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

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-cubar 1.2.0
Propagated dependencies: r-rlang@1.1.6 r-iranges@2.44.0 r-ggplot2@4.0.1 r-data-table@1.17.8 r-biostrings@2.78.0
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/mt1022/cubar
Licenses: Expat
Build system: r
Synopsis: Codon Usage Bias Analysis
Description:

This package provides a suite of functions for rapid and flexible analysis of codon usage bias. It provides in-depth analysis at the codon level, including relative synonymous codon usage (RSCU), tRNA weight calculations, machine learning predictions for optimal or preferred codons, and visualization of codon-anticodon pairing. Additionally, it can calculate various gene- specific codon indices such as codon adaptation index (CAI), effective number of codons (ENC), fraction of optimal codons (Fop), tRNA adaptation index (tAI), mean codon stabilization coefficients (CSCg), and GC contents (GC/GC3s/GC4d). It also supports both standard and non-standard genetic code tables found in NCBI, as well as custom genetic code tables.

r-card 0.1.1
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-rlang@1.1.6 r-purrr@1.2.0 r-parsnip@1.3.3 r-hardhat@1.4.2 r-ggplot2@4.0.1 r-generics@0.1.4 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=card
Licenses: Expat
Build system: r
Synopsis: Cardiovascular Applications in Research Data
Description:

This package provides a collection of cardiovascular research datasets and analytical tools, including methods for cardiovascular procedural data, such as electrocardiography, echocardiography, and catheterization data. Additional methods exist for analysis of procedural billing codes.

r-congress 0.1.0
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-stringr@1.6.0 r-rlang@1.1.6 r-purrr@1.2.0 r-httr2@1.2.1 r-glue@1.8.0 r-dplyr@1.1.4 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/christopherkenny/congress
Licenses: Expat
Build system: r
Synopsis: Access the Congress.gov API
Description:

Download and read data on United States congressional proceedings. Data is read from the Library of Congress's Congress.gov Application Programming Interface (<https://github.com/LibraryOfCongress/api.congress.gov/>). Functions exist for all version 3 endpoints, including for bills, amendments, congresses, summaries, members, reports, communications, nominations, and treaties.

r-configparser 1.0.0
Propagated dependencies: r-r6@2.6.1 r-ini@0.3.1
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/hhoeflin/ConfigParser
Licenses: GPL 3
Build system: r
Synopsis: Package to Parse an INI File, Including Variable Interpolation
Description:

Enhances the ini package by adding the ability to interpolate variables. The INI configuration file is read into an R6 ConfigParser object (loosely inspired by Pythons ConfigParser module) and the keys can be read, where %(....)s instances are interpolated by other included options or outside variables.

r-certara-r 1.1.0
Propagated dependencies: r-xml2@1.5.0 r-ssh@0.9.4 r-sortable@0.6.0 r-shinywidgets@0.9.0 r-shinymaterial@1.2.0 r-shinyjs@2.1.0 r-shinyjqui@0.4.1 r-shiny@1.11.1 r-rlang@1.1.6 r-reshape@0.8.10 r-remotes@2.5.0 r-plotly@4.11.0 r-magrittr@2.0.4 r-jsonlite@2.0.0 r-htmltools@0.5.8.1 r-ggplot2@4.0.1 r-dt@0.34.0 r-dplyr@1.1.4 r-data-table@1.17.8 r-batchtools@0.9.18
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/certara/R-Certara
Licenses: LGPL 3
Build system: r
Synopsis: Easily Install Pharmacometric Packages and Shiny Applications Developed by Certara
Description:

This package provides a convenient set of wrapper functions to install pharmacometric packages and Shiny applications developed by Certara PMX and Integrated Drug Development (iDD). The functions ensure the successful installation of packages from non-standard repositories.

r-clustermole 1.1.1
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-singscore@1.30.0 r-rlang@1.1.6 r-magrittr@2.0.4 r-gsva@2.4.1 r-gseabase@1.72.0 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://igordot.github.io/clustermole/
Licenses: Expat
Build system: r
Synopsis: Unbiased Single-Cell Transcriptomic Data Cell Type Identification
Description:

Assignment of cell type labels to single-cell RNA sequencing (scRNA-seq) clusters is often a time-consuming process that involves manual inspection of the cluster marker genes complemented with a detailed literature search. This is especially challenging when unexpected or poorly described populations are present. The clustermole R package provides methods to query thousands of human and mouse cell identity markers sourced from a variety of databases.

