_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
      \_\ \       /  \ \\ \ \   / / /     / /  \
      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
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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-s3 1.1.0
Propagated dependencies: r-rlang@1.1.6 r-purrr@1.2.0 r-prettyunits@1.2.0 r-httr@1.4.7 r-glue@1.8.0 r-fs@1.6.6 r-dplyr@1.1.4 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/brokamp-group/s3
Licenses: Expat
Build system: r
Synopsis: Download Files from 'AWS S3'
Description:

Download files hosted on AWS S3 (Amazon Web Services Simple Storage Service; <https://aws.amazon.com/s3/>) to a local directory based on their URI. Avoid downloading files that are already present locally. Allow for customization of where to store downloaded files.

r-standardize 0.2.2
Propagated dependencies: r-stringr@1.6.0 r-mass@7.3-65 r-lme4@1.1-37
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/CDEager/standardize
Licenses: GPL 3+
Build system: r
Synopsis: Tools for Standardizing Variables for Regression in R
Description:

This package provides tools which allow regression variables to be placed on similar scales, offering computational benefits as well as easing interpretation of regression output.

r-sylly-en 0.1-3
Propagated dependencies: r-sylly@0.1-7
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: http://reaktanz.de/?c=hacking&s=koRpus
Licenses: GPL 3+
Build system: r
Synopsis: Language Support for 'sylly' Package: English
Description:

Adds support for the English language to the sylly package. Due to some restrictions on CRAN, the full package sources are only available from the project homepage. To ask for help, report bugs, suggest feature improvements, or discuss the global development of the package, please consider subscribing to the koRpus-dev mailing list (<http://korpusml.reaktanz.de>).

r-skedastic 2.0.3
Propagated dependencies: r-slam@0.1-55 r-roi-plugin-qpoases@1.0-3 r-roi@1.0-1 r-rfast@2.1.5.2 r-rdpack@2.6.4 r-quadprogxt@0.0.6 r-quadprog@1.5-8 r-pracma@2.4.6 r-osqp@0.6.3.3 r-mgcv@1.9-4 r-matrix@1.7-4 r-mass@7.3-65 r-inflection@1.3.7 r-compquadform@1.4.4 r-caret@7.0-1 r-broom@1.0.10
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/tjfarrar/skedastic
Licenses: Expat
Build system: r
Synopsis: Handling Heteroskedasticity in the Linear Regression Model
Description:

This package implements numerous methods for testing for, modelling, and correcting for heteroskedasticity in the classical linear regression model. The most novel contribution of the package is found in the functions that implement the as-yet-unpublished auxiliary linear variance models and auxiliary nonlinear variance models that are designed to estimate error variances in a heteroskedastic linear regression model. These models follow principles of statistical learning described in Hastie (2009) <doi:10.1007/978-0-387-21606-5>. The nonlinear version of the model is estimated using quasi-likelihood methods as described in Seber and Wild (2003, ISBN: 0-471-47135-6). Bootstrap methods for approximate confidence intervals for error variances are implemented as described in Efron and Tibshirani (1993, ISBN: 978-1-4899-4541-9), including also the expansion technique described in Hesterberg (2014) <doi:10.1080/00031305.2015.1089789>. The wild bootstrap employed here follows the description in Davidson and Flachaire (2008) <doi:10.1016/j.jeconom.2008.08.003>. Tuning of hyper-parameters makes use of a golden section search function that is modelled after the MATLAB function of Zarnowiec (2022) <https://www.mathworks.com/matlabcentral/fileexchange/25919-golden-section-method-algorithm>. A methodological description of the algorithm can be found in Fox (2021, ISBN: 978-1-003-00957-3). There are 25 different functions that implement hypothesis tests for heteroskedasticity. These include a test based on Anscombe (1961) <https://projecteuclid.org/euclid.bsmsp/1200512155>, Ramsey's (1969) BAMSET Test <doi:10.1111/j.2517-6161.1969.tb00796.x>, the tests of Bickel (1978) <doi:10.1214/aos/1176344124>, Breusch and Pagan (1979) <doi:10.2307/1911963> with and without the modification proposed by Koenker (1981) <doi:10.1016/0304-4076(81)90062-2>, Carapeto and Holt (2003) <doi:10.1080/0266476022000018475>, Cook and Weisberg (1983) <doi:10.1093/biomet/70.1.1> (including their graphical methods), Diblasi and Bowman (1997) <doi:10.1016/S0167-7152(96)00115-0>, Dufour, Khalaf, Bernard, and Genest (2004) <doi:10.1016/j.jeconom.2003.10.024>, Evans and King (1985) <doi:10.1016/0304-4076(85)90085-5> and Evans and King (1988) <doi:10.1016/0304-4076(88)90006-1>, Glejser (1969) <doi:10.1080/01621459.1969.10500976> as formulated by Mittelhammer, Judge and Miller (2000, ISBN: 0-521-62394-4), Godfrey and Orme (1999) <doi:10.1080/07474939908800438>, Goldfeld and Quandt (1965) <doi:10.1080/01621459.1965.10480811>, Harrison and McCabe (1979) <doi:10.1080/01621459.1979.10482544>, Harvey (1976) <doi:10.2307/1913974>, Honda (1989) <doi:10.1111/j.2517-6161.1989.tb01749.x>, Horn (1981) <doi:10.1080/03610928108828074>, Li and Yao (2019) <doi:10.1016/j.ecosta.2018.01.001> with and without the modification of Bai, Pan, and Yin (2016) <doi:10.1007/s11749-017-0575-x>, Rackauskas and Zuokas (2007) <doi:10.1007/s10986-007-0018-6>, Simonoff and Tsai (1994) <doi:10.2307/2986026> with and without the modification of Ferrari, Cysneiros, and Cribari-Neto (2004) <doi:10.1016/S0378-3758(03)00210-6>, Szroeter (1978) <doi:10.2307/1913831>, Verbyla (1993) <doi:10.1111/j.2517-6161.1993.tb01918.x>, White (1980) <doi:10.2307/1912934>, Wilcox and Keselman (2006) <doi:10.1080/10629360500107923>, Yuce (2008) <https://dergipark.org.tr/en/pub/iuekois/issue/8989/112070>, and Zhou, Song, and Thompson (2015) <doi:10.1002/cjs.11252>. Besides these heteroskedasticity tests, there are supporting functions that compute the BLUS residuals of Theil (1965) <doi:10.1080/01621459.1965.10480851>, the conditional two-sided p-values of Kulinskaya (2008) <doi:10.48550/arXiv.0810.2124>, and probabilities for the nonparametric trend statistic of Lehmann (1975, ISBN: 0-816-24996-1). For handling heteroskedasticity, in addition to the new auxiliary variance model methods, there is a function to implement various existing Heteroskedasticity-Consistent Covariance Matrix Estimators from the literature, such as those of White (1980) <doi:10.2307/1912934>, MacKinnon and White (1985) <doi:10.1016/0304-4076(85)90158-7>, Cribari-Neto (2004) <doi:10.1016/S0167-9473(02)00366-3>, Cribari-Neto et al. (2007) <doi:10.1080/03610920601126589>, Cribari-Neto and da Silva (2011) <doi:10.1007/s10182-010-0141-2>, Aftab and Chang (2016) <doi:10.18187/pjsor.v12i2.983>, and Li et al. (2017) <doi:10.1080/00949655.2016.1198906>.

r-sensitivityixj 0.1.5
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=sensitivityIxJ
Licenses: GPL 3
Build system: r
Synopsis: Exact Nonparametric Sensitivity Analysis for I by J Contingency Tables
Description:

This package implements exact, normally approximated, and sampling-based sensitivity analysis for observational studies with contingency tables. Includes exact (kernel-based), normal approximation, and sequential importance sampling (SIS) methods using Rcpp for computational efficiency. The methods build upon the framework introduced in Rosenbaum (2002) <doi:10.1007/978-1-4757-3692-2> and the generalized design sensitivity framework developed by Chiu (2025) <doi:10.48550/arXiv.2507.17207>.

r-scepter 0.2-4
Propagated dependencies: r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SCEPtER
Licenses: GPL 2+
Build system: r
Synopsis: Stellar CharactEristics Pisa Estimation gRid
Description:

This package provides a pipeline for estimating the stellar age, mass, and radius given observational effective temperature, [Fe/H], and astroseismic parameters. The results are obtained adopting a maximum likelihood technique over a grid of pre-computed stellar models, as described in Valle et al. (2014) <doi:10.1051/0004-6361/201322210>.

r-shrinkagetrees 1.0.2
Propagated dependencies: r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/tijn-jacobs/ShrinkageTrees
Licenses: Expat
Build system: r
Synopsis: Regression Trees with Shrinkage Priors
Description:

Bayesian regression tree models with shrinkage priors on step heights. Supports continuous, binary, and right-censored (survival) outcomes. Used for high-dimensional prediction and causal inference.

r-sensobol 1.1.6
Propagated dependencies: r-stringr@1.6.0 r-scales@1.4.0 r-rlang@1.1.6 r-rfast@2.1.5.2 r-rdpack@2.6.4 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-randtoolbox@2.0.5 r-matrixstats@1.5.0 r-magrittr@2.0.4 r-lhs@1.2.0 r-ggplot2@4.0.1 r-desolve@1.40 r-data-table@1.17.8 r-boot@1.3-32
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/arnaldpuy/sensobol
Licenses: GPL 3
Build system: r
Synopsis: Computation of Variance-Based Sensitivity Indices
Description:

It allows to rapidly compute, bootstrap and plot up to fourth-order Sobol'-based sensitivity indices using several state-of-the-art first and total-order estimators. Sobol indices can be computed either for models that yield a scalar as a model output or for systems of differential equations. The package also provides a suit of benchmark tests functions and several options to obtain publication-ready figures of the model output uncertainty and sensitivity-related analysis. An overview of the package can be found in Puy et al. (2022) <doi:10.18637/jss.v102.i05>.

r-stuart 0.10.2
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=stuart
Licenses: GPL 3
Build system: r
Synopsis: Subtests Using Algorithmic Rummaging Techniques
Description:

Construct subtests from a pool of items by using ant-colony-optimization, genetic algorithms, brute force, or random sampling. Schultze (2017) <doi:10.17169/refubium-622>.

r-shapleyoutlier 0.1.2
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-robustbase@0.99-6 r-rdpack@2.6.4 r-rcolorbrewer@1.1-3 r-magrittr@2.0.4 r-gridextra@2.3 r-ggplot2@4.0.1 r-forcats@1.0.1 r-egg@0.4.5 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=ShapleyOutlier
Licenses: GPL 3
Build system: r
Synopsis: Multivariate Outlier Explanations using Shapley Values and Mahalanobis Distances
Description:

Based on Shapley values to explain multivariate outlyingness and to detect and impute cellwise outliers. Includes implementations of methods described in Mayrhofer and Filzmoser (2023) <doi:10.1016/j.ecosta.2023.04.003>.

r-spuriouscorrelations 0.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=spuriouscorrelations
Licenses: CC0
Build system: r
Synopsis: Datasets with Strong and Spurious Correlations
Description:

This package provides datasets from Vigen (2015) <https://web.archive.org/web/20230607181247/https%3A/tylervigen.com/spurious-correlations> rescued from the Internet Wayback Machine. These should be preserved for statistics introductory courses as these make it very clear that correlation is not causation.

r-saebnocov 0.1.0
Propagated dependencies: r-rlang@1.1.6 r-dplyr@1.1.4 r-descr@1.1.8
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=saebnocov
Licenses: GPL 3+
Build system: r
Synopsis: Small Area Estimation using Empirical Bayes without Auxiliary Variable
Description:

Estimates the parameter of small area in binary data without auxiliary variable using Empirical Bayes technique, mainly from Rao and Molina (2015,ISBN:9781118735787) with book entitled "Small Area Estimation Second Edition". This package provides another option of direct estimation using weight. This package also features alpha and beta parameter estimation on calculating process of small area. Those methods are Newton-Raphson and Moment which based on Wilcox (1979) <doi:10.1177/001316447903900302> and Kleinman (1973) <doi:10.1080/01621459.1973.10481332>.

r-see 0.13.0
Propagated dependencies: r-performance@0.15.2 r-patchwork@1.3.2 r-parameters@0.28.3 r-modelbased@0.13.1 r-insight@1.4.3 r-ggplot2@4.0.1 r-effectsize@1.0.1 r-datawizard@1.3.0 r-correlation@0.8.8 r-bayestestr@0.17.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://easystats.github.io/see/
Licenses: Expat
Build system: r
Synopsis: Model Visualisation Toolbox for 'easystats' and 'ggplot2'
Description:

This package provides plotting utilities supporting packages in the easystats ecosystem (<https://github.com/easystats/easystats>) and some extra themes, geoms, and scales for ggplot2'. Color scales are based on <https://materialui.co/>. References: Lüdecke et al. (2021) <doi:10.21105/joss.03393>.

r-sleev 1.1.6
Propagated dependencies: r-rcppeigen@0.3.4.0.2 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/dragontaoran/sleev
Licenses: GPL 2+
Build system: r
Synopsis: Semiparametric Likelihood Estimation with Errors in Variables
Description:

Efficient regression analysis under general two-phase sampling, where Phase I includes error-prone data and Phase II contains validated data on a subset.

r-supercells 1.0.0
Propagated dependencies: r-terra@1.8-86 r-sf@1.0-23 r-philentropy@0.10.0 r-future-apply@1.20.0 r-cpp11@0.5.2
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://jakubnowosad.com/supercells/
Licenses: GPL 3+
Build system: r
Synopsis: Superpixels of Spatial Data
Description:

This package creates superpixels based on input spatial data. This package works on spatial data with one variable (e.g., continuous raster), many variables (e.g., RGB rasters), and spatial patterns (e.g., areas in categorical rasters). It is based on the SLIC algorithm (Achanta et al. (2012) <doi:10.1109/TPAMI.2012.120>), and readapts it to work with arbitrary dissimilarity measures.

r-sunsvoc 0.1.2
Propagated dependencies: r-stringr@1.6.0 r-rlang@1.1.6 r-purrr@1.2.0 r-magrittr@2.0.4 r-dplyr@1.1.4 r-ddiv@0.1.1 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=SunsVoc
Licenses: Modified BSD
Build system: r
Synopsis: Constructing Suns-Voc from Outdoor Time-Series I-V Curves
Description:

Suns-Voc (or Isc-Voc) curves can provide the current-voltage (I-V) characteristics of the diode of photovoltaic cells without the effect of series resistance. Here, Suns-Voc curves can be constructed with outdoor time-series I-V curves [1,2,3] of full-size photovoltaic (PV) modules instead of having to be measured in the lab. Time series of four different power loss modes can be calculated based on obtained Isc-Voc curves. This material is based upon work supported by the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE) under Solar Energy Technologies Office (SETO) Agreement Number DE-EE0008172. Jennifer L. Braid is supported by the U.S. Department of Energy (DOE) Office of Energy Efficiency and Renewable Energy administered by the Oak Ridge Institute for Science and Education (ORISE) for the DOE. ORISE is managed by Oak Ridge Associated Universities (ORAU) under DOE contract number DE-SC0014664. [1] Wang, M. et al, 2018. <doi:10.1109/PVSC.2018.8547772>. [2] Walters et al, 2018 <doi:10.1109/PVSC.2018.8548187>. [3] Guo, S. et al, 2016. <doi:10.1117/12.2236939>.

r-sisvive 1.4
Propagated dependencies: r-lars@1.3
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=sisVIVE
Licenses: GPL 2
Build system: r
Synopsis: Some Invalid Some Valid Instrumental Variables Estimator
Description:

Selects invalid instruments amongst a candidate of potentially bad instruments. The algorithm selects potentially invalid instruments and provides an estimate of the causal effect between exposure and outcome.

r-singcar 0.1.5
Propagated dependencies: r-withr@3.0.2 r-mass@7.3-65 r-cholwishart@1.1.4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/jorittmo/singcar
Licenses: Expat
Build system: r
Synopsis: Comparing Single Cases to Small Samples
Description:

When comparing single cases to control populations and no parameters are known researchers and clinicians must estimate these with a control sample. This is often done when testing a case's abnormality on some variable or testing abnormality of the discrepancy between two variables. Appropriate frequentist and Bayesian methods for doing this are here implemented, including tests allowing for the inclusion of covariates. These have been developed first and foremost by John Crawford and Paul Garthwaite, e.g. in Crawford and Howell (1998) <doi:10.1076/clin.12.4.482.7241>, Crawford and Garthwaite (2005) <doi:10.1037/0894-4105.19.3.318>, Crawford and Garthwaite (2007) <doi:10.1080/02643290701290146> and Crawford, Garthwaite and Ryan (2011) <doi:10.1016/j.cortex.2011.02.017>. The package is also equipped with power calculators for each method.

r-sjdbc 1.6.1
Propagated dependencies: r-rjava@1.0-11
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=sjdbc
Licenses: Modified BSD
Build system: r
Synopsis: JDBC Driver Interface
Description:

This package provides a database-independent JDBC interface.

r-starm 0.1.0
Propagated dependencies: r-matrix@1.7-4
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://cran.r-project.org/package=starm
Licenses: GPL 3
Build system: r
Synopsis: Spatio-Temporal Autologistic Regression Model
Description:

Estimates the coefficients of the two-time centered autologistic regression model based on Gegout-Petit A., Guerin-Dubrana L., Li S. "A new centered spatio-temporal autologistic regression model. Application to local spread of plant diseases." 2019. <arXiv:1811.06782>, using a grid of binary variables to estimate the spread of a disease on the grid over the years.

r-sdcspatial 0.6.1
Propagated dependencies: r-raster@3.6-32
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/edwindj/sdcSpatial
Licenses: GPL 2
Build system: r
Synopsis: Statistical Disclosure Control for Spatial Data
Description:

Privacy protected raster maps can be created from spatial point data. Protection methods include smoothing of dichotomous variables by de Jonge and de Wolf (2016) <doi:10.1007/978-3-319-45381-1_9>, continuous variables by de Wolf and de Jonge (2018) <doi:10.1007/978-3-319-99771-1_23>, suppressing revealing values and a generalization of the quad tree method by Suñé, Rovira, Ibáñez and Farré (2017) <doi:10.2901/EUROSTAT.C2017.001>.

r-sampbias 2.0.0
Propagated dependencies: r-viridis@0.6.5 r-tidyr@1.3.1 r-terra@1.8-86 r-sf@1.0-23 r-rnaturalearth@1.1.0 r-rlang@1.1.6 r-magrittr@2.0.4 r-ggplot2@4.0.1 r-forcats@1.0.1 r-dplyr@1.1.4 r-cowplot@1.2.0
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/azizka/sampbias
Licenses: GPL 3
Build system: r
Synopsis: Evaluating Geographic Sampling Bias in Biological Collections
Description:

Evaluating the biasing impact of geographic features such as airports, cities, roads, rivers in datasets of coordinates based biological collection datasets, by Bayesian estimation of the parameters of a Poisson process. Enables also spatial visualization of sampling bias and includes a set of convenience functions for publication level plotting. Also available as shiny app. The reference for the methodology is: Zizka et al. (2020) <doi:10.1111/ecog.05102>.

r-secret 1.1.0
Propagated dependencies: r-rprojroot@2.1.1 r-openssl@2.3.4 r-jsonlite@2.0.0 r-curl@7.0.0 r-assertthat@0.2.1
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://github.com/gaborcsardi/secret#readme
Licenses: Expat
Build system: r
Synopsis: Share Sensitive Information in R Packages
Description:

Allow sharing sensitive information, for example passwords, API keys, etc., in R packages, using public key cryptography.

r-spades-core 3.0.4
Propagated dependencies: r-whisker@0.4.1 r-terra@1.8-86 r-require@1.0.1 r-reproducible@3.0.0 r-quickplot@1.0.4 r-qs2@0.1.6 r-lobstr@1.1.3 r-igraph@2.2.1 r-fs@1.6.6 r-data-table@1.17.8 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/s.scm (guix-cran packages s)
Home page: https://spades-core.predictiveecology.org/
Licenses: GPL 3
Build system: r
Synopsis: Core Utilities for Developing and Running Spatially Explicit Discrete Event Models
Description:

This package provides the core framework for a discrete event system to implement a complete data-to-decisions, reproducible workflow. The core components facilitate the development of modular pieces, and enable the user to include additional functionality by running user-built modules. Includes conditional scheduling, restart after interruption, packaging of reusable modules, tools for developing arbitrary automated workflows, automated interweaving of modules of different temporal resolution, and tools for visualizing and understanding the within-project dependencies. The suggested package NLMR can be installed from the repository (<https://PredictiveEcology.r-universe.dev>).

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