_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
      \_\ \       /  \ \\ \ \   / / /     / /  \
      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
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/_/ /      / / /____\/ /       \ \_\\ \/___/ /
\_\/       \/_________/         \/_/ \_____\/

Enter the query into the form above. You can look for specific version of a package by using @ symbol like this: gcc@10.

API method:

GET /api/packages?search=hello&page=1&limit=20

where search is your query, page is a page number and limit is a number of items on a single page. Pagination information (such as a number of pages and etc) is returned in response headers.

If you'd like to join our channel webring send a patch to ~whereiseveryone/toys@lists.sr.ht adding your channel as an entry in channels.scm.


r-qtl-gcimapping 3.4
Propagated dependencies: r-stringr@1.6.0 r-readxl@1.4.5 r-rcpp@1.1.0 r-qtl@1.72 r-openxlsx@4.2.8.1 r-mass@7.3-65 r-lars@1.3 r-glmnet@4.1-10 r-foreach@1.5.2 r-doparallel@1.0.17 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/q.scm (guix-cran packages q)
Home page: https://cran.r-project.org/package=QTL.gCIMapping
Licenses: GPL 2+
Build system: r
Synopsis: QTL Genome-Wide Composite Interval Mapping
Description:

Conduct multiple quantitative trait loci (QTL) and QTL-by-environment interaction (QEI) mapping via ordinary or compressed variance component mixed models with random- or fixed QTL/QEI effects. 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 or on each locus curve are viewed as potential main-effect QTLs and QEIs, all their effects are included in a multi-locus model, their effects are estimated by both least angle regression and empirical Bayes (or adaptive lasso) in backcross and F2 populations, and true QTLs and QEIs are identified by likelihood radio test. See Zhou et al. (2022) <doi:10.1093/bib/bbab596> and Wen et al. (2018) <doi:10.1093/bib/bby058>.

r-qrcmnp 0.2.2
Propagated dependencies: r-survival@3.8-3 r-qrcm@3.2
Channel: guix-cran
Location: guix-cran/packages/q.scm (guix-cran packages q)
Home page: https://cran.r-project.org/package=qrcmNP
Licenses: GPL 2
Build system: r
Synopsis: Nonlinear and Penalized Quantile Regression Coefficients Modeling
Description:

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

r-quantilogram 3.1.1
Propagated dependencies: r-scales@1.4.0 r-rlang@1.1.6 r-quantreg@6.1 r-np@0.60-18 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/q.scm (guix-cran packages q)
Home page: https://cran.r-project.org/package=quantilogram
Licenses: GPL 3+
Build system: r
Synopsis: Cross-Quantilogram
Description:

Estimation and inference methods for the cross-quantilogram. The cross-quantilogram is a measure of nonlinear dependence between two variables, based on either unconditional or conditional quantile functions. It can be considered an extension of the correlogram, which is a correlation function over multiple lag periods that mainly focuses on linear dependency. One can use the cross-quantilogram to detect the presence of directional predictability from one time series to another. This package provides a statistical inference method based on the stationary bootstrap. For detailed theoretical and empirical explanations, see Linton and Whang (2007) for univariate time series analysis and Han, Linton, Oka and Whang (2016) for multivariate time series analysis. The full references for these key publications are as follows: (1) Linton, O., and Whang, Y. J. (2007). The quantilogram: with an application to evaluating directional predictability. Journal of Econometrics, 141(1), 250-282 <doi:10.1016/j.jeconom.2007.01.004>; (2) Han, H., Linton, O., Oka, T., and Whang, Y. J. (2016). The cross-quantilogram: measuring quantile dependence and testing directional predictability between time series. Journal of Econometrics, 193(1), 251-270 <doi:10.1016/j.jeconom.2016.03.001>.

r-qch 2.1.0
Propagated dependencies: r-stringr@1.6.0 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-qvalue@2.42.0 r-purrr@1.2.0 r-ks@1.15.1 r-dplyr@1.1.4 r-copula@1.1-6
Channel: guix-cran
Location: guix-cran/packages/q.scm (guix-cran packages q)
Home page: https://cran.r-project.org/package=qch
Licenses: GPL 3
Build system: r
Synopsis: Query Composite Hypotheses
Description:

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.

r-qgglmm 0.8.0
Propagated dependencies: r-cubature@2.1.4-1
Channel: guix-cran
Location: guix-cran/packages/q.scm (guix-cran packages q)
Home page: https://cran.r-project.org/package=QGglmm
Licenses: GPL 2
Build system: r
Synopsis: Estimate Quantitative Genetics Parameters from Generalised Linear Mixed Models
Description:

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.

r-qr 0.1.3
Propagated dependencies: r-tinytest@1.4.1
Channel: guix-cran
Location: guix-cran/packages/q.scm (guix-cran packages q)
Home page: http://www.netlib.org/lapack/explore-html/df/dc5/group__variants_g_ecomputational_ga3766ea903391b5cf9008132f7440ec7b.html
Licenses: GPL 3
Build system: r
Synopsis: QR Factorization without Pivoting
Description:

This function performs QR factorization without pivoting to a real or complex matrix. It is based on Anderson. E. and ten others (1999) "LAPACK Users Guide". Third Edition. SIAM.

r-qrlmm 2.3
Propagated dependencies: r-quantreg@6.1 r-psych@2.5.6 r-mvtnorm@1.3-3 r-lqr@5.2 r-ald@1.3.1
Channel: guix-cran
Location: guix-cran/packages/q.scm (guix-cran packages q)
Home page: https://cran.r-project.org/package=qrLMM
Licenses: GPL 2+
Build system: r
Synopsis: Quantile Regression for Linear Mixed-Effects Models
Description:

Quantile regression (QR) for Linear 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 results for the fixed-effects and variance components. It also provides graphical summaries for assessing the algorithm convergence and fitting results.

r-qsub 1.1.3
Propagated dependencies: r-tidyr@1.3.1 r-stringr@1.6.0 r-ssh@0.9.4 r-readr@2.1.6 r-random@0.2.6 r-purrr@1.2.0 r-processx@3.8.6 r-pbapply@1.7-4 r-glue@1.8.0 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/q.scm (guix-cran packages q)
Home page: https://github.com/rcannood/qsub
Licenses: GPL 3
Build system: r
Synopsis: Running Commands Remotely on 'Gridengine' Clusters
Description:

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

r-quadform 0.0-4
Channel: guix-cran
Location: guix-cran/packages/q.scm (guix-cran packages q)
Home page: https://github.com/RobinHankin/quadform
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Efficient Evaluation of Quadratic Forms
Description:

This package provides a range of quadratic forms are evaluated, using efficient methods. Unnecessary transposes are not performed. Complex values are handled consistently.

r-qlcdata 0.3
Propagated dependencies: r-yaml@2.3.10 r-stringi@1.8.7 r-shiny@1.11.1 r-phytools@2.5-2 r-docopt@0.7.2 r-data-tree@1.2.0 r-ape@5.8-1
Channel: guix-cran
Location: guix-cran/packages/q.scm (guix-cran packages q)
Home page: https://cran.r-project.org/package=qlcData
Licenses: GPL 3
Build system: r
Synopsis: Processing Data for Quantitative Language Comparison
Description:

Functionality to read, recode, and transcode data as used in quantitative language comparison, specifically to deal with multilingual orthographic variation (Moran & Cysouw (2018) <doi:10.5281/zenodo.1296780>) and with the recoding of nominal data.

r-quoradsr 0.1.0
Propagated dependencies: r-jsonlite@2.0.0
Channel: guix-cran
Location: guix-cran/packages/q.scm (guix-cran packages q)
Home page: https://windsor.ai/
Licenses: GPL 3
Build system: r
Synopsis: Get 'Quora' Ads Data via the 'Windsor.ai' API
Description:

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

r-qmap 1.0-6
Propagated dependencies: r-fitdistrplus@1.2-4
Channel: guix-cran
Location: guix-cran/packages/q.scm (guix-cran packages q)
Home page: https://cran.r-project.org/package=qmap
Licenses: GPL 2+
Build system: r
Synopsis: Statistical Transformations for Post-Processing Climate Model Output
Description:

Empirical adjustment of the distribution of variables originating from (regional) climate model simulations using quantile mapping.

r-qfa 4.2
Propagated dependencies: r-rhpcblasctl@0.23-42 r-quantreg@6.1 r-nlme@3.1-168 r-mgcv@1.9-4 r-mass@7.3-65 r-foreach@1.5.2 r-fields@17.1 r-doparallel@1.0.17 r-colorramps@2.3.4
Channel: guix-cran
Location: guix-cran/packages/q.scm (guix-cran packages q)
Home page: https://github.com/IBM/qfa
Licenses: GPL 2+
Build system: r
Synopsis: Quantile-Frequency Analysis (QFA) of Time Series
Description:

Quantile-frequency analysis (QFA) of time series based on trigonometric quantile regression. Spline quantile regression (SQR) for regression coefficient estimation. References: [1] Li, T.-H. (2012) "Quantile periodograms," Journal of the American Statistical Association, 107, 765รข 776, <doi:10.1080/01621459.2012.682815>. [2] Li, T.-H. (2014) Time Series with Mixed Spectra, CRC Press, <doi:10.1201/b15154> [3] Li, T.-H. (2022) "Quantile Fourier transform, quantile series, and nonparametric estimation of quantile spectra," <doi:10.48550/arXiv.2211.05844>. [4] Li, T.-H. (2024) "Quantile crossing spectrum and spline autoregression estimation," <doi:10.48550/arXiv.2412.02513>. [5] Li, T.-H. (2024) "Spline autoregression method for estimation of quantile spectrum," <doi:10.48550/arXiv.2412.17163>. [6] Li, T.-H., and Megiddo, N. (2025) "Spline quantile regression," <doi:10.48550/arXiv.2501.03883>.

r-quantilegrader 0.1.1
Channel: guix-cran
Location: guix-cran/packages/q.scm (guix-cran packages q)
Home page: http://www.kingcounty.gov/depts/health/environmental-health/food-safety/inspection-system/food-safety-rating.aspx
Licenses: GPL 2+
Build system: r
Synopsis: Quantile-Adjusted Restaurant Grading
Description:

Implementation of the food safety restaurant grading system adopted by Public Health - Seattle & King County (see Ashwood, Z.C., Elias, B., and Ho. D.E. "Improving the Reliability of Food Safety Disclosure: A Quantile Adjusted Restaurant Grading System for Seattle-King County" (working paper)). As reported in the accompanying paper, this package allows jurisdictions to easily implement refinements that address common challenges with unadjusted grading systems. First, in contrast to unadjusted grading, where the most recent single routine inspection is the primary determinant of a grade, grading inputs are allowed to be flexible. For instance, it is straightforward to base the grade on average inspection scores across multiple inspection cycles. Second, the package can identify quantile cutoffs by inputting substantively meaningful regulatory thresholds (e.g., the proportion of establishments receiving sufficient violation points to warrant a return visit). Third, the quantile adjustment equalizes the proportion of establishments in a flexible number of grading categories (e.g., A/B/C) across areas (e.g., ZIP codes, inspector areas) to account for inspector differences. Fourth, the package implements a refined quantile adjustment that addresses two limitations with the stats::quantile() function when applied to inspection score datasets with large numbers of score ties. The quantile adjustment algorithm iterates over quantiles until, over all restaurants in all areas, grading proportions are within a tolerance of desired global proportions. In addition the package allows a modified definition of "quantile" from "Nearest Rank". Instead of requiring that at least p[1]% of restaurants receive the top grade and at least (p[1]+p[2])% of restaurants receive the top or second best grade for quantiles p, the algorithm searches for cutoffs so that as close as possible p[1]% of restaurants receive the top grade, and as close as possible to p[2]% of restaurants receive the second top grade.

r-qfrm 1.0.1
Channel: guix-cran
Location: guix-cran/packages/q.scm (guix-cran packages q)
Home page: http://Oleg.Rice.edu
Licenses: GPL 2+
Build system: r
Synopsis: Pricing of Vanilla and Exotic Option Contracts
Description:

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.

r-qpcrtools 1.0.1
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-rstatix@0.7.3 r-multcomp@1.4-29 r-magrittr@2.0.4 r-kableextra@1.4.0 r-ggthemes@5.1.0 r-ggpmisc@0.6.2 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-broom@1.0.10
Channel: guix-cran
Location: guix-cran/packages/q.scm (guix-cran packages q)
Home page: https://github.com/lixiang117423/qPCRtools
Licenses: Expat
Build system: r
Synopsis: Tools for qPCR
Description:

PKG_DESC.

r-qad 1.0.6
Propagated dependencies: r-viridis@0.6.5 r-rcpp@1.1.0 r-ggplot2@4.0.1 r-ggextra@0.11.0 r-dplyr@1.1.4 r-data-table@1.17.8 r-cowplot@1.2.0 r-copula@1.1-6
Channel: guix-cran
Location: guix-cran/packages/q.scm (guix-cran packages q)
Home page: https://github.com/griefl/qad
Licenses: GPL 2
Build system: r
Synopsis: Quantification of Asymmetric Dependence
Description:

This package provides a copula-based measure for quantifying asymmetry in dependence and associations. Documentation and theory about qad is provided by the paper by Junker, Griessenberger & Trutschnig (2021, <doi:10.1016/j.csda.2020.107058>), and the paper by Trutschnig (2011, <doi:10.1016/j.jmaa.2011.06.013>).

r-qurve 1.1.2
Propagated dependencies: r-tidyr@1.3.1 r-stringr@1.6.0 r-shiny@1.11.1 r-scales@1.4.0 r-rmarkdown@2.30 r-readxl@1.4.5 r-rcolorbrewer@1.1-3 r-purrr@1.2.0 r-plyr@1.8.9 r-minpack-lm@1.2-4 r-magrittr@2.0.4 r-labeling@0.4.3 r-knitr@1.50 r-kableextra@1.4.0 r-ggpubr@0.6.2 r-ggplot2@4.0.1 r-ggnewscale@0.5.2 r-ggh4x@0.3.1 r-foreach@1.5.2 r-dt@0.34.0 r-drc@3.0-1 r-dplyr@1.1.4 r-doparallel@1.0.17
Channel: guix-cran
Location: guix-cran/packages/q.scm (guix-cran packages q)
Home page: https://github.com/NicWir/QurvE
Licenses: GPL 3+
Build system: r
Synopsis: Robust and User-Friendly Analysis of Growth and Fluorescence Curves
Description:

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>.

r-qcsimulator 0.0.1
Propagated dependencies: r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/q.scm (guix-cran packages q)
Home page: https://github.com/tvganesh/QCSimulator
Licenses: Expat
Build system: r
Synopsis: 5-Qubit Quantum Computing Simulator
Description:

Simulates a 5 qubit Quantum Computer and evaluates quantum circuits with 1,2 qubit quantum gates.

r-quantumops 3.0.1
Channel: guix-cran
Location: guix-cran/packages/q.scm (guix-cran packages q)
Home page: https://cran.r-project.org/package=QuantumOps
Licenses: GPL 3
Build system: r
Synopsis: Performs Common Linear Algebra Operations Used in Quantum Computing and Implements Quantum Algorithms
Description:

This package contains basic structures and operations used frequently in quantum computing. Intended to be a convenient tool to help learn quantum mechanics and algorithms. Can create arbitrarily sized kets and bras and implements quantum gates, inner products, and tensor products. Creates arbitrarily controlled versions of all gates and can simulate complete or partial measurements of kets. Has functionality to convert functions into equivalent quantum gates and model quantum noise. Includes larger applications, such as Steane error correction <DOI:10.1103/physrevlett.77.793>, Quantum Fourier Transform and Shor's algorithm (Shor 1999), Grover's algorithm (1996), Quantum Approximation Optimization Algorithm (QAOA) (Farhi, Goldstone, and Gutmann 2014) <arXiv:1411.4028>, and a variational quantum classifier (Schuld 2018) <arXiv:1804.00633>. Can be used with the gridsynth algorithm <arXiv:1212.6253> to perform decomposition into the Clifford+T set.

r-qreport 1.1-0
Propagated dependencies: r-viridis@0.6.5 r-rms@8.1-0 r-knitr@1.50 r-htmltools@0.5.8.1 r-hmisc@5.2-4 r-ggplot2@4.0.1 r-formula@1.2-5 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/q.scm (guix-cran packages q)
Home page: https://hbiostat.org/R/qreport/
Licenses: GPL 2+
Build system: r
Synopsis: Statistical Reporting with 'Quarto'
Description:

This package provides statistical components, tables, and graphs that are useful in Quarto and RMarkdown reports and that produce Quarto elements for special formatting such as tabs and marginal notes and graphs. Some of the functions produce entire report sections with tabs, e.g., the missing data report created by missChk(). Functions for inserting variables and tables inside graphviz and mermaid diagrams are included, and so are special clinical trial graphics for adverse event reporting.

r-quartose 0.1.0
Propagated dependencies: r-rlang@1.1.6 r-purrr@1.2.0 r-knitr@1.50 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/q.scm (guix-cran packages q)
Home page: https://github.com/djnavarro/quartose
Licenses: Expat
Build system: r
Synopsis: Dynamically Generate Quarto Syntax
Description:

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.

r-quest 0.2.1
Propagated dependencies: r-str2str@1.0.0 r-psych@2.5.6 r-plyr@1.8.9 r-nlme@3.1-168 r-multilevel@2.7.1 r-mbess@4.9.41 r-lme4@1.1-37 r-lavaan@0.6-20 r-checkmate@2.3.3 r-car@3.1-3 r-boot@1.3-32 r-abind@1.4-8
Channel: guix-cran
Location: guix-cran/packages/q.scm (guix-cran packages q)
Home page: https://cran.r-project.org/package=quest
Licenses: GPL 2+
Build system: r
Synopsis: Prepare Questionnaire Data for Analysis
Description:

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.

r-qrjoint 2.0-11
Propagated dependencies: r-quantreg@6.1 r-matrix@1.7-4 r-kernlab@0.9-33 r-coda@0.19-4.1
Channel: guix-cran
Location: guix-cran/packages/q.scm (guix-cran packages q)
Home page: https://cran.r-project.org/package=qrjoint
Licenses: GPL 2
Build system: r
Synopsis: Joint Estimation in Linear Quantile Regression
Description:

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

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