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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-ibelief 1.3.1
Channel: guix-cran
Location: guix-cran/packages/i.scm (guix-cran packages i)
Home page: https://cran.r-project.org/package=ibelief
Licenses: GPL 2+
Build system: r
Synopsis: Belief Function Implementation
Description:

Some basic functions to implement belief functions including: transformation between belief functions using the method introduced by Philippe Smets <arXiv:1304.1122>, evidence combination, evidence discounting, decision-making, and constructing masses. Currently, thirteen combination rules and six decision rules are supported. It can also be used to generate different types of random masses when working on belief combination and conflict management.

r-itensor 1.0.2
Propagated dependencies: r-rtensor@1.4.9 r-mixomics@6.34.0 r-mgcv@1.9-4 r-mass@7.3-65 r-jointdiag@0.4 r-groupica@0.1.1 r-geigen@2.3 r-einsum@0.1.2
Channel: guix-cran
Location: guix-cran/packages/i.scm (guix-cran packages i)
Home page: https://github.com/rikenbit/iTensor
Licenses: Expat
Build system: r
Synopsis: ICA-Based Matrix/Tensor Decomposition
Description:

Some functions for performing ICA, MICA, Group ICA, and Multilinear ICA are implemented. ICA, MICA/Group ICA, and Multilinear ICA extract statistically independent components from single matrix, multiple matrices, and single tensor, respectively. For the details of these methods, see the reference section of GitHub README.md <https://github.com/rikenbit/iTensor>.

r-ipadmixture 0.1.2
Propagated dependencies: r-treemap@2.4-4 r-ape@5.8-1
Channel: guix-cran
Location: guix-cran/packages/i.scm (guix-cran packages i)
Home page: https://github.com/DarkEyes/ipADMIXTURE
Licenses: GPL 3
Build system: r
Synopsis: Iterative Pruning Population Admixture Inference Framework
Description:

This package provides a data clustering package based on admixture ratios (Q matrix) of population structure. The framework is based on iterative Pruning procedure that performs data clustering by splitting a given population into subclusters until meeting the condition of stopping criteria the same as ipPCA, iNJclust, and IPCAPS frameworks. The package also provides a function to retrieve phylogeny tree that construct a neighbor-joining tree based on a similar matrix between clusters. By given multiple Q matrices with varying a number of ancestors (K), the framework define a similar value between clusters i,j as a minimum number K* that makes majority of members of two clusters are in the different clusters. This K* reflexes a minimum number of ancestors we need to splitting cluster i,j into different clusters if we assign K* clusters based on maximum admixture ratio of individuals. The publication of this package is at Chainarong Amornbunchornvej, Pongsakorn Wangkumhang, and Sissades Tongsima (2020) <doi:10.1101/2020.03.21.001206>.

r-index0 0.0.1
Channel: guix-cran
Location: guix-cran/packages/i.scm (guix-cran packages i)
Home page: https://cran.r-project.org/package=index0
Licenses: Expat
Build system: r
Synopsis: Zero-Based Indexing in R
Description:

Extract and replace elements using indices that start from zero (rather than one), as is common in mathematical notation and other programming languages.

r-inverseregex 0.2.0
Channel: guix-cran
Location: guix-cran/packages/i.scm (guix-cran packages i)
Home page: https://cran.r-project.org/package=inverseRegex
Licenses: Expat
Build system: r
Synopsis: Reverse Engineers Regular Expression Patterns for R Objects
Description:

Reverse engineer a regular expression pattern for the characters contained in an R object. Individual characters can be categorised into digits, letters, punctuation or spaces and encoded into run-lengths. This can be used to summarise the structure of a dataset or identify non-standard entries. Many non-character inputs such as numeric vectors and data frames are supported.

r-icmm 1.2
Propagated dependencies: r-ebayesthresh@1.4-12
Channel: guix-cran
Location: guix-cran/packages/i.scm (guix-cran packages i)
Home page: https://www.researchgate.net/publication/279279744_Selecting_massive_variables_using_an_iterated_conditional_modesmedians_algorithm
Licenses: GPL 2+
Build system: r
Synopsis: Empirical Bayes Variable Selection via ICM/M Algorithm
Description:

Empirical Bayes variable selection via ICM/M algorithm for normal, binary logistic, and Cox's regression. The basic problem is to fit high-dimensional regression which sparse coefficients. This package allows incorporating the Ising prior to capture structure of predictors in the modeling process. More information can be found in the papers listed in the URL below.

r-iatanalytics 0.2.0
Channel: guix-cran
Location: guix-cran/packages/i.scm (guix-cran packages i)
Home page: https://cran.r-project.org/package=IATanalytics
Licenses: Expat
Build system: r
Synopsis: Compute Effect Sizes and Reliability for Implicit Association Test (IAT) Data
Description:

Quickly score raw data outputted from an Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998) <doi:10.1037/0022-3514.74.6.1464>. IAT scores are calculated as specified by Greenwald, Nosek, and Banaji (2003) <doi:10.1037/0022-3514.85.2.197>. The output of this function is a data frame that consists of four rows containing the following information: (1) the overall IAT effect size for the participant's dataset, (2) the effect size calculated for odd trials only, (3) the effect size calculated for even trials only, and (4) the proportion of trials with reaction times under 300ms (which is important for exclusion purposes). Items (2) and (3) allow for a measure of the internal consistency of the IAT. Specifically, you can use the subsetted IAT effect sizes for odd and even trials to calculate Cronbach's alpha across participants in the sample. The input function consists of three arguments. First, indicate the name of the dataset to be analyzed. This is the only required input. Second, indicate the number of trials in your entire IAT (the default is set to 220, which is typical for most IATs). Last, indicate whether congruent trials (e.g., flowers and pleasant) or incongruent trials (e.g., guns and pleasant) were presented first for this participant (the default is set to congruent). Data files should consist of six columns organized in order as follows: Block (0-6), trial (0-19 for training blocks, 0-39 for test blocks), category (dependent on your IAT), the type of item within that category (dependent on your IAT), a dummy variable indicating whether the participant was correct or incorrect on that trial (0=correct, 1=incorrect), and the participantâ s reaction time (in milliseconds). A sample dataset (titled sampledata') is included in this package to practice with.

r-inet 0.1.0
Propagated dependencies: r-mass@7.3-65 r-hdi@0.1-10 r-glmnet@4.1-10
Channel: guix-cran
Location: guix-cran/packages/i.scm (guix-cran packages i)
Home page: https://cran.r-project.org/package=inet
Licenses: GPL 2+
Build system: r
Synopsis: Performing Inference on Networks with Regularization
Description:

This package performs inference with the lasso in Gaussian Graphical Models. The package consists of wrappers for functions from the hdi package.

r-isingsampler 0.2.4
Propagated dependencies: r-rcpp@1.1.0 r-plyr@1.8.9 r-nnet@7.3-20 r-magrittr@2.0.4 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/i.scm (guix-cran packages i)
Home page: https://github.com/SachaEpskamp/IsingSampler
Licenses: GPL 2
Build system: r
Synopsis: Sampling Methods and Distribution Functions for the Ising Model
Description:

Sample states from the Ising model and compute the probability of states. Sampling can be done for any number of nodes, but due to the intractibility of the Ising model the distribution can only be computed up to ~10 nodes.

r-imputemulti 0.8.4
Propagated dependencies: r-rcpp@1.1.0 r-gtools@3.9.5 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/i.scm (guix-cran packages i)
Home page: https://cran.r-project.org/package=imputeMulti
Licenses: GPL 3
Build system: r
Synopsis: Imputation Methods for Multivariate Multinomial Data
Description:

This package implements imputation methods using EM and Data Augmentation for multinomial data following the work of Schafer 1997 <ISBN: 978-0-412-04061-0>.

r-inext-4steps 1.0.1
Propagated dependencies: r-reshape2@1.4.5 r-purrr@1.2.0 r-inext-3d@1.0.12 r-ggpubr@0.6.2 r-ggplot2@4.0.1 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/i.scm (guix-cran packages i)
Home page: https://sites.google.com/view/chao-lab-website/software/inext-4steps/
Licenses: GPL 3+
Build system: r
Synopsis: Four-Step Biodiversity Analysis Based on 'iNEXT'
Description:

