_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
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      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
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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-texter 0.1.9
Propagated dependencies: r-tidytext@0.4.3 r-tidyr@1.3.1 r-textdata@0.4.5 r-stringr@1.6.0 r-stopwords@2.3 r-purrr@1.2.0 r-plyr@1.8.9 r-magrittr@2.0.4 r-ggplot2@4.0.1 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://github.com/simmieyungie/texter
Licenses: Expat
Build system: r
Synopsis: An Easy Text and Sentiment Analysis Library
Description:

Implement text and sentiment analysis with texter'. Generate sentiment scores on text data and also visualize sentiments. texter allows you to quickly generate insights on your data. It includes support for lexicons such as NRC and Bing'.

r-transpror 1.0.7
Propagated dependencies: r-tidyr@1.3.1 r-tidygraph@1.3.1 r-tibble@3.3.0 r-sva@3.58.0 r-stringr@1.6.0 r-spiralize@1.1.0 r-rlang@1.1.6 r-magrittr@2.0.4 r-limma@3.66.0 r-hmisc@5.2-4 r-ggvenndiagram@1.5.4 r-ggtree@4.0.1 r-ggraph@2.2.2 r-ggpubr@0.6.2 r-ggplot2@4.0.1 r-ggnewscale@0.5.2 r-ggdensity@1.0.0 r-geomtextpath@0.2.0 r-edger@4.8.0 r-dplyr@1.1.4 r-deseq2@1.50.2 r-complexheatmap@2.26.0 r-circlize@0.4.16
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://github.com/SSSYDYSSS/TransProRBook
Licenses: Expat
Build system: r
Synopsis: Analysis and Visualization of Multi-Omics Data
Description:

This package provides a tool for comprehensive transcriptomic data analysis, with a focus on transcript-level data preprocessing, expression profiling, differential expression analysis, and functional enrichment. It enables researchers to identify key biological processes, disease biomarkers, and gene regulatory mechanisms. TransProR is aimed at researchers and bioinformaticians working with RNA-Seq data, providing an intuitive framework for in-depth analysis and visualization of transcriptomic datasets. The package includes comprehensive documentation and usage examples to guide users through the entire analysis pipeline. The differential expression analysis methods incorporated in the package include limma (Ritchie et al., 2015, <doi:10.1093/nar/gkv007>; Smyth, 2005, <doi:10.1007/0-387-29362-0_23>), edgeR (Robinson et al., 2010, <doi:10.1093/bioinformatics/btp616>), DESeq2 (Love et al., 2014, <doi:10.1186/s13059-014-0550-8>), and Wilcoxon tests (Li et al., 2022, <doi:10.1186/s13059-022-02648-4>), providing flexible and robust approaches to RNA-Seq data analysis. For more information, refer to the package vignettes and related publications.

r-tidyllm 0.3.5
Propagated dependencies: r-tibble@3.3.0 r-stringr@1.6.0 r-s7@0.2.1 r-rlang@1.1.6 r-purrr@1.2.0 r-png@0.1-8 r-pdftools@3.6.0 r-lubridate@1.9.4 r-lifecycle@1.0.4 r-jsonlite@2.0.0 r-httr2@1.2.1 r-glue@1.8.0 r-curl@7.0.0 r-cli@3.6.5 r-base64enc@0.1-3
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://edubruell.github.io/tidyllm/
Licenses: Expat
Build system: r
Synopsis: Tidy Integration of Large Language Models
Description:

This package provides a tidy interface for integrating large language model (LLM) APIs such as Claude', Openai', Gemini','Mistral and local models via Ollama into R workflows. The package supports text and media-based interactions, interactive message history, batch request APIs, and a tidy, pipeline-oriented interface for streamlined integration into data workflows. Web services are available at <https://www.anthropic.com>, <https://openai.com>, <https://aistudio.google.com/>, <https://mistral.ai/> and <https://ollama.com>.

r-taxonomizr 0.11.1
Propagated dependencies: r-rsqlite@2.4.4 r-r-utils@2.13.0 r-data-table@1.17.8 r-curl@7.0.0
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://github.com/sherrillmix/taxonomizr/
Licenses: GPL 2+ FSDG-compatible
Build system: r
Synopsis: Functions to Work with NCBI Accessions and Taxonomy
Description:

This package provides functions for assigning taxonomy to NCBI accession numbers and taxon IDs based on NCBI's accession2taxid and taxdump files. This package allows the user to download NCBI data dumps and create a local database for fast and local taxonomic assignment.

r-tworegression 1.1.1
Propagated dependencies: r-tidyr@1.3.1 r-rlang@1.1.6 r-rcpproll@0.3.1 r-proc@1.19.0.1 r-pautilities@1.2.1 r-magrittr@2.0.4 r-lubridate@1.9.4 r-gridextra@2.3 r-ggplot2@4.0.1 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://github.com/paulhibbing/TwoRegression
Licenses: GPL 3
Build system: r
Synopsis: Develop and Apply Two-Regression Algorithms
Description:

Facilitates development and application of two-regression algorithms for research-grade wearable devices. It provides an easy way for users to access previously-developed algorithms, and also to develop their own. Initial motivation came from Hibbing PR, LaMunion SR, Kaplan AS, & Crouter SE (2018) <doi:10.1249/MSS.0000000000001532>. However, other algorithms are now supported. Please see the associated references in the package documentation for full details of the algorithms that are supported.

r-taxanorm 2.4
Propagated dependencies: r-vegan@2.7-2 r-s4vectors@0.48.0 r-pscl@1.5.9 r-phyloseq@1.54.0 r-parallelly@1.45.1 r-microbiome@1.32.0 r-matrixstats@1.5.0 r-mass@7.3-65 r-ggplot2@4.0.1 r-future-apply@1.20.0 r-future@1.68.0 r-biocgenerics@0.56.0
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://github.com/wangziyue57/TaxaNorm
Licenses: GPL 3
Build system: r
Synopsis: Feature-Wise Normalization for Microbiome Sequencing Data
Description:

This package provides a novel feature-wise normalization method based on a zero-inflated negative binomial model. This method assumes that the effects of sequencing depth vary for each taxon on their mean and also incorporates a rational link of zero probability and taxon dispersion as a function of sequencing depth. Ziyue Wang, Dillon Lloyd, Shanshan Zhao, Alison Motsinger-Reif (2023) <doi:10.1101/2023.10.31.563648>.

r-tsci 3.0.5
Propagated dependencies: r-xgboost@1.7.11.1 r-rfast@2.1.5.2 r-ranger@0.17.0 r-fastdummies@1.7.5
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://github.com/dlcarl/TSCI
Licenses: GPL 3+
Build system: r
Synopsis: Tools for Causal Inference with Possibly Invalid Instrumental Variables
Description:

Two stage curvature identification with machine learning for causal inference in settings when instrumental variable regression is not suitable because of potentially invalid instrumental variables. Based on Guo and Buehlmann (2022) "Two Stage Curvature Identification with Machine Learning: Causal Inference with Possibly Invalid Instrumental Variables" <doi:10.48550/arXiv.2203.12808>. The vignette is available in Carl, Emmenegger, Bühlmann and Guo (2025) "TSCI: Two Stage Curvature Identification for Causal Inference with Invalid Instruments in R" <doi:10.18637/jss.v114.i07>.

r-textir 2.0-5
Propagated dependencies: r-matrix@1.7-4 r-gamlr@1.13-8 r-distrom@1.0.2
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: http://taddylab.com
Licenses: GPL 3
Build system: r
Synopsis: Inverse Regression for Text Analysis
Description:

Multinomial (inverse) regression inference for text documents and associated attributes. For details see: Taddy (2013 JASA) Multinomial Inverse Regression for Text Analysis <arXiv:1012.2098> and Taddy (2015, AoAS), Distributed Multinomial Regression, <arXiv:1311.6139>. A minimalist partial least squares routine is also included. Note that the topic modeling capability of earlier textir is now a separate package, maptpx'.

r-tatest 1.0
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://cran.r-project.org/package=tatest
Licenses: GPL 3
Build system: r
Synopsis: Two-Group Ta-Test
Description:

The ta-test is a modified two-sample or two-group t-test of Gosset (1908). In small samples with less than 15 replicates,the ta-test significantly reduces type I error rate but has almost the same power with the t-test and hence can greatly enhance reliability or reproducibility of discoveries in biology and medicine. The ta-test can test single null hypothesis or multiple null hypotheses without needing to correct p-values.

r-tsdt 1.0.8
Propagated dependencies: r-survrm2@1.0-4 r-survival@3.8-3 r-rpart@4.1.24 r-party@1.3-18 r-modeltools@0.2-24 r-mlbench@2.1-6 r-hash@2.2.6.3
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://github.com/EliLillyCo/CRAN_TSDT
Licenses: GPL 2+
Build system: r
Synopsis: Treatment-Specific Subgroup Detection Tool
Description:

This package implements a method for identifying subgroups with superior response relative to the overall sample.

r-tsensembler 0.1.0
Propagated dependencies: r-zoo@1.8-14 r-xts@0.14.1 r-xgboost@1.7.11.1 r-softimpute@1.4-3 r-rcpproll@0.3.1 r-ranger@0.17.0 r-pls@2.8-5 r-monmlp@1.1.5-1 r-kernlab@0.9-33 r-glmnet@4.1-10 r-gbm@2.2.2 r-foreach@1.5.2 r-earth@5.3.4 r-doparallel@1.0.17 r-cubist@0.5.1
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://github.com/vcerqueira/tsensembler
Licenses: GPL 2+
Build system: r
Synopsis: Dynamic Ensembles for Time Series Forecasting
Description:

This package provides a framework for dynamically combining forecasting models for time series forecasting predictive tasks. It leverages machine learning models from other packages to automatically combine expert advice using metalearning and other state-of-the-art forecasting combination approaches. The predictive methods receive a data matrix as input, representing an embedded time series, and return a predictive ensemble model. The ensemble use generic functions predict() and forecast() to forecast future values of the time series. Moreover, an ensemble can be updated using methods, such as update_weights() or update_base_models()'. A complete description of the methods can be found in: Cerqueira, V., Torgo, L., Pinto, F., and Soares, C. "Arbitrated Ensemble for Time Series Forecasting." to appear at: Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2017; and Cerqueira, V., Torgo, L., and Soares, C.: "Arbitrated Ensemble for Solar Radiation Forecasting." International Work-Conference on Artificial Neural Networks. Springer, 2017 <doi:10.1007/978-3-319-59153-7_62>.

r-tidycharts 0.1.3
Propagated dependencies: r-testthat@3.3.0 r-stringr@1.6.0 r-rsvg@2.7.0 r-rlang@1.1.6 r-magrittr@2.0.4 r-magick@2.9.0 r-lubridate@1.9.4 r-knitr@1.50 r-htmlwidgets@1.6.4
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://mi2datalab.github.io/tidycharts/
Licenses: GPL 3+
Build system: r
Synopsis: Generate Tidy Charts Inspired by 'IBCS'
Description:

There is a wide range of R packages created for data visualization, but still, there was no simple and easily accessible way to create clean and transparent charts - up to now. The tidycharts package enables the user to generate charts compliant with International Business Communication Standards ('IBCS'). It means unified bar widths, colors, chart sizes, etc. Creating homogeneous reports has never been that easy! Additionally, users can apply semantic notation to indicate different data scenarios (plan, budget, forecast). What's more, it is possible to customize the charts by creating a personal color pallet with the possibility of switching to default options after the experiments. We wanted the package to be helpful in writing reports, so we also made joining charts in a one, clear image possible. All charts are generated in SVG format and can be shown in the RStudio viewer pane or exported to HTML output of knitr'/'markdown'.

r-tseriesmma 0.1.1
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://cran.r-project.org/package=TSeriesMMA
Licenses: GPL 2+
Build system: r
Synopsis: Multiscale Multifractal Analysis of Time Series Data
Description:

Multiscale multifractal analysis (MMA) (GieraÅ towski et al., 2012)<DOI:10.1103/PhysRevE.85.021915> is a time series analysis method, designed to describe scaling properties of fluctuations within the signal analyzed. The main result of this procedure is the so called Hurst surface h(q,s) , which is a dependence of the local Hurst exponent h (fluctuation scaling exponent) on the multifractal parameter q and the scale of observation s (data window width).

r-tuckerr-mmgg 1.5.1
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://github.com/gusart/tuckerR_mmgg
Licenses: GPL 3
Build system: r
Synopsis: Three-Mode Principal Components Analysis
Description:

This package performs Three-Mode Principal Components Analysis, which carries out Tucker Models.

r-trialemulation 0.0.4.9
Propagated dependencies: r-sandwich@3.1-1 r-rcpp@1.1.0 r-mvtnorm@1.3-3 r-lmtest@0.9-40 r-lifecycle@1.0.4 r-formula-tools@1.7.1 r-duckdb@1.4.2 r-dbi@1.2.3 r-data-table@1.17.8 r-checkmate@2.3.3 r-broom@1.0.10
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://causal-lda.github.io/TrialEmulation/
Licenses: FSDG-compatible
Build system: r
Synopsis: Causal Analysis of Observational Time-to-Event Data
Description:

This package implements target trial emulation methods to apply randomized clinical trial design and analysis in an observational setting. Using marginal structural models, it can estimate intention-to-treat and per-protocol effects in emulated trials using electronic health records. A description and application of the method can be found in Danaei et al (2013) <doi:10.1177/0962280211403603>.

r-tippy 0.1.0
Propagated dependencies: r-shiny@1.11.1 r-jsonlite@2.0.0 r-htmlwidgets@1.6.4 r-htmltools@0.5.8.1
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://tippy.john-coene.com/
Licenses: Expat
Build system: r
Synopsis: Add Tooltips to 'R markdown' Documents or 'Shiny' Apps
Description:

Htmlwidget of Tippyjs to add tooltips to Shiny apps and R markdown documents.

r-triact 0.3.1
Propagated dependencies: r-r6@2.6.1 r-lubridate@1.9.4 r-data-table@1.17.8 r-checkmate@2.3.3
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://github.com/agroscope-ch/triact
Licenses: GPL 3+
Build system: r
Synopsis: Analyzing the Lying Behavior of Cows from Accelerometer Data
Description:

Assists in analyzing the lying behavior of cows from raw data recorded with a triaxial accelerometer attached to the hind leg of a cow. Allows the determination of common measures for lying behavior including total lying duration, the number of lying bouts, and the mean duration of lying bouts. Further capabilities are the description of lying laterality and the calculation of proxies for the level of physical activity of the cow. Reference: Simmler M., Brouwers S. P. (2024) <doi:10.7717/peerj.17036>.

r-txshift 0.3.8
Propagated dependencies: r-stringr@1.6.0 r-scales@1.4.0 r-rdpack@2.6.4 r-mvtnorm@1.3-3 r-lspline@1.0-0 r-latex2exp@0.9.6 r-haldensify@0.2.8 r-hal9001@0.4.6 r-ggplot2@4.0.1 r-data-table@1.17.8 r-assertthat@0.2.1
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://github.com/nhejazi/txshift
Licenses: Expat
Build system: r
Synopsis: Efficient Estimation of the Causal Effects of Stochastic Interventions
Description:

Efficient estimation of the population-level causal effects of stochastic interventions on a continuous-valued exposure. Both one-step and targeted minimum loss estimators are implemented for the counterfactual mean value of an outcome of interest under an additive modified treatment policy, a stochastic intervention that may depend on the natural value of the exposure. To accommodate settings with outcome-dependent two-phase sampling, procedures incorporating inverse probability of censoring weighting are provided to facilitate the construction of inefficient and efficient one-step and targeted minimum loss estimators. The causal parameter and its estimation were first described by DÃ az and van der Laan (2013) <doi:10.1111/j.1541-0420.2011.01685.x>, while the multiply robust estimation procedure and its application to data from two-phase sampling designs is detailed in NS Hejazi, MJ van der Laan, HE Janes, PB Gilbert, and DC Benkeser (2020) <doi:10.1111/biom.13375>. The software package implementation is described in NS Hejazi and DC Benkeser (2020) <doi:10.21105/joss.02447>. Estimation of nuisance parameters may be enhanced through the Super Learner ensemble model in sl3', available for download from GitHub using remotes::install_github("tlverse/sl3")'.

r-tsrepr 1.1.0
Propagated dependencies: r-wavelets@0.3-0.2 r-rcpp@1.1.0 r-quantreg@6.1 r-mgcv@1.9-4 r-mass@7.3-65 r-dtt@0.1-2.1
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://petolau.github.io/package/
Licenses: GPL 3 FSDG-compatible
Build system: r
Synopsis: Time Series Representations
Description:

This package provides methods for representations (i.e. dimensionality reduction, preprocessing, feature extraction) of time series to help more accurate and effective time series data mining. Non-data adaptive, data adaptive, model-based and data dictated (clipped) representation methods are implemented. Also various normalisation methods (min-max, z-score, Box-Cox, Yeo-Johnson), and forecasting accuracy measures are implemented.

r-tssvm 0.1.0
Propagated dependencies: r-forecast@8.24.0 r-e1071@1.7-16
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://cran.r-project.org/package=TSSVM
Licenses: GPL 3
Build system: r
Synopsis: Time Series Forecasting using SVM Model
Description:

Implementation and forecasting univariate time series data using the Support Vector Machine model. Support Vector Machine is one of the prominent machine learning approach for non-linear time series forecasting. For method details see Kim, K. (2003) <doi:10.1016/S0925-2312(03)00372-2>.

r-tushare 0.1.4
Propagated dependencies: r-tidyverse@2.0.0 r-httr@1.4.7 r-forecast@8.24.0 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://cran.r-project.org/package=Tushare
Licenses: GPL 2
Build system: r
Synopsis: Interface to 'Tushare Pro' API
Description:

Helps the R users to get data from Tushare Pro'<https://tushare.pro>. Tushare Pro is a platform as well as a community with a lot of staffs working in financial area. We support financial data such as stock price, financial report statements and digital coins data.

r-tatoo 1.1.3
Propagated dependencies: r-withr@3.0.2 r-stringi@1.8.7 r-openxlsx@4.2.8.1 r-magrittr@2.0.4 r-data-table@1.17.8 r-crayon@1.5.3 r-colt@0.1.1 r-assertthat@0.2.1
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://github.com/statistikat/tatoo
Licenses: Expat
Build system: r
Synopsis: Combine and Export Data Frames
Description:

This package provides functions to combine data.frames in ways that require additional effort in base R, and to add metadata (id, title, ...) that can be used for printing and xlsx export. The Tatoo_report class is provided as a convenient helper to write several such tables to a workbook, one table per worksheet. Tatoo is built on top of openxlsx', but intimate knowledge of that package is not required to use tatoo.

r-toxcrit 1.0
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://cran.r-project.org/package=ToxCrit
Licenses: GPL 3
Build system: r
Synopsis: Calculates Safety Stopping Boundaries for a Single-Arm Trial using Bayes
Description:

Computation of stopping boundaries for a single-arm trial using a Bayesian criterion; i.e., for each m<=n (n= total patient number of the trial) the smallest number of observed toxicities is calculated leading to the termination of the trial/accrual according to the specified criteria. The probabilities of stopping the trial/accrual at and up until (resp.) the m-th patient (m<=n) is also calculated. This design is more conservative than the frequentist approach (using Clopper Pearson CIs) which might be preferred as it concerns safety.See also Aamot et.al.(2010) "Continuous monitoring of toxicity in clinical trials - simulating the risk of stopping prematurely" <doi:10.5414/cpp48476>.

r-tsutils 0.9.4
Propagated dependencies: r-rcolorbrewer@1.1-3 r-plotrix@3.8-13 r-mapa@2.0.7 r-forecast@8.24.0
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://github.com/trnnick/tsutils/
Licenses: GPL 3
Build system: r
Synopsis: Time Series Exploration, Modelling and Forecasting
Description:

Includes: (i) tests and visualisations that can help the modeller explore time series components and perform decomposition; (ii) modelling shortcuts, such as functions to construct lagmatrices and seasonal dummy variables of various forms; (iii) an implementation of the Theta method; (iv) tools to facilitate the design of the forecasting process, such as ABC-XYZ analyses; and (v) "quality of life" functions, such as treating time series for trailing and leading values.

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