r-coda-plot 0.2.1
Propagated dependencies: r-ggrepel@0.9.6 r-ggplot2@4.0.1 r-coda-base@1.0.5
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=coda.plot
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Plots for Compositional Data
Description:

This package provides a collection of easy-to-use functions for creating visualizations of compositional data using ggplot2'. Includes support for common plotting techniques in compositional data analysis.

r-copulareg 0.1.0
Propagated dependencies: r-rvinecopulib@0.7.3.1.0
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=copulareg
Licenses: FSDG-compatible
Build system: r
Synopsis: Copula Regression
Description:

Fits multivariate models in an R-vine pair copula construction framework, in such a way that the conditional copula can be easily evaluated. In addition, the package implements functionality to compute or approximate the conditional expectation via the conditional copula.

r-ctmva 1.6.0
Propagated dependencies: r-viridislite@0.4.2 r-vegan@2.7-2 r-rlang@1.1.6 r-polynom@1.4-1 r-mgcv@1.9-4 r-matrix@1.7-4 r-mass@7.3-65 r-ggplot2@4.0.1 r-fda@6.3.0
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=ctmva
Licenses: GPL 2+
Build system: r
Synopsis: Continuous-Time Multivariate Analysis
Description:

This package implements a basis function or functional data analysis framework for several techniques of multivariate analysis in continuous-time setting. Specifically, we introduced continuous-time analogues of several classical techniques of multivariate analysis, such as principal component analysis, canonical correlation analysis, Fisher linear discriminant analysis, K-means clustering, and so on. Details are in Biplab Paul, Philip T. Reiss, Erjia Cui and Noemi Foa (2025) "Continuous-time multivariate analysis" <doi: 10.1080/10618600.2024.2374570>.

r-contresppp 0.4.2
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/jcliff89/ContRespPP
Licenses: CC0
Build system: r
Synopsis: Predictive Probability for a Continuous Response with an ANOVA Structure
Description:

This package provides a Bayesian approach to using predictive probability in an ANOVA construct with a continuous normal response, when threshold values must be obtained for the question of interest to be evaluated as successful (Sieck and Christensen (2021) <doi:10.1002/qre.2802>). The Bayesian Mission Mean (BMM) is used to evaluate a question of interest (that is, a mean that randomly selects combination of factor levels based on their probability of occurring instead of averaging over the factor levels, as in the grand mean). Under this construct, in contrast to a Gibbs sampler (or Metropolis-within-Gibbs sampler), a two-stage sampling method is required. The nested sampler determines the conditional posterior distribution of the model parameters, given Y, and the outside sampler determines the marginal posterior distribution of Y (also commonly called the predictive distribution for Y). This approach provides a sample from the joint posterior distribution of Y and the model parameters, while also accounting for the threshold value that must be obtained in order for the question of interest to be evaluated as successful.

r-cavariants 6.0
Propagated dependencies: r-plotly@4.11.0 r-gridextra@2.3 r-ggrepel@0.9.6 r-ggplot2@4.0.1 r-ggforce@0.5.0
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://www.R-project.org
Licenses: GPL 3+
Build system: r
Synopsis: Correspondence Analysis Variants
Description:

This package provides six variants of two-way correspondence analysis (ca): simple ca, singly ordered ca, doubly ordered ca, non symmetrical ca, singly ordered non symmetrical ca, and doubly ordered non symmetrical ca.

r-clevr 0.1.2
Propagated dependencies: r-rcpp@1.1.0 r-matrix@1.7-4 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/cleanzr/clevr
Licenses: GPL 2
Build system: r
Synopsis: Clustering and Link Prediction Evaluation in R
Description:

This package provides tools for evaluating link prediction and clustering algorithms with respect to ground truth. Includes efficient implementations of common performance measures such as pairwise precision/recall, cluster homogeneity/completeness, variation of information, Rand index etc.

r-cgmquantify 0.1.0
Propagated dependencies: r-tidyverse@2.0.0 r-magrittr@2.0.4 r-hms@1.1.4 r-ggplot2@4.0.1 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=cgmquantify
Licenses: FSDG-compatible
Build system: r
Synopsis: Analyzing Glucose and Glucose Variability
Description:

Continuous glucose monitoring (CGM) systems provide real-time, dynamic glucose information by tracking interstitial glucose values throughout the day. Glycemic variability, also known as glucose variability, is an established risk factor for hypoglycemia (Kovatchev) and has been shown to be a risk factor in diabetes complications. Over 20 metrics of glycemic variability have been identified. Here, we provide functions to calculate glucose summary metrics, glucose variability metrics (as defined in clinical publications), and visualizations to visualize trends in CGM data. Cho P, Bent B, Wittmann A, et al. (2020) <https://diabetes.diabetesjournals.org/content/69/Supplement_1/73-LB.abstract> American Diabetes Association (2020) <https://professional.diabetes.org/diapro/glucose_calc> Kovatchev B (2019) <doi:10.1177/1932296819826111> Kovdeatchev BP (2017) <doi:10.1038/nrendo.2017.3> Tamborlane W V., Beck RW, Bode BW, et al. (2008) <doi:10.1056/NEJMoa0805017> Umpierrez GE, P. Kovatchev B (2018) <doi:10.1016/j.amjms.2018.09.010>.

r-codalomic 0.1.1
Propagated dependencies: r-zcompositions@1.5.0-5 r-xtable@1.8-4 r-reshape2@1.4.5 r-r2jags@0.8-9 r-mass@7.3-65 r-ggplot2@4.0.1 r-ggbiplot@0.6.2 r-compositions@2.0-9 r-broom@1.0.10
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=CoDaLoMic
Licenses: GPL 3
Build system: r
Synopsis: Compositional Models to Longitudinal Microbiome Data
Description:

Implementation of models to analyse compositional microbiome time series taking into account the interaction between groups of bacteria. The models implemented are described in Creus-Martà et al (2018, ISBN:978-84-09-07541-6), Creus-Martà et al (2021) <doi:10.1155/2021/9951817> and Creus-Martà et al (2022) <doi:10.1155/2022/4907527>.

r-confintvariance 1.0.2
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=ConfIntVariance
Licenses: GPL 3
Build system: r
Synopsis: Confidence Interval for the Univariate Population Variance without Normality Assumption
Description:

Surrounds the usual sample variance of a univariate numeric sample with a confidence interval for the population variance. This has been done so far only under the assumption that the underlying distribution is normal. Under the hood, this package implements the unique least-variance unbiased estimator of the variance of the sample variance, in a formula that is equivalent to estimating kurtosis and square of the population variance in an unbiased way and combining them according to the classical formula into an estimator of the variance of the sample variance. Both the sample variance and the estimator of its variance are U-statistics. By the theory of U-statistic, the resulting estimator is unique. See Fuchs, Krautenbacher (2016) <doi:10.1080/15598608.2016.1158675> and the references therein for an overview of unbiased estimation of variances of U-statistics.

r-cdft 1.2
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=CDFt
Licenses: GPL 2+
Build system: r
Synopsis: Downscaling and Bias Correction via Non-Parametric CDF-Transform
Description:

Statistical downscaling and bias correction (model output statistics) method based on cumulative distribution functions (CDF) transformation. See Michelangeli, Vrac, Loukos (2009) Probabilistic downscaling approaches: Application to wind cumulative distribution functions. Geophysical Research Letters, 36, L11708, <doi:10.1029/2009GL038401>. ; and Vrac, Drobinski, Merlo, Herrmann, Lavaysse, Li, Somot (2012) Dynamical and statistical downscaling of the French Mediterranean climate: uncertainty assessment. Nat. Hazards Earth Syst. Sci., 12, 2769-2784, www.nat-hazards-earth-syst-sci.net/12/2769/2012/, <doi:10.5194/nhess-12-2769-2012>.

r-conversim 0.1.0
Propagated dependencies: r-word2vec@0.4.1 r-topicmodels@0.2-17 r-tm@0.7-16 r-slam@0.1-55 r-sentimentr@2.9.0 r-lsa@0.73.3 r-lme4@1.1-37 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/chaoliu-cl/conversim
Licenses: GPL 3+
Build system: r
Synopsis: Conversation Similarity Analysis
Description:

Analyze and compare conversations using various similarity measures including topic, lexical, semantic, structural, stylistic, sentiment, participant, and timing similarities. Supports both pairwise conversation comparisons and analysis of multiple dyads. Methods are based on established research: Topic modeling: Blei et al. (2003) <doi:10.1162/jmlr.2003.3.4-5.993>; Landauer et al. (1998) <doi:10.1080/01638539809545028>; Lexical similarity: Jaccard (1912) <doi:10.1111/j.1469-8137.1912.tb05611.x>; Semantic similarity: Salton & Buckley (1988) <doi:10.1016/0306-4573(88)90021-0>; Mikolov et al. (2013) <doi:10.48550/arXiv.1301.3781>; Pennington et al. (2014) <doi:10.3115/v1/D14-1162>; Structural and stylistic analysis: Graesser et al. (2004) <doi:10.1075/target.21131.ryu>; Sentiment analysis: Rinker (2019) <https://github.com/trinker/sentimentr>.

r-crc32c 0.0.3
Dependencies: cmake@4.1.3
Propagated dependencies: r-tidycpp@0.0.9
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/google/crc32c
Licenses: GPL 2+
Build system: r
Synopsis: Cyclic Redundancy Check with CPU-Specific Acceleration
Description:

Hardware-based support for CRC32C cyclic redundancy checksum function is made available for x86_64 systems with SSE2 support as well as for arm64', and detected at build-time via cmake with a software-based fallback. This functionality is exported at the C'-language level for use by other packages. CRC32C is described in RFC 3270 at <https://datatracker.ietf.org/doc/html/rfc3720> and is based on Castagnoli et al <doi:10.1109/26.231911>.

r-caesar-suite 0.3.0
Propagated dependencies: r-seurat@5.3.1 r-scater@1.38.0 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-progress@1.2.3 r-profast@1.7 r-pbapply@1.7-4 r-matrix@1.7-4 r-irlba@2.3.5.1 r-ggrepel@0.9.6 r-ggplot2@4.0.1 r-future@1.68.0 r-furrr@0.3.1 r-desctools@0.99.60 r-ade4@1.7-23
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/XiaoZhangryy/CAESAR.Suite
Licenses: GPL 2+
Build system: r
Synopsis: CAESAR: a Cross-Technology and Cross-Resolution Framework for Spatial Omics Annotation
Description:

Biotechnology in spatial omics has advanced rapidly over the past few years, enhancing both throughput and resolution. However, existing annotation pipelines in spatial omics predominantly rely on clustering methods, lacking the flexibility to integrate extensive annotated information from single-cell RNA sequencing (scRNA-seq) due to discrepancies in spatial resolutions, species, or modalities. Here we introduce the CAESAR suite, an open-source software package that provides image-based spatial co-embedding of locations and genomic features. It uniquely transfers labels from scRNA-seq reference, enabling the annotation of spatial omics datasets across different technologies, resolutions, species, and modalities, based on the conserved relationship between signature genes and cells/locations at an appropriate level of granularity. Notably, CAESAR enriches location-level pathways, allowing for the detection of gradual biological pathway activation within spatially defined domain types. More details on the methods related to our paper currently under submission. A full reference to the paper will be provided in future versions once the paper is published.

r-clsiep15 0.1.0
Propagated dependencies: r-tidyr@1.3.1 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=CLSIEP15
Licenses: Expat
Build system: r
Synopsis: Clinical and Laboratory Standards Institute (CLSI) EP15-A3 Calculations
Description:

Calculations of "EP15-A3 document. A manual for user verification of precision and estimation of bias" CLSI (2014, ISBN:1-56238-966-1).

r-ccmestimator 1.0.0
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://github.com/xiw021/ccmEstimator
Licenses: GPL 3
Build system: r
Synopsis: Comparative Causal Mediation Estimation
Description:

This package provides functions to perform comparative causal mediation analysis to compare the mediation effects of different treatments via a common mediator. Results contain the estimates and confidence intervals for the two comparative causal mediation analysis estimands, as well as the ATE and ACME for each treatment. Functions provided in the package will automatically assess the comparative causal mediation analysis scope conditions (i.e. for each comparative causal mediation estimand, a numerator and denominator that are both estimated with the desired statistical significance and of the same sign). Results will be returned for each comparative causal mediation estimand only if scope conditions are met for it. See details in Bansak(2020)<doi:10.1017/pan.2019.31>.

r-comparegroups 4.10.2
Propagated dependencies: r-writexl@1.5.4 r-survival@3.8-3 r-rstatix@0.7.3 r-rmdformats@1.0.4 r-rmarkdown@2.30 r-pmcmrplus@1.9.12 r-officer@0.7.1 r-knitr@1.50 r-kableextra@1.4.0 r-hardyweinberg@1.7.9 r-flextable@0.9.10 r-chron@2.3-62
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://isubirana.github.io/compareGroups/index.html
Licenses: GPL 2+
Build system: r
Synopsis: Descriptive Analysis by Groups
Description:

Create data summaries for quality control, extensive reports for exploring data, as well as publication-ready univariate or bivariate tables in several formats (plain text, HTML,LaTeX, PDF, Word or Excel. Create figures to quickly visualise the distribution of your data (boxplots, barplots, normality-plots, etc.). Display statistics (mean, median, frequencies, incidences, etc.). Perform the appropriate tests (t-test, Analysis of variance, Kruskal-Wallis, Fisher, log-rank, ...) depending on the nature of the described variable (normal, non-normal or qualitative). Summarize genetic data (Single Nucleotide Polymorphisms) data displaying Allele Frequencies and performing Hardy-Weinberg Equilibrium tests among other typical statistics and tests for these kind of data.

r-cctest 2.3.1
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=cctest
Licenses: Expat FSDG-compatible
Build system: r
Synopsis: Canonical Correlations and Tests of Independence
Description:

This package provides a simple interface for multivariate correlation analysis that unifies various classical statistical procedures including t-tests, tests in univariate and multivariate linear models, parametric and nonparametric tests for correlation, Kruskal-Wallis tests, common approximate versions of Wilcoxon rank-sum and signed rank tests, chi-squared tests of independence, score tests of particular hypotheses in generalized linear models, canonical correlation analysis and linear discriminant analysis.

r-cpop 1.0.8
Propagated dependencies: r-rdpack@2.6.4 r-rcpp@1.1.0 r-pracma@2.4.6 r-pacman@0.5.1 r-mathjaxr@1.8-0 r-ggplot2@4.0.1 r-crops@1.0.3
Channel: guix-cran
Location: guix-cran/packages/c.scm (guix-cran packages c)
Home page: https://cran.r-project.org/package=cpop
Licenses: GPL 2+
Build system: r
Synopsis: Detection of Multiple Changes in Slope in Univariate Time-Series
Description:

Detects multiple changes in slope using the CPOP dynamic programming approach of Fearnhead, Maidstone, and Letchford (2019) <doi:10.1080/10618600.2018.1512868>. This method finds the best continuous piecewise linear fit to data under a criterion that measures fit to data using the residual sum of squares, but penalizes complexity based on an L0 penalty on changes in slope. Further information regarding the use of this package with detailed examples can be found in Fearnhead and Grose (2024) <doi:10.18637/jss.v109.i07>.

Page: 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197198199200201202203204205206207208209210211212213214215216217218219220221222223224225226227228229230231232233234235236237238239240241242243244245246247248249250251252253254255256257258259260261262263264265266267268269270271272273274275276277278279280281282283284285286287288289290291292293294295296297298299300301302303304305306307308309310311312313314315316317318319320321322323324325326327328329330331332333334335336337338339340341342343344345346347348349350351352353354355356357358359360361362363364365366367368369370371372373374375376377378379380381382383384385386387388389390391392393394395396397398399400401402403404405406407408409410411412413414415416417418419420421422423424425426427428429430431432433434435436437438439440441442443444445446447448449450451452453454455456457458459460461462463464465466467468469470471472473474475476477478479480481482483484485486487488489490491492493494495496497498499500501502503504505506507508509510511512513514515516517518519520521522523524525526527528529530531532533534535536537538539540541542543544545546547548549550551552553554555556557558559560561562563564565566567568569570571572573574575576577578579580581582583584585586587588589590591592593594595596597598599600601602603604605606607608609610611612613614615616617618619620621622623624625626627628629630631632633634635636637638639640641642643644645646647648649650651652653654655656657658659660661662663664665666667668669670671672673674675676677678679680681682683684685686687688689690691692693694695696697698699700701702703704705706707708709710711712713714715716717718719720721722723724725726727728729730731732733734735736737738739740741742743744745746747748749750751752753754755756757758759760761762763764765766767768769770771772773774775776777778779780781782783784785786787788789790791792793794795796797798799800801802803804805806807808809810811812813814815816817818819820821822823824825826827828829830831832833834835836837838839840841842843844845846847848849850851852853854855856857858859860861862863864865866867868869870871872873874875876877878879880881882883884885886887888889890891892893894895
Total results: 21457