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.

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.

G-computation for a set of time-fixed exposures with quantile-based basis functions, possibly under linearity and homogeneity assumptions. This approach estimates a regression line corresponding to the expected change in the outcome (on the link basis) given a simultaneous increase in the quantile-based category for all exposures. Works with continuous, binary, and right-censored time-to-event outcomes. 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>.

This package provides a set of functions for taking qualitative GIS data, hand drawn on a map, and converting it to a simple features object. These tools are focused on data that are drawn on a map that contains some type of polygon features. For each area identified on the map, the id numbers of these polygons can be entered as vectors and transformed using qualmap.

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.

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 implements moving-blocks bootstrap and extended tapered-blocks bootstrap, as well as smooth versions of each, for quantile regression in time series. This package accompanies the paper: Gregory, K. B., Lahiri, S. N., & Nordman, D. J. (2018). A smooth block bootstrap for quantile regression with time series. The Annals of Statistics, 46(3), 1138-1166.

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.

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

Option pricing (financial derivatives) techniques mainly following textbook Options, Futures and Other Derivatives', 9ed by John C.Hull, 2014. Prentice Hall. Implementations are via binomial tree option model (BOPM), Black-Scholes model, Monte Carlo simulations, etc. This package is a result of Quantitative Financial Risk Management course (STAT 449 and STAT 649) at Rice University, Houston, TX, USA, taught by Oleg Melnikov, statistics PhD student, as of Spring 2015.

This package provides seamless access to the QGIS (<https://qgis.org>) processing toolbox using the standalone qgis_process command-line utility. Both native and third-party (plugin) processing providers are supported. Beside referring data sources from file, also common objects from sf', terra and stars are supported. The native processing algorithms are documented by QGIS.org (2024) <https://docs.qgis.org/latest/en/docs/user_manual/processing_algs/>.

This package provides functions for quickly writing (and reading back) a data.frame to file in SQLite format. The name stands for *Store Tables using SQLite'*, or alternatively for *Quick Store Tables* (either way, it could be pronounced as *Quest*). For data.frames containing the supported data types it is intended to work as a drop-in replacement for the write_*() and read_*() functions provided by similar packages.

Execute multi-step SQL workflows by leveraging specially formatted comments to define and control execution. This enables users to mix queries, commands, and metadata within a single script. Results are returned as named objects for use in downstream workflows.

Joint estimation of quantile specific intercept and slope parameters in a linear regression setting.

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>.

Code for centroid, median and quantile classifiers.

Non-parametric methods as local normal regression, polynomial local regression and penalized cubic B-splines regression are used to estimate quantiles curves. See Fan and Gijbels (1996) <doi:10.1201/9780203748725> and Perperoglou et al.(2019) <doi:10.1186/s12874-019-0666-3>.

This package provides methods for statistical analysis of count data and quantal data. For the analysis of count data an implementation of the Closure Principle Computational Approach Test ("CPCAT") is provided (Lehmann, R et al. (2016) <doi:10.1007/s00477-015-1079-4>), as well as an implementation of a "Dunnett GLM" approach using a Quasi-Poisson regression (Hothorn, L, Kluxen, F (2020) <doi:10.1101/2020.01.15.907881>). For the analysis of quantal data an implementation of the Closure Principle Fisherâ Freemanâ Halton test ("CPFISH") is provided (Lehmann, R et al. (2018) <doi:10.1007/s00477-017-1392-1>). P-values and no/lowest observed (adverse) effect concentration values are calculated. All implemented methods include further functions to evaluate the power and the minimum detectable difference using a bootstrapping approach.

Fits classical sparse regression models with efficient active set algorithms by solving quadratic problems as described by Grandvalet, Chiquet and Ambroise (2017) <doi:10.48550/arXiv.1210.2077>. Also provides a few methods for model selection purpose (cross-validation, stability selection).

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

Datasets for the book, A Guide to QTL Mapping with R/qtl. Broman and Sen (2009) <doi:10.1007/978-0-387-92125-9>.

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.

Offers a suite of functions to prepare questionnaire data for analysis (perhaps other types of data as well). By data preparation, I mean data analytic tasks to get your raw data ready for statistical modeling (e.g., regression). There are functions to investigate missing data, reshape data, validate responses, recode variables, score questionnaires, center variables, aggregate by groups, shift scores (i.e., leads or lags), etc. It provides functions for both single level and multilevel (i.e., grouped) data. With a few exceptions (e.g., ncases()), functions without an "s" at the end of their primary word (e.g., center_by()) act on atomic vectors, while functions with an "s" at the end of their primary word (e.g., centers_by()) act on multiple columns of a data.frame.

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.