_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
      \_\ \       /  \ \\ \ \   / / /     / /  \
      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
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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-topchef 0.2.0
Propagated dependencies: r-tidyr@1.3.1 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://github.com/celevitz/topChef
Licenses: Expat
Build system: r
Synopsis: Top Chef Data
Description:

Several datasets which describe the chef contestants in Top Chef, the challenges that they compete in, and the results of those challenges. This data is useful for practicing data wrangling, graphing, and analyzing how each season of Top Chef played out.

r-tetragon 1.3.0
Propagated dependencies: r-tictoc@1.2.1 r-stringr@1.6.0 r-scales@1.4.0 r-rfast@2.1.5.2 r-readr@2.1.6 r-purrr@1.2.0 r-philentropy@0.10.0 r-narray@0.5.2 r-moments@0.14.1 r-modeest@2.4.0 r-lubridate@1.9.4 r-imputets@3.4 r-greybox@2.0.8 r-ggplot2@4.0.1 r-fastdummies@1.7.5 r-fancova@0.6-1 r-entropy@1.3.2 r-dqrng@0.4.1 r-abind@1.4-8
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://rpubs.com/giancarlo_vercellino/tetragon
Licenses: GPL 3
Build system: r
Synopsis: Automatic Sequence Prediction by Expansion of the Distance Matrix
Description:

Each sequence is predicted by expanding the distance matrix. The compact set of hyper-parameters is tuned through random search.

r-tall 0.5.2
Propagated dependencies: r-word2vec@0.4.1 r-visnetwork@2.1.4 r-umap@0.2.10.0 r-udpipe@0.8.16 r-topicmodels@0.2-17 r-tidyr@1.3.1 r-tidygraph@1.3.1 r-textrank@0.3.1 r-strucchange@1.5-4 r-stringr@1.6.0 r-sparkline@2.0 r-shinywidgets@0.9.0 r-shinyjs@2.1.0 r-shinyfiles@0.9.3 r-shinydashboardplus@2.0.6 r-shinycssloaders@1.1.0 r-shiny@1.11.1 r-rspectra@0.16-2 r-rlang@1.1.6 r-readxl@1.4.5 r-readtext@0.92.1 r-readr@2.1.6 r-rcpp@1.1.0 r-ranger@0.17.0 r-purrr@1.2.0 r-promises@1.5.0 r-plotly@4.11.0 r-pdftools@3.6.0 r-pagedown@0.23 r-openxlsx@4.2.8.1 r-later@1.4.4 r-jsonlite@2.0.0 r-igraph@2.2.1 r-httr2@1.2.1 r-ggwordcloud@0.6.2 r-ggraph@2.2.2 r-ggplot2@4.0.1 r-fontawesome@0.5.3 r-dt@0.34.0 r-dplyr@1.1.4 r-doparallel@1.0.17 r-curl@7.0.0 r-chromote@0.5.1 r-ca@0.71.1 r-base64enc@0.1-3
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://github.com/massimoaria/tall
Licenses: Expat
Build system: r
Synopsis: Text Analysis for All
Description:

An R shiny app designed for diverse text analysis tasks, offering a wide range of methodologies tailored to Natural Language Processing (NLP) needs. It is a versatile, general-purpose tool for analyzing textual data. tall features a comprehensive workflow, including data cleaning, preprocessing, statistical analysis, and visualization, all integrated for effective text analysis.

r-tailplots 0.1.1
Propagated dependencies: 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=tailplots
Licenses: Expat
Build system: r
Synopsis: Estimators and Plots for Gamma and Pareto Tail Detection
Description:

Estimators for two functionals used to detect Gamma, Pareto or Lognormal distributions, as well as distributions exhibiting similar tail behavior, as introduced by Iwashita and Klar (2023) <doi:10.1111/stan.12316> and Klar (2024) <doi:10.1080/00031305.2024.2413081>. One of these functionals, g, originally proposed by Asmussen and Lehtomaa (2017) <doi:10.3390/risks5010010>, distinguishes between log-convex and log-concave tail behavior. Furthermore the characterization of the lognormal distribution is based on the work of Mosimann (1970) <doi:10.2307/2284599>. The package also includes methods for visualizing these estimators and their associated confidence intervals across various threshold values.

r-truncnormbayes 0.0.3
Propagated dependencies: r-stanheaders@2.32.10 r-rstantools@2.5.0 r-rstan@2.32.7 r-rdpack@2.6.4 r-rcppparallel@5.1.11-1 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://github.com/mathurlabstanford/truncnormbayes
Licenses: GPL 3+
Build system: r
Synopsis: Estimates Moments for a Truncated Normal Distribution using 'Stan'
Description:

Finds the posterior modes for the mean and standard deviation for a truncated normal distribution with one or two known truncation points. The method used extends Bayesian methods for parameter estimation for a singly truncated normal distribution under the Jeffreys prior (see Zhou X, Giacometti R, Fabozzi FJ, Tucker AH (2014). "Bayesian estimation of truncated data with applications to operational risk measurement". <doi:10.1080/14697688.2012.752103>). This package additionally allows for a doubly truncated normal distribution.

r-tidygenr 0.1.7
Propagated dependencies: r-writexl@1.5.4 r-tidyselect@1.2.1 r-tidyr@1.3.1 r-tibble@3.3.0 r-stringr@1.6.0 r-shortread@1.68.0 r-readr@2.1.6 r-plyr@1.8.9 r-patchwork@1.3.2 r-glue@1.8.0 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-digest@0.6.39 r-decipher@3.6.0 r-dada2@1.38.0 r-biostrings@2.78.0
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://github.com/csmiguel/tidyGenR
Licenses: GPL 3+
Build system: r
Synopsis: Tidy Multilocus Amplicon Genotypes
Description:

Variant determination and genotyping from high throughput sequences from multilocus amplicon libraries, typically sequenced in Illumina MiSeq or similar. It provides a set of core functions for the central steps: demultiplex by locus, truncate reads, variant calling, and genotype calling. Additionally, it provides a set of functions for diagnosis and estimation of best running parameters and multiple extensions for genotype/variants manipulation and reformatting. Output variants and genotypes are output in tidy format, thus facilitating reformatting, manipulation and potential connection to other R packages.

r-trainer 2.2.12
Propagated dependencies: r-xgboost@1.7.11.1 r-stringr@1.6.0 r-rpart@4.1.24 r-rocr@1.0-11 r-rlang@1.1.6 r-randomforest@4.7-1.2 r-nnet@7.3-20 r-neuralnet@1.44.2 r-mass@7.3-65 r-kknn@1.4.1 r-glmnet@4.1-10 r-ggplot2@4.0.1 r-gbm@2.2.2 r-e1071@1.7-16 r-dplyr@1.1.4 r-adabag@5.1
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://promidat.website/
Licenses: GPL 2+
Build system: r
Synopsis: Predictive (Classification and Regression) Models Homologator
Description:

This package provides methods to unify the different ways of creating predictive models and their different predictive formats for classification and regression. It includes methods such as K-Nearest Neighbors Schliep, K. P. (2004) <doi:10.5282/ubm/epub.1769>, Decision Trees Leo Breiman, Jerome H. Friedman, Richard A. Olshen, Charles J. Stone (2017) <doi:10.1201/9781315139470>, ADA Boosting Esteban Alfaro, Matias Gamez, Noelia Garcà a (2013) <doi:10.18637/jss.v054.i02>, Extreme Gradient Boosting Chen & Guestrin (2016) <doi:10.1145/2939672.2939785>, Random Forest Breiman (2001) <doi:10.1023/A:1010933404324>, Neural Networks Venables, W. N., & Ripley, B. D. (2002) <ISBN:0-387-95457-0>, Support Vector Machines Bennett, K. P. & Campbell, C. (2000) <doi:10.1145/380995.380999>, Bayesian Methods Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (1995) <doi:10.1201/9780429258411>, Linear Discriminant Analysis Venables, W. N., & Ripley, B. D. (2002) <ISBN:0-387-95457-0>, Quadratic Discriminant Analysis Venables, W. N., & Ripley, B. D. (2002) <ISBN:0-387-95457-0>, Logistic Regression Dobson, A. J., & Barnett, A. G. (2018) <doi:10.1201/9781315182780> and Penalized Logistic Regression Friedman, J. H., Hastie, T., & Tibshirani, R. (2010) <doi:10.18637/jss.v033.i01>.

r-testex 0.2.1
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://github.com/dgkf/testex
Licenses: Expat
Build system: r
Synopsis: Add Tests to Examples
Description:

Add tests in-line in examples. Provides standalone functions for facilitating easier test writing in Rd files. However, a more familiar interface is provided using roxygen2 tags. Tools are also provided for facilitating package configuration and use with testthat'.

r-tidydfidx 0.0-3
Propagated dependencies: r-vctrs@0.6.5 r-rdpack@2.6.4 r-pillar@1.11.1 r-dplyr@1.1.4 r-dfidx@0.2-0
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://cran.r-project.org/package=tidydfidx
Licenses: GPL 2+
Build system: r
Synopsis: Indexed 'tibble' and Methods for 'dplyr'
Description:

This package provides extended data frames, with a special data frame column which contains two indexes, with potentially a nesting structure, and support for tibbles and methods for dplyr'.

r-toprdata 1.0.2
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://cran.r-project.org/package=toprdata
Licenses: LGPL 3+
Build system: r
Synopsis: Gene and Exon Data from Ensembl
Description:

Gene and exon information from Ensembl genome builds GRCh38.p13 (104) and GRCh37 (v40) to use with the topr package.

r-tenm 0.5.1
Propagated dependencies: r-tidyr@1.3.1 r-terra@1.8-86 r-stringr@1.6.0 r-sf@1.0-23 r-rgl@1.3.31 r-purrr@1.2.0 r-mass@7.3-65 r-lubridate@1.9.4 r-future@1.68.0 r-furrr@0.3.1 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://luismurao.github.io/tenm/
Licenses: GPL 3
Build system: r
Synopsis: Temporal Ecological Niche Models
Description:

This package implements methods and functions to calibrate time-specific niche models (multi-temporal calibration), letting users execute a strict calibration and selection process of niche models based on ellipsoids, as well as functions to project the potential distribution in the present and in global change scenarios.The tenm package has functions to recover information that may be lost or overlooked while applying a data curation protocol. This curation involves preserving occurrences that may appear spatially redundant (occurring in the same pixel) but originate from different time periods. A novel aspect of this package is that it might reconstruct the fundamental niche more accurately than mono-calibrated approaches. The theoretical background of the package can be found in Peterson et al. (2011)<doi:10.5860/CHOICE.49-6266>.

r-twowaytests 1.5
Propagated dependencies: r-wesanderson@0.3.7 r-onewaytests@3.1 r-nortest@1.0-4 r-mass@7.3-65 r-ggplot2@4.0.1 r-car@3.1-3
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://cran.r-project.org/package=twowaytests
Licenses: GPL 2+
Build system: r
Synopsis: Two-Way Tests in Independent Groups Designs
Description:

This package performs two-way tests in independent groups designs. These are two-way ANOVA, two-way ANOVA under heteroscedasticity: parametric bootstrap based generalized test and generalized pivotal quantity based generalized test, two-way ANOVA for medians, trimmed means, M-estimators. The package performs descriptive statistics and graphical approaches. Moreover, it assesses variance homogeneity and normality of data in each group via tests and plots. All twowaytests functions are designed for two-way layout (Dag et al., 2024, <doi:10.1016/j.softx.2024.101862>).

r-tuvalues 1.1.1
Propagated dependencies: r-roi-plugin-glpk@1.0-0 r-roi@1.0-1 r-gtools@3.9.5
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://github.com/mariaguilleng/TUvalues
Licenses: AGPL 3+
Build system: r
Synopsis: Tools for Calculating Allocations in Game Theory using Exact and Approximated Methods
Description:

The main objective of cooperative Transferable-Utility games (TU-games) is to allocate a good among the agents involved. The package implements major solution concepts including the Shapley value, Banzhaf value, and egalitarian rules, alongside their extensions for structured games: the Owen value and Banzhaf-Owen value for games with a priori unions, and the Myerson value for communication games on networks. To address the inherent exponential computational complexity of exact evaluation, the package offers both exact algorithms and linear approximation methods based on sampling, enabling the analysis of large-scale games. Additionally, it supports core set-based solutions, allowing computation of the vertices and the centroid of the core.

r-tsfngm 0.1.0
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://cran.r-project.org/package=tsfngm
Licenses: GPL 3
Build system: r
Synopsis: Time Series Forecasting using Nonlinear Growth Models
Description:

Nonlinear growth models are extremely useful in gaining insight into the underlying mechanism. These models are generally mechanistic, with parameters that have biological meaning. This package allows you to fit and forecast time series data using nonlinear growth models.

r-torchvision 0.8.0
Propagated dependencies: r-zeallot@0.2.0 r-withr@3.0.2 r-torch@0.16.3 r-tiff@0.1-12 r-rlang@1.1.6 r-rappdirs@0.3.3 r-png@0.1-8 r-magrittr@2.0.4 r-jsonlite@2.0.0 r-jpeg@0.1-11 r-glue@1.8.0 r-fs@1.6.6 r-cli@3.6.5 r-abind@1.4-8
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://torchvision.mlverse.org
Licenses: Expat
Build system: r
Synopsis: Models, Datasets and Transformations for Images
Description:

This package provides access to datasets, models and preprocessing facilities for deep learning with images. Integrates seamlessly with the torch package and it's API borrows heavily from PyTorch vision package.

r-tbd 0.1.0
Propagated dependencies: r-numderiv@2016.8-1.1
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://github.com/KillingVectorField/causal-inference-truncated-by-death
Licenses: GPL 2+
Build system: r
Synopsis: Estimation of Causal Effects with Outcomes Truncated by Death
Description:

Estimation of the survivor average causal effect under outcomes truncated by death, which requires the existence of a substitution variable. It can be applied to both experimental and observational data.

r-tlm 0.2.0
Propagated dependencies: r-boot@1.3-32
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://cran.r-project.org/package=tlm
Licenses: GPL 3+
Build system: r
Synopsis: Effects under Linear, Logistic and Poisson Regression Models with Transformed Variables
Description:

Computation of effects under linear, logistic and Poisson regression models with transformed variables. Logarithm and power transformations are allowed. Effects can be displayed both numerically and graphically in both the original and the transformed space of the variables. The methods are described in Barrera-Gomez and Basagana (2015) <doi:10.1097/EDE.0000000000000247>.

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-tsxtreme 0.3.4
Propagated dependencies: r-tictoc@1.2.1 r-mvtnorm@1.3-3 r-mass@7.3-65 r-evd@2.3-7.1
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://cran.r-project.org/package=tsxtreme
Licenses: GPL 2+
Build system: r
Synopsis: Bayesian Modelling of Extremal Dependence in Time Series
Description:

Characterisation of the extremal dependence structure of time series, avoiding pre-processing and filtering as done typically with peaks-over-threshold methods. It uses the conditional approach of Heffernan and Tawn (2004) <DOI:10.1111/j.1467-9868.2004.02050.x> which is very flexible in terms of extremal and asymptotic dependence structures, and Bayesian methods improve efficiency and allow for deriving measures of uncertainty. For example, the extremal index, related to the size of clusters in time, can be estimated and samples from its posterior distribution obtained.

r-tfhub 0.8.1
Propagated dependencies: r-vctrs@0.6.5 r-tensorflow@2.20.0 r-rstudioapi@0.17.1 r-reticulate@1.44.1 r-magrittr@2.0.4
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://github.com/rstudio/tfhub
Licenses: ASL 2.0
Build system: r
Synopsis: Interface to 'TensorFlow' Hub
Description:

TensorFlow Hub is a library for the publication, discovery, and consumption of reusable parts of machine learning models. A module is a self-contained piece of a TensorFlow graph, along with its weights and assets, that can be reused across different tasks in a process known as transfer learning. Transfer learning train a model with a smaller dataset, improve generalization, and speed up training.

r-tidyfit 0.7.4
Propagated dependencies: r-yardstick@1.3.2 r-vctrs@0.6.5 r-tidyr@1.3.1 r-tibble@3.3.0 r-rsample@1.3.1 r-rlang@1.1.6 r-purrr@1.2.0 r-progressr@0.18.0 r-mass@7.3-65 r-generics@0.1.4 r-furrr@0.3.1 r-dplyr@1.1.4 r-dials@1.4.2 r-crayon@1.5.3 r-broom@1.0.10
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://tidyfit.residualmetrics.com
Licenses: GPL 3
Build system: r
Synopsis: Regularized Linear Modeling with Tidy Data
Description:

An extension to the R tidy data environment for automated machine learning. The package allows fitting and cross validation of linear regression and classification algorithms on grouped data.

r-tern-mmrm 0.3.3
Propagated dependencies: r-tidyr@1.3.1 r-tern@0.9.10 r-rtables@0.6.15 r-rlang@1.1.6 r-parallelly@1.45.1 r-mmrm@0.3.17 r-magrittr@2.0.4 r-lifecycle@1.0.4 r-ggplot2@4.0.1 r-generics@0.1.4 r-formatters@0.5.12 r-emmeans@2.0.0 r-dplyr@1.1.4 r-cowplot@1.2.0 r-checkmate@2.3.3
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://github.com/insightsengineering/tern.mmrm
Licenses: ASL 2.0
Build system: r
Synopsis: Tables and Graphs for Mixed Models for Repeated Measures (MMRM)
Description:

Mixed models for repeated measures (MMRM) are a popular choice for analyzing longitudinal continuous outcomes in randomized clinical trials and beyond; see for example Cnaan, Laird and Slasor (1997) <doi:10.1002/(SICI)1097-0258(19971030)16:20%3C2349::AID-SIM667%3E3.0.CO;2-E>. This package provides an interface for fitting MMRM within the tern <https://cran.r-project.org/package=tern> framework by Zhu et al. (2023) and tabulate results easily using rtables <https://cran.r-project.org/package=rtables> by Becker et al. (2023). It builds on mmrm <https://cran.r-project.org/package=mmrm> by Sabanés Bové et al. (2023) for the actual MMRM computations.

r-transformmos 0.1.0
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://cran.r-project.org/package=transformmos
Licenses: Expat
Build system: r
Synopsis: Transform MOS Values to be Robust for using Rank Based Statistics
Description:

Implementation of the transformation of the Mean Opinion Scores (MOS) to be used before applying the rank based statistical techniques. The method and its necessity is described in: Babak Naderi, Sebastian Möller (2020) <arXiv:2004.11490>.

r-tetrys 1.2
Propagated dependencies: r-tuner@1.4.7 r-audio@0.1-11
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://cran.r-project.org/package=tetRys
Licenses: GPL 3
Build system: r
Synopsis: Game of 'tetRys'
Description:

This package provides a game inspired by Tetris'. Opens a plot window device and starts a game of tetRys in it. Steer the tetrominos with the arrow keys, press Pause to pause and Esc to end the game.

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