding histories and team-consensus results -- validates them against the shipped contract, and reproduces the plugin's graphics with ggplot2'. Adds what those plugins deliberately leave out: six agreement coefficients with bootstrap confidence intervals, the reliability of the segmentation itself, chi-squared tests of code by group tables with effect sizes, correspondence analysis, multidimensional scaling and hierarchical clustering of codes. Projects from other programs can be read through the REFI-QDA interchange standard <https://www.qdasoftware.org/>, which makes those analyses available to users of established software that does not offer them; the subset a .qdpx supports is reported on import. Reference files are included, so every function can be tried without a Zotero installation.

The Ensemble Quadratic and Affine Invariant Markov chain Monte Carlo algorithms provide an efficient way to perform Bayesian inference in difficult parameter space geometries. The Ensemble Quadratic Monte Carlo algorithm was developed by Militzer (2023) <doi:10.3847/1538-4357/ace1f1>. The Ensemble Affine Invariant algorithm was developed by Goodman and Weare (2010) <doi:10.2140/camcos.2010.5.65> and it was implemented in Python by Foreman-Mackey et al (2013) <doi:10.48550/arXiv.1202.3665>. The Quadratic Monte Carlo method was shown to perform better than the Affine Invariant method in the paper by Militzer (2023) <doi:10.3847/1538-4357/ace1f1> and the Quadratic Monte Carlo method is the default method used. The Chen-Shao Highest Posterior Density Estimation algorithm is used for obtaining credible intervals and the potential scale reduction factor diagnostic is used for checking the convergence of the chains.

Plotting functions for visualising textual data. Extends quanteda and related packages with plot methods designed specifically for text data, textual statistics, and models fit to textual data. Plot types include word clouds, lexical dispersion plots, scaling plots, network visualisations, and word keyness plots.

Design of QTL (quantitative trait locus) experiments involves choosing which strains to cross, the type of cross, genotyping strategies, phenotyping strategies, and the number of progeny to raise and phenotype. This package provides tools to help make such choices. Sen and others (2007) <doi:10.1007/s00335-006-0090-y>.

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>.

Various quantile-based clustering algorithms: algorithm CU (Common theta and Unscaled variables), algorithm CS (Common theta and Scaled variables through lambda_j), algorithm VU (Variable-wise theta_j and Unscaled variables) and algorithm VW (Variable-wise theta_j and Scaled variables through lambda_j). Hennig, C., Viroli, C., Anderlucci, L. (2019) "Quantile-based clustering." Electronic Journal of Statistics. 13 (2) 4849 - 4883 <doi:10.1214/19-EJS1640>.

QuantLib bindings are provided for R using Rcpp via an evolved version of the initial header-only Quantuccia project offering an subset of QuantLib (now maintained separately just for the calendaring subset). See the included file AUTHORS for a full list of contributors to QuantLib (and hence also Quantuccia').

Create quantile binned and conditional plots for Exploratory Data Analysis. The package provides several plotting functions that are all based on quantile binning. The plots are created with ggplot2 and patchwork and can be further adjusted.

Extensions of ggplot2 Q-Q plot functionalities.

Create surface forms from matrix or raster data for flexible plotting and conversion to other mesh types. The functions quadmesh or triangmesh produce a continuous surface as a mesh3d object as used by the rgl package. This is used for plotting raster data in 3D (optionally with texture), and allows the application of a map projection without data loss and many processing applications that are restricted by inflexible regular grid rasters. There are discrete forms of these continuous surfaces available with dquadmesh and dtriangmesh functions.

Conduct multiple quantitative trait loci (QTL) mapping under the framework of random-QTL-effect linear mixed model. First, each position on the genome is detected in order to obtain a negative logarithm P-value curve against genome position. Then, all the peaks on each effect (additive or dominant) curve are viewed as potential QTL, all the effects of the potential QTL are included in a multi-QTL model, their effects are estimated by empirical Bayes in doubled haploid population or by adaptive lasso in F2 population, and true QTL are identified by likelihood radio test. See Wen et al. (2018) <doi:10.1093/bib/bby058>.