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      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
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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-sgpr 0.1.2
Propagated dependencies: r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SGPR
Licenses: GPL 3+
Build system: r
Synopsis: Sparse Group Penalized Regression for Bi-Level Variable Selection
Description:

Fits the regularization path of regression models (linear and logistic) with additively combined penalty terms. All possible combinations with Least Absolute Shrinkage and Selection Operator (LASSO), Smoothly Clipped Absolute Deviation (SCAD), Minimax Concave Penalty (MCP) and Exponential Penalty (EP) are supported. This includes Sparse Group LASSO (SGL), Sparse Group SCAD (SGS), Sparse Group MCP (SGM) and Sparse Group EP (SGE). For more information, see Buch, G., Schulz, A., Schmidtmann, I., Strauch, K., & Wild, P. S. (2024) <doi:10.1002/bimj.202200334>.

r-sepals 0.1.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SEPaLS
Licenses: Expat
Build system: r
Synopsis: Shrinkage for Extreme Partial Least-Squares (SEPaLS)
Description:

Regression context for the Partial Least Squares framework for Extreme values. Estimations of the Shrinkage for Extreme Partial Least-Squares (SEPaLS) estimators, an adaptation of the original Partial Least Squares (PLS) method tailored to the extreme-value framework. The SEPaLS project is a joint work by Stephane Girard, Hadrien Lorenzo and Julyan Arbel. R code to replicate the results of the paper is available at <https://github.com/hlorenzo/SEPaLS_simus>. Extremes within PLS was already studied by one of the authors, see M Bousebeta, G Enjolras, S Girard (2023) <doi:10.1016/j.jmva.2022.105101>.

r-tokenizers-bpe 0.1.4
Propagated dependencies: r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://github.com/bnosac/tokenizers.bpe
Licenses: FSDG-compatible
Build system: r
Synopsis: Byte Pair Encoding Text Tokenization
Description:

Unsupervised text tokenizer focused on computational efficiency. Wraps the YouTokenToMe library <https://github.com/VKCOM/YouTokenToMe> which is an implementation of fast Byte Pair Encoding (BPE) <https://aclanthology.org/P16-1162/>.

r-taylor 3.2.0
Propagated dependencies: r-vctrs@0.6.5 r-tibble@3.3.0 r-scales@1.4.0 r-rlang@1.1.6 r-lifecycle@1.0.4 r-glue@1.8.0 r-ggplot2@4.0.1 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://taylor.wjakethompson.com
Licenses: Expat
Build system: r
Synopsis: Lyrics and Song Data for Taylor Swift's Discography
Description:

This package provides a comprehensive resource for data on Taylor Swift songs. Data is included for all officially released studio albums, extended plays (EPs), and individual singles are included. Data comes from Genius (lyrics) and Spotify (song characteristics). Additional functions are included for easily creating data visualizations with color palettes inspired by Taylor Swift's album covers.

r-textdata 0.4.5
Propagated dependencies: r-tibble@3.3.0 r-readr@2.1.6 r-rappdirs@0.3.3 r-fs@1.6.6
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://emilhvitfeldt.github.io/textdata/
Licenses: Expat
Build system: r
Synopsis: Download and Load Various Text Datasets
Description:

This package provides a framework to download, parse, and store text datasets on the disk and load them when needed. Includes various sentiment lexicons and labeled text data sets for classification and analysis.

r-threeway 1.1.3
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://cran.r-project.org/package=ThreeWay
Licenses: GPL 2+
Build system: r
Synopsis: Three-Way Component Analysis
Description:

Component analysis for three-way data arrays by means of Candecomp/Parafac, Tucker3, Tucker2 and Tucker1 models.

r-tsibbletalk 0.1.0
Propagated dependencies: r-vctrs@0.6.5 r-tsibble@1.1.6 r-shiny@1.11.1 r-rlang@1.1.6 r-r6@2.6.1 r-plotly@4.11.0 r-lubridate@1.9.4 r-glue@1.8.0 r-dplyr@1.1.4 r-dendextend@1.19.1 r-crosstalk@1.2.2
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://cran.r-project.org/package=tsibbletalk
Licenses: GPL 3
Build system: r
Synopsis: Interactive Graphics for Tsibble Objects
Description:

This package provides a shared tsibble data easily communicates between htmlwidgets on both client and server sides, powered by crosstalk'. A shiny module is provided to visually explore periodic/aperiodic temporal patterns.

r-textboxplacement 1.0
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://cran.r-project.org/package=textBoxPlacement
Licenses: GPL 2+
Build system: r
Synopsis: Compute a Non-Overlapping Layout of Text Boxes to Label Multiple Overlain Plots
Description:

Compute a non-overlapping layout of text boxes to label multiple overlain curves. For each curve, iteratively search for an adjacent x,y position for the text box that does not overlap with the other curves. If this process fails, then offsets are computed to add to the y values for each curve, that results in sufficient space to add all of the text labels.

r-tbest 5.2
Propagated dependencies: r-signal@1.8-1 r-fdrtool@1.2.18
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://cran.r-project.org/package=TBEST
Licenses: GPL 2
Build system: r
Synopsis: Tree Branches Evaluated Statistically for Tightness
Description:

Our method introduces mathematically well-defined measures for tightness of branches in a hierarchical tree. Statistical significance of the findings is determined, for all branches of the tree, by performing permutation tests, optionally with generalized Pareto p-value estimation.

r-timber 2.0.1
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-shiny@1.11.1 r-miniui@0.1.2 r-magrittr@2.0.4 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-cowplot@1.2.0
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://cran.r-project.org/package=timbeR
Licenses: Expat
Build system: r
Synopsis: Calculate Wood Volumes from Taper Functions
Description:

This package provides functions for estimation of wood volumes, number of logs, diameters along the stem and heights at which certain diameters occur, based on taper functions and other parameters. References: McTague, J. P., & Weiskittel, A. (2021). <doi:10.1139/cjfr-2020-0326>.

r-tvdenoising 1.0.0
Propagated dependencies: r-rlang@1.1.6 r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://github.com/glmgen/tvdenoising
Licenses: Expat
Build system: r
Synopsis: Univariate Total Variation Denoising
Description:

Total variation denoising can be used to approximate a given sequence of noisy observations by a piecewise constant sequence, with adaptively-chosen break points. An efficient linear-time algorithm for total variation denoising is provided here, based on Johnson (2013) <doi:10.1080/10618600.2012.681238>.

r-tsrobprep 0.3.2
Propagated dependencies: r-zoo@1.8-14 r-texttinyr@1.1.8 r-rdpack@2.6.4 r-quantreg@6.1 r-mclust@6.1.2 r-matrix@1.7-4 r-mass@7.3-65 r-glmnet@4.1-10
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://cran.r-project.org/package=tsrobprep
Licenses: Expat
Build system: r
Synopsis: Robust Preprocessing of Time Series Data
Description:

This package provides methods for handling the missing values outliers are introduced in this package. The recognized missing values and outliers are replaced using a model-based approach. The model may consist of both autoregressive components and external regressors. The methods work robust and efficient, and they are fully tunable. The primary motivation for writing the package was preprocessing of the energy systems data, e.g. power plant production time series, but the package could be used with any time series data. For details, see Narajewski et al. (2021) <doi:10.1016/j.softx.2021.100809>.

r-tidywater 0.10.0
Propagated dependencies: r-tidyr@1.3.1 r-rlang@1.1.6 r-magrittr@2.0.4 r-lifecycle@1.0.4 r-knitr@1.50 r-ggrepel@0.9.6 r-ggplot2@4.0.1 r-forcats@1.0.1 r-dplyr@1.1.4 r-desolve@1.40
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://github.com/BrownandCaldwell-Public/tidywater
Licenses: FSDG-compatible Expat
Build system: r
Synopsis: Water Quality Models for Drinking Water Treatment Processes
Description:

This package provides multiple water chemistry-based models and published empirical models in one standard format. As many models have been included as possible, however, users should be aware that models have varying degrees of accuracy and applicability. To learn more, read the references provided below for the models implemented. Functions can be chained together to model a complete treatment process and are designed to work in a tidyverse workflow. Models are primarily based on these sources: Benjamin, M. M. (2002, ISBN:147862308X), Crittenden, J. C., Trussell, R., Hand, D., Howe, J. K., & Tchobanoglous, G., Borchardt, J. H. (2012, ISBN:9781118131473), USEPA. (2001) <https://www.epa.gov/sites/default/files/2017-03/documents/wtp_model_v._2.0_manual_508.pdf>.

r-tbfmultinomial 0.1.3
Propagated dependencies: r-vgam@1.1-13 r-stringr@1.6.0 r-plotrix@3.8-13 r-nnet@7.3-20
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://cran.r-project.org/package=TBFmultinomial
Licenses: GPL 2+
Build system: r
Synopsis: TBF Methodology Extension for Multinomial Outcomes
Description:

Extends the test-based Bayes factor (TBF) methodology to multinomial regression models and discrete time-to-event models with competing risks. The TBF methodology has been well developed and implemented for the generalised linear model [Held et al. (2015) <doi:10.1214/14-STS510>] and for the Cox model [Held et al. (2016) <doi:10.1002/sim.7089>].

r-tosr 0.1.5
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-stringr@1.6.0 r-rlang@1.1.6 r-rebus@0.1-3 r-magrittr@2.0.4 r-igraph@2.2.1 r-dplyr@1.1.4 r-bibliometrix@5.2.1
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://github.com/coreofscience/tosr
Licenses: Expat
Build system: r
Synopsis: Create the Tree of Science from WoS and Scopus
Description:

The goal of tosr is to create the Tree of Science from Web of Science (WoS) and Scopus data. It can read files from both sources at the same time. More information can be found in Valencia-Hernández (2020) <https://revistas.unal.edu.co/index.php/ingeinv/article/view/77718>.

r-tbrf 0.1.7
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-rlang@1.1.6 r-purrr@1.2.0 r-lubridate@1.9.4 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-boot@1.3-32
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://mps9506.github.io/tbrf/
Licenses: GPL 3 FSDG-compatible
Build system: r
Synopsis: Time-Based Rolling Functions
Description:

This package provides rolling statistical functions based on date and time windows instead of n-lagged observations.

r-tfrmtbuilder 0.1.0
Propagated dependencies: r-webshot2@0.1.2 r-tfrmt@0.3.0 r-stringr@1.6.0 r-sortable@0.6.0 r-shinywidgets@0.9.0 r-shinyjs@2.1.0 r-shinyfeedback@0.4.0 r-shinycssloaders@1.1.0 r-shinyace@0.4.4 r-shiny@1.11.1 r-rlang@1.1.6 r-rio@1.2.4 r-purrr@1.2.0 r-gt@1.3.0 r-forcats@1.0.1 r-fontawesome@0.5.3 r-dt@0.34.0 r-dplyr@1.1.4 r-bslib@0.9.0
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://gsk-biostatistics.github.io/tfrmtbuilder/
Licenses: FSDG-compatible
Build system: r
Synopsis: 'shiny' App Companion to the 'tfrmt' Package
Description:

This package provides an interactive interface to the tfrmt package. Users can import, modify, and export tables and templates with little to no code.

r-treefit 1.0.3
Propagated dependencies: r-pracma@2.4.6 r-patchwork@1.3.2 r-igraph@2.2.1 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://hayamizu-lab.github.io/treefit-r/
Licenses: GPL 3+
Build system: r
Synopsis: The First Software for Quantitative Trajectory Inference
Description:

Perform two types of analysis: 1) checking the goodness-of-fit of tree models to your single-cell gene expression data; and 2) deciding which tree best fits your data.

r-testassay 0.1.1
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://cran.r-project.org/package=testassay
Licenses: Expat
Build system: r
Synopsis: Hypothesis Testing Framework for Validating an Assay for Precision
Description:

This package provides a common way of validating a biological assay for is through a procedure, where m levels of an analyte are measured with n replicates at each level, and if all m estimates of the coefficient of variation (CV) are less than some prespecified level, then the assay is declared validated for precision within the range of the m analyte levels. Two limitations of this procedure are: there is no clear statistical statement of precision upon passing, and it is unclear how to modify the procedure for assays with constant standard deviation. We provide tools to convert such a procedure into a set of m hypothesis tests. This reframing motivates the m:n:q procedure, which upon completion delivers a 100q% upper confidence limit on the CV. Additionally, for a post-validation assay output of y, the method gives an ``effective standard deviation interval of log(y) plus or minus r, which is a 68% confidence interval on log(mu), where mu is the expected value of the assay output for that sample. Further, the m:n:q procedure can be straightforwardly applied to constant standard deviation assays. We illustrate these tools by applying them to a growth inhibition assay. This is an implementation of the methods described in Fay, Sachs, and Miura (2018) <doi:10.1002/sim.7528>.

r-tsdecomp 0.2
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://jalobe.com
Licenses: GPL 2
Build system: r
Synopsis: Decomposition of Time Series Data
Description:

ARIMA-model-based decomposition of quarterly and monthly time series data. The methodology is developed and described, among others, in Burman (1980) <DOI:10.2307/2982132> and Hillmer and Tiao (1982) <DOI:10.2307/2287770>.

r-trafficcar 0.1.0
Propagated dependencies: r-units@1.0-0 r-sf@1.0-23 r-rlang@1.1.6 r-posterior@1.6.1 r-matrix@1.7-4 r-igraph@2.2.1 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://cran.r-project.org/package=trafficCAR
Licenses: Expat
Build system: r
Synopsis: Bayesian CAR Models for Road-Segment Traffic
Description:

This package provides tools for simulating and modeling traffic flow on road networks using spatial conditional autoregressive (CAR) models. The package represents road systems as graphs derived from OpenStreetMap data <https://www.openstreetmap.org/> and supports network-based spatial dependence, basic preprocessing, and visualization for spatial traffic analysis.

r-tcomp 1.0.1
Propagated dependencies: r-mcomp@2.8 r-forecast@8.24.0
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://cran.r-project.org/package=Tcomp
Licenses: GPL 3
Build system: r
Synopsis: Data from the 2010 Tourism Forecasting Competition
Description:

The 1311 time series from the tourism forecasting competition conducted in 2010 and described in Athanasopoulos et al. (2011) <DOI:10.1016/j.ijforecast.2010.04.009>.

r-tvreg 0.5.9
Propagated dependencies: r-vars@1.6-1 r-systemfit@1.1-30 r-plm@2.6-7 r-matrix@1.7-4 r-mass@7.3-65 r-bvarsv@1.1
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://github.com/icasas/tvReg
Licenses: GPL 3+
Build system: r
Synopsis: Time-Varying Coefficient for Single and Multi-Equation Regressions
Description:

Fitting time-varying coefficient models for single and multi-equation regressions, using kernel smoothing techniques.

r-tfre 0.1.0
Propagated dependencies: r-rcppparallel@5.1.11-1 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://cran.r-project.org/package=TFRE
Licenses: GPL 2+
Build system: r
Synopsis: Tuning-Free Robust and Efficient Approach to High-Dimensional Regression
Description:

Provide functions to estimate the coefficients in high-dimensional linear regressions via a tuning-free and robust approach. The method was published in Wang, L., Peng, B., Bradic, J., Li, R. and Wu, Y. (2020), "A Tuning-free Robust and Efficient Approach to High-dimensional Regression", Journal of the American Statistical Association, 115:532, 1700-1714(JASAâ s discussion paper), <doi:10.1080/01621459.2020.1840989>. See also Wang, L., Peng, B., Bradic, J., Li, R. and Wu, Y. (2020), "Rejoinder to â A tuning-free robust and efficient approach to high-dimensional regression". Journal of the American Statistical Association, 115, 1726-1729, <doi:10.1080/01621459.2020.1843865>; Peng, B. and Wang, L. (2015), "An Iterative Coordinate Descent Algorithm for High-Dimensional Nonconvex Penalized Quantile Regression", Journal of Computational and Graphical Statistics, 24:3, 676-694, <doi:10.1080/10618600.2014.913516>; Clémençon, S., Colin, I., and Bellet, A. (2016), "Scaling-up empirical risk minimization: optimization of incomplete u-statistics", The Journal of Machine Learning Research, 17(1):2682â 2717; Fan, J. and Li, R. (2001), "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties", Journal of the American Statistical Association, 96:456, 1348-1360, <doi:10.1198/016214501753382273>.

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