Expands iNEXT to include the estimation of sample completeness and evenness. The package provides simple functions to perform the following four-step biodiversity analysis: STEP 1: Assessment of sample completeness profiles. STEP 2a: Analysis of size-based rarefaction and extrapolation sampling curves to determine whether the asymptotic diversity can be accurately estimated. STEP 2b: Comparison of the observed and the estimated asymptotic diversity profiles. STEP 3: Analysis of non-asymptotic coverage-based rarefaction and extrapolation sampling curves. STEP 4: Assessment of evenness profiles. The analyses in STEPs 2a, 2b and STEP 3 are mainly based on the previous iNEXT package. Refer to the iNEXT package for details. This package is mainly focusing on the computation for STEPs 1 and 4. See Chao et al. (2020) <doi:10.1111/1440-1703.12102> for statistical background.

r-iglm 1.1
Propagated dependencies: r-rcppprogress@0.4.2 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-r6@2.6.1 r-matrix@1.7-4 r-mass@7.3-65 r-igraph@2.2.1 r-coda@0.19-4.1
Channel: guix-cran
Location: guix-cran/packages/i.scm (guix-cran packages i)
Home page: https://cran.r-project.org/package=iglm
Licenses: GPL 3
Build system: r
Synopsis: Regression under Network Interference
Description:

An implementation of generalized linear models (GLMs) for studying relationships among attributes in connected populations, where responses of connected units can be dependent, as introduced by Fritz et al. (2025) <doi:10.1080/01621459.2025.2565851>. igml extends GLMs for independent responses to dependent responses and can be used for studying spillover in connected populations and other network-mediated phenomena.

r-ibb 0.0.2
Propagated dependencies: r-tidyselect@1.2.1 r-tibble@3.3.0 r-rlang@1.1.6 r-magrittr@2.0.4 r-jsonlite@2.0.0 r-httr@1.4.7 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/i.scm (guix-cran packages i)
Home page: https://github.com/berkorbay/ibb
Licenses: Expat
Build system: r
Synopsis: R Wrapper for Istanbul Municipality Open Data Portal
Description:

Call wrappers for Istanbul Metropolitan Municipality's Open Data Portal (Turkish: İstanbul BüyükŠehir Belediyesi Açık Veri Portalı) at <https://data.ibb.gov.tr/en/>.

r-image2data 1.0.1
Propagated dependencies: r-readbitmap@0.1.5
Channel: guix-cran
Location: guix-cran/packages/i.scm (guix-cran packages i)
Home page: https://cran.r-project.org/package=image2data
Licenses: Expat
Build system: r
Synopsis: Turn Images into Data Sets
Description:

The goal of image2data is to extract images and return them into a data set, especially for teaching data manipulation and data visualization. Basically, the eponymous function takes an image file ('png', tiff', jpeg', bmp') and turn it into a data set, pixels being rows (subjects) and columns (variables) being their coordinate positions (x- and y-axis) and their respective color (in hex codes). The function can return a complete image or a range of color (i.e., contour, silhouette). The data can then be manipulated as would any data set by either creating other related variables (to hide the image) or as a genuine toy data set.

r-itos 1.0.3
Propagated dependencies: r-xtable@1.8-4 r-rcbalance@1.8.8 r-mass@7.3-65 r-biasedurn@2.0.12
Channel: guix-cran
Location: guix-cran/packages/i.scm (guix-cran packages i)
Home page: https://cran.r-project.org/package=iTOS
Licenses: GPL 2
Build system: r
Synopsis: Methods and Examples from Introduction to the Theory of Observational Studies
Description:

Supplements for a book, "iTOS" = "Introduction to the Theory of Observational Studies." Data sets are aHDL from Rosenbaum (2023a) <doi:10.1111/biom.13558> and bingeM from Rosenbaum (2023b) <doi:10.1111/biom.13921>. The function makematch() uses two-criteria matching from Zhang et al. (2023) <doi:10.1080/01621459.2021.1981337> to create the matched data bingeM from binge'. The makematch() function also implements optimal matching (Rosenbaum (1989) <doi:10.2307/2290079>) and matching with fine or near-fine balance (Rosenbaum et al. (2007) <doi:10.1198/016214506000001059> and Yang et al (2012) <doi:10.1111/j.1541-0420.2011.01691.x>). The book makes use of two other R packages, weightedRank and tightenBlock'.

r-itol-toolkit 1.1.12
Propagated dependencies: r-wesanderson@0.3.7 r-tidyr@1.3.1 r-stringr@1.6.0 r-stringdist@0.9.15 r-shiny@1.11.1 r-seqinr@4.2-36 r-rstudioapi@0.17.1 r-rcolorbrewer@1.1-3 r-purrr@1.2.0 r-miniui@0.1.2 r-magrittr@2.0.4 r-ggsci@4.1.0 r-dplyr@1.1.4 r-data-table@1.17.8 r-colourpicker@1.3.0 r-ape@5.8-1
Channel: guix-cran
Location: guix-cran/packages/i.scm (guix-cran packages i)
Home page: https://tongzhou2017.github.io/itol.toolkit/
Licenses: Expat
Build system: r
Synopsis: Helper Functions for 'Interactive Tree Of Life'
Description:

The Interactive Tree Of Life <https://itol.embl.de/> online server can edit and annotate trees interactively. The itol.toolkit package can support all types of annotation templates.

r-icesconnect 1.1.4
Propagated dependencies: r-jsonlite@2.0.0 r-httr@1.4.7 r-base64enc@0.1-3 r-askpass@1.2.1
Channel: guix-cran
Location: guix-cran/packages/i.scm (guix-cran packages i)
Home page: https://www.ices.dk/data/tools/Pages/WebServices.aspx
Licenses: GPL 3
Build system: r
Synopsis: Provides User Tokens for Access to ICES Web Services
Description:

This package provides user tokens for ICES web services that require authentication and authorization. Web services covered by this package are ICES VMS database, the ICES DATSU web services, and the ICES SharePoint site <https://www.ices.dk/data/tools/Pages/WebServices.aspx>.

r-ic10trainingdata 2.0.1
Channel: guix-cran
Location: guix-cran/packages/i.scm (guix-cran packages i)
Home page: https://cran.r-project.org/package=iC10TrainingData
Licenses: GPL 3
Build system: r
Synopsis: Training Datasets for iC10 Package
Description:

Training datasets for iC10; which implements the classifier described in the paper Genome-driven integrated classification of breast cancer validated in over 7,500 samples (Ali HR et al., Genome Biology 2014). It uses copy number and/or expression form breast cancer data, trains a pamr classifier (Tibshirani et al.) with the features available and predicts the iC10 group. Genomic annotation for the training dataset has been obtained from Mark Dunning's lluminaHumanv3.db package.

r-interface 0.1.2
Channel: guix-cran
Location: guix-cran/packages/i.scm (guix-cran packages i)
Home page: https://github.com/dereckmezquita/interface
Licenses: Expat
Build system: r
Synopsis: Runtime Type System
Description:

This package provides a runtime type system, allowing users to define and implement interfaces, enums, typed data.frame/data.table, as well as typed functions. This package enables stricter type checking and validation, improving code structure, robustness and reliability.

r-icompelm 0.1.0
Propagated dependencies: r-tsutils@0.9.4 r-ica@1.0-3
Channel: guix-cran
Location: guix-cran/packages/i.scm (guix-cran packages i)
Home page: https://cran.r-project.org/package=ICompELM
Licenses: GPL 3
Build system: r
Synopsis: Independent Component Analysis Based Extreme Learning Machine
Description:

Single Layer Feed-forward Neural networks (SLFNs) have many applications in various fields of statistical modelling, especially for time-series forecasting. However, there are some major disadvantages of training such networks via the widely accepted gradient-based backpropagation algorithm, such as convergence to local minima, dependencies on learning rate and large training time. These concerns were addressed by Huang et al. (2006) <doi:10.1016/j.neucom.2005.12.126>, wherein they introduced the Extreme Learning Machine (ELM), an extremely fast learning algorithm for SLFNs which randomly chooses the weights connecting input and hidden nodes and analytically determines the output weights of SLFNs. It shows good generalized performance, but is still subject to a high degree of randomness. To mitigate this issue, this package uses a dimensionality reduction technique given in Hyvarinen (1999) <doi:10.1109/72.761722>, namely, the Independent Component Analysis (ICA) to determine the input-hidden connections and thus, remove any sort of randomness from the algorithm. This leads to a robust, fast and stable ