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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-tf 0.3.4
Propagated dependencies: r-zoo@1.8-14 r-vctrs@0.6.5 r-rlang@1.1.6 r-purrr@1.2.0 r-pracma@2.4.6 r-mvtnorm@1.3-3 r-mgcv@1.9-4 r-checkmate@2.3.3
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://tidyfun.github.io/tf/
Licenses: AGPL 3+
Synopsis: S3 Classes and Methods for Tidy Functional Data
Description:

Defines S3 vector data types for vectors of functional data (grid-based, spline-based or functional principal components-based) with all arithmetic and summary methods, derivation, integration and smoothing, plotting, data import and export, and data wrangling, such as re-evaluating, subsetting, sub-assigning, zooming into sub-domains, or extracting functional features like minima/maxima and their locations. The implementation allows including such vectors in data frames for joint analysis of functional and scalar variables.

r-tdiagree 0.2.0
Propagated dependencies: r-plotfunctions@1.4 r-nlme@3.1-168 r-multcomp@1.4-29 r-katex@1.5.0 r-gt@1.2.0 r-furniture@1.9.14 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=TDIagree
Licenses: GPL 2+
Synopsis: Assessment of Agreement using the Total Deviation Index
Description:

The total deviation index (TDI) is an unscaled statistical measure used to evaluate the deviation between paired quantitative measurements when assessing the extent of agreement between different raters. It describes a boundary such that a large specified proportion of the differences in paired measurements are within the boundary (Lin, 2000) <https://pubmed.ncbi.nlm.nih.gov/10641028/>. This R package implements some methodologies existing in the literature for TDI estimation and inference in the case of two raters.

r-tsqn 1.0.0
Propagated dependencies: r-robustbase@0.99-6 r-mass@7.3-65 r-fracdiff@1.5-3
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://cran.r-project.org/package=tsqn
Licenses: GPL 2+
Synopsis: Applications of the Qn Estimator to Time Series (Univariate and Multivariate)
Description:

Time Series Qn is a package with applications of the Qn estimator of Rousseeuw and Croux (1993) <doi:10.1080/01621459.1993.10476408> to univariate and multivariate Time Series in time and frequency domains. More specifically, the robust estimation of autocorrelation or autocovariance matrix functions from Ma and Genton (2000, 2001) <doi:10.1111/1467-9892.00203>, <doi:10.1006/jmva.2000.1942> and Cotta (2017) <doi:10.13140/RG.2.2.14092.10883> are provided. The robust pseudo-periodogram of Molinares et. al. (2009) <doi:10.1016/j.jspi.2008.12.014> is also given. This packages also provides the M-estimator of the long-memory parameter d based on the robustification of the GPH estimator proposed by Reisen et al. (2017) <doi:10.1016/j.jspi.2017.02.008>.

r-tldrdocs 0.0.1
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://cran.r-project.org/package=tldrDocs
Licenses: Expat
Synopsis: Provide 'tldrPages' Documentation for Base R Functions
Description:

Documentation for commonly-used objects included in the base distribution of R. Note that tldrDocs does not export any functions itself, its purpose is to write .Rd files during its installation for tldr() to find.

r-tidyquery 0.2.4
Propagated dependencies: r-stringr@1.6.0 r-rlang@1.1.6 r-queryparser@0.3.2 r-lubridate@1.9.4 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://github.com/ianmcook/tidyquery
Licenses: ASL 2.0
Synopsis: Query 'R' Data Frames with 'SQL'
Description:

Use SQL SELECT statements to query R data frames.

r-tidytlg 0.11.0
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-stringr@1.6.0 r-rstudioapi@0.17.1 r-rlang@1.1.6 r-readxl@1.4.5 r-purrr@1.2.0 r-png@0.1-8 r-magrittr@2.0.4 r-huxtable@5.8.0 r-glue@1.8.0 r-ggplot2@4.0.1 r-forcats@1.0.1 r-dplyr@1.1.4 r-crayon@1.5.3 r-cli@3.6.5 r-cellranger@1.1.0 r-assertthat@0.2.1
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://pharmaverse.github.io/tidytlg/main/
Licenses: ASL 2.0
Synopsis: Create TLGs using the 'tidyverse'
Description:

Generate tables, listings, and graphs (TLG) using tidyverse'. Tables can be created functionally, using a standard TLG process, or by specifying table and column metadata to create generic analysis summaries. The envsetup package can also be leveraged to create environments for table creation.

r-tergmlite 2.6.1
Propagated dependencies: r-tibble@3.3.0 r-tergm@4.2.2 r-statnet-common@4.12.0 r-rcpp@1.1.0 r-networkdynamic@0.11.5 r-network@1.19.0 r-ergm@4.10.1
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://cran.r-project.org/package=tergmLite
Licenses: GPL 3
Synopsis: Fast Simulation of Simple Temporal Exponential Random Graph Models
Description:

This package provides functions for the computationally efficient simulation of dynamic networks estimated with the statistical framework of temporal exponential random graph models, implemented in the tergm package.

r-tsapp 1.0.4
Propagated dependencies: r-vars@1.6-1 r-matrix@1.7-4 r-hdm@0.3.2 r-fftwtools@0.9-11
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://cran.r-project.org/package=tsapp
Licenses: GPL 2+ GPL 3+
Synopsis: Time Series, Analysis and Application
Description:

Accompanies the book Rainer Schlittgen and Cristina Sattarhoff (2020) <https://www.degruyter.com/view/title/575978> "Angewandte Zeitreihenanalyse mit R, 4. Auflage" . The package contains the time series and functions used therein. It was developed over many years teaching courses about time series analysis.

r-thetasvm 0.1.0
Propagated dependencies: r-tssvm@0.1.0 r-tseries@0.10-58 r-forecast@8.24.0
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://cran.r-project.org/package=THETASVM
Licenses: GPL 3
Synopsis: Time Series Forecasting using THETA-SVM Hybrid Model
Description:

Testing, Implementation, and Forecasting of the THETA-SVM hybrid model. The THETA-SVM hybrid model combines the distinct strengths of the THETA model and the Support Vector Machine (SVM) model for time series forecasting.For method details see Bhattacharyya et al. (2022) <doi:10.1007/s11071-021-07099-3>.

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
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-teachingsampling 4.1.1
Propagated dependencies: r-magrittr@2.0.4 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://cran.r-project.org/package=TeachingSampling
Licenses: GPL 2+
Synopsis: Selection of Samples and Parameter Estimation in Finite Population
Description:

Allows the user to draw probabilistic samples and make inferences from a finite population based on several sampling designs.

r-tensorbf 1.0.2
Propagated dependencies: r-tensor@1.5.1
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://cran.r-project.org/package=tensorBF
Licenses: Expat
Synopsis: Bayesian Tensor Factorization
Description:

Bayesian Tensor Factorization for decomposition of tensor data sets using the trilinear CANDECOMP/PARAFAC (CP) factorization, with automatic component selection. The complete data analysis pipeline is provided, including functions and recommendations for data normalization and model definition, as well as missing value prediction and model visualization. The method performs factorization for three-way tensor datasets and the inference is implemented with Gibbs sampling.

r-tesselle 1.6.0
Propagated dependencies: r-tabula@3.3.2 r-nexus@0.6.0 r-khroma@1.17.0 r-kairos@2.3.0 r-isopleuros@1.4.0 r-folio@1.5.1 r-dimensio@0.14.1
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://codeberg.org/tesselle/tesselle
Licenses: GPL 3+
Synopsis: Easily Install and Load 'tesselle' Packages
Description:

Easy install and load key packages from the tesselle suite in a single step. The tesselle suite is a collection of packages for research and teaching in archaeology. These packages focus on quantitative analysis methods developed for archaeology. The tesselle packages are designed to work seamlessly together and to complement general-purpose and other specialized statistical packages. These packages can be used to explore and analyze common data types in archaeology: count data, compositional data and chronological data. Learn more about tesselle at <https://www.tesselle.org>.

r-taper 0.5.3
Propagated dependencies: r-pracma@2.4.6 r-nlme@3.1-168
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://cran.r-project.org/package=TapeR
Licenses: GPL 2+
Synopsis: Flexible Tree Taper Curves Based on Semiparametric Mixed Models
Description:

Implementation of functions for fitting taper curves (a semiparametric linear mixed effects taper model) to diameter measurements along stems. Further functions are provided to estimate the uncertainty around the predicted curves, to calculate timber volume (also by sections) and marginal (e.g., upper) diameters. For cases where tree heights are not measured, methods for estimating additional variance in volume predictions resulting from uncertainties in tree height models (tariffs) are provided. The example data include the taper curve parameters for Norway spruce used in the 3rd German NFI fitted to 380 trees and a subset of section-wise diameter measurements of these trees. The functions implemented here are detailed in Kublin, E., Breidenbach, J., Kaendler, G. (2013) <doi:10.1007/s10342-013-0715-0>.

r-timechecker 1.1.5
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://cran.r-project.org/package=timechecker
Licenses: GPL 3
Synopsis: Visualization of Processing Time with Standard Output
Description:

Displays processing time in a clear and structured way. One function supports iterative workflows by predicting and showing the total time required, while another reports the time taken for individual steps within a process.

r-timevizpro 1.0.1
Propagated dependencies: r-sparkline@2.0 r-shiny@1.11.1 r-ggplot2@4.0.1 r-ggiraph@0.9.2 r-dplyr@1.1.4 r-countrycode@1.6.1
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://cran.r-project.org/package=TimeVizPro
Licenses: GPL 3
Synopsis: Dynamic Data Explorer: Visualize and Forecast with 'TimeVizPro'
Description:

Unleash the power of time-series data visualization with ease using our package. Designed with simplicity in mind, it offers three key features through the shiny package output. The first tab shows time- series charts with forecasts, allowing users to visualize trends and changes effortlessly. The second one displays Averages per country presented in tables with accompanying sparklines, providing a quick and attractive overview of the data. The last tab presents A customizable world map colored based on user-defined variables for any chosen number of countries, offering an advanced visual approach to understanding geographical data distributions. This package operates with just a few simple arguments, enabling users to conduct sophisticated analyses without the need for complex programming skills. Transform your time-series data analysis experience with our user-friendly tool.

r-tmle 2.1.1
Propagated dependencies: r-superlearner@2.0-29 r-glmnet@4.1-10
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://CRAN.R-project.org/package=tmle
Licenses: Modified BSD GPL 2
Synopsis: Targeted Maximum Likelihood Estimation
Description:

Targeted maximum likelihood estimation of point treatment effects (Targeted Maximum Likelihood Learning, The International Journal of Biostatistics, 2(1), 2006. This version automatically estimates the additive treatment effect among the treated (ATT) and among the controls (ATC). The tmle() function calculates the adjusted marginal difference in mean outcome associated with a binary point treatment, for continuous or binary outcomes. Relative risk and odds ratio estimates are also reported for binary outcomes. Missingness in the outcome is allowed, but not in treatment assignment or baseline covariate values. The population mean is calculated when there is missingness, and no variation in the treatment assignment. The tmleMSM() function estimates the parameters of a marginal structural model for a binary point treatment effect. Effect estimation stratified by a binary mediating variable is also available. An ID argument can be used to identify repeated measures. Default settings call SuperLearner to estimate the Q and g portions of the likelihood, unless values or a user-supplied regression function are passed in as arguments.

r-tpr 0.3-3
Propagated dependencies: r-lgtdl@1.1.5
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://cran.r-project.org/package=tpr
Licenses: GPL 3+
Synopsis: Temporal Process Regression
Description:

Regression models for temporal process responses with time-varying coefficient.

r-tidygeorss 0.0.1
Propagated dependencies: r-xml2@1.5.0 r-tidyrss@2.0.7 r-stringr@1.6.0 r-strex@2.0.1 r-sf@1.0-23 r-rlang@1.1.6 r-purrr@1.2.0 r-magrittr@2.0.4 r-jsonlite@2.0.0 r-httr@1.4.7 r-dplyr@1.1.4 r-anytime@0.3.12
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://github.com/RobertMyles/tidygeoRSS
Licenses: Expat
Synopsis: Tidy GeoRSS
Description:

In order to easily integrate geoRSS data into analysis, tidygeoRSS parses geo feeds and returns tidy simple features data frames.

r-tongfen 0.3.6
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-stringr@1.6.0 r-sf@1.0-23 r-rlang@1.1.6 r-readr@2.1.6 r-purrr@1.2.0 r-lifecycle@1.0.4 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://github.com/mountainMath/tongfen
Licenses: Expat
Synopsis: Make Data Based on Different Geographies Comparable
Description:

Several functions to allow comparisons of data across different geographies, in particular for Canadian census data from different censuses.

r-testcor 0.0.2.2
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://cran.r-project.org/package=TestCor
Licenses: GPL 2+
Synopsis: FWER and FDR Controlling Procedures for Multiple Correlation Tests
Description:

Different multiple testing procedures for correlation tests are implemented. These procedures were shown to theoretically control asymptotically the Family Wise Error Rate (Roux (2018) <https://tel.archives-ouvertes.fr/tel-01971574v1>) or the False Discovery Rate (Cai & Liu (2016) <doi:10.1080/01621459.2014.999157>). The package gather four test statistics used in correlation testing, four FWER procedures with either single step or stepdown versions, and four FDR procedures.

r-tkrplotr 0.1.7
Dependencies: tcl@8.6.12
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://cran.r-project.org/package=tkRplotR
Licenses: GPL 2+
Synopsis: Display Resizable Plots
Description:

Display a plot in a Tk canvas.

r-tripack 1.3-9.3
Channel: guix-cran
Location: guix-cran/packages/t.scm (guix-cran packages t)
Home page: https://cran.r-project.org/package=tripack
Licenses: FSDG-compatible
Synopsis: Triangulation of Irregularly Spaced Data
Description:

This package provides a constrained two-dimensional Delaunay triangulation package providing both triangulation and generation of voronoi mosaics of irregular spaced data. Please note that most of the functions are now also covered in package interp, which is a re-implementation from scratch under a free license based on a different triangulation algorithm.

r-targeted 0.7
Propagated dependencies: r-survival@3.8-3 r-rlang@1.1.6 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-r6@2.6.1 r-quadprog@1.5-8 r-progressr@0.18.0 r-mets@1.3.8 r-lava@1.8.2 r-future-apply@1.20.0 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://kkholst.github.io/targeted/
Licenses: ASL 2.0
Synopsis: Targeted Inference
Description:

Various methods for targeted and semiparametric inference including augmented inverse probability weighted (AIPW) estimators for missing data and causal inference (Bang and Robins (2005) <doi:10.1111/j.1541-0420.2005.00377.x>), variable importance and conditional average treatment effects (CATE) (van der Laan (2006) <doi:10.2202/1557-4679.1008>), estimators for risk differences and relative risks (Richardson et al. (2017) <doi:10.1080/01621459.2016.1192546>), assumption lean inference for generalized linear model parameters (Vansteelandt et al. (2022) <doi:10.1111/rssb.12504>).

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