Generalized eigenvalues and eigenvectors use QZ decomposition (generalized Schur decomposition). The decomposition needs an N-by-N non-symmetric matrix A or paired matrices (A,B) with eigenvalues reordering mechanism. The decomposition functions are mainly based Fortran subroutines in complex*16 and double precision of LAPACK library (version 3.10.0 or later).

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.

Fits non-crossing regression quantiles as a function of linear covariates and multiple smooth terms, including varying coefficients, via B-splines with L1-norm difference penalties. Random intercepts and variable selection are allowed via the lasso penalties. The smoothing parameters are estimated as part of the model fitting, see Muggeo and others (2021) <doi:10.1177/1471082X20929802>. Monotonicity and concavity constraints on the fitted curves are allowed, see Muggeo and others (2013) <doi:10.1007/s10651-012-0232-1>, and also <doi:10.13140/RG.2.2.12924.85122> or <doi:10.13140/RG.2.2.29306.21445> some code examples.

Support package for the textbook "An Introduction to Quantitative Text Analysis for Linguists: Reproducible Research Using R" (Francom, 2024) <doi:10.4324/9781003393764>. Includes functions to acquire, clean, and analyze text data as well as functions to document and share the results of text analysis. The package is designed to be used in conjunction with the book, but can also be used as a standalone package for text analysis.

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.

This package provides a collection of tools associated with the qdap package that may be useful outside of the context of text analysis.

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.

Nonlinear and Penalized parametric modeling of quantile regression coefficient functions. Sottile G, Frumento P, Chiodi M and Bottai M (2020) <doi:10.1177/1471082X19825523>.

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>.

Syntax for defining complex filtering expressions in a programmatic way. A filtering query, built as a nested list configuration, can be easily stored in other formats like YAML or JSON'. What's more, it's possible to convert such configuration to a valid expression that can be applied to popular dplyr package operations.

This package provides a tool for automatic generation of sibling items from a parent item model defined by the user. It is an implementation of the process automatic item generation (AIG) focused on generating quantitative multiple-choice type of items (see Embretson, Kingston (2018) <doi:10.1111/jedm.12166>).

Developed to perform the estimation and inference for regression coefficient parameters in longitudinal marginal models using the method of quadratic inference functions. Like generalized estimating equations, this method is also a quasi-likelihood inference method. It has been showed that the method gives consistent estimators of the regression coefficients even if the correlation structure is misspecified, and it is more efficient than GEE when the correlation structure is misspecified. Based on Qu, A., Lindsay, B.G. and Li, B. (2000) <doi:10.1093/biomet/87.4.823>.

Translate SQL SELECT statements into lists of R expressions.

For fitting N-mixture models using either FFT or asymptotic approaches. FFT N-mixture models extend the work of Cowen et al. (2017) <doi:10.1111/biom.12701>. Asymptotic N-mixture models extend the work of Dail and Madsen (2011) <doi:10.1111/j.1541-0420.2010.01465.x>, to consider asymptotic solutions to the open population N-mixture models. The FFT models are derived and described in "Parker, M.R.P., Elliott, L., Cowen, L.L.E. (2022). Computational efficiency and precision for replicated-count and batch-marked hidden population models [Manuscript in preparation]. Department of Statistics and Actuarial Sciences, Simon Fraser University.". The asymptotic models are derived and described in: "Parker, M.R.P., Elliott, L., Cowen, L.L.E., Cao, J. (2022). Fast asymptotic solutions for N-mixtures on large populations [Manuscript in preparation]. Department of Statistics and Actuarial Sciences, Simon Fraser University.".

This package provides functions/methods to accompany the book Quantitative Risk Management: Concepts, Techniques and Tools by Alexander J. McNeil, Ruediger Frey, and Paul Embrechts.

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.

This package provides a collection of routines for finding reference limits using, where appropriate, QQ methodology. All use a data vector X of cases from the reference population. The default is to get the central 95% reference range of the population, namely the 2.5 and 97.5 percentile, with optional adjustment of the range. Along with the reference limits, we want confidence intervals which, for historical reasons, are typically at 90% confidence. A full analysis provides six numbers: â the upper and the lower reference limits, and - each of their confidence intervals. For application details, see Hawkins and Esquivel (2024) <doi:10.1093/jalm/jfad109>.

This package provides functions for assigning treatments in randomized experiments using near-optimal threshold blocking. The package is made with large data sets in mind and derives blocks more than an order of magnitude quicker than other methods.

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.(2023) <doi:10.1101/2024.03.17.585412>. 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.

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.

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>.

Quantile-based estimators (Q-estimators) can be used to fit any parametric distribution, using its quantile function. Q-estimators are usually more robust than standard maximum likelihood estimators. The method is described in: Sottile G. and Frumento P. (2022). Robust estimation and regression with parametric quantile functions. <doi:10.1016/j.csda.2022.107471>.

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 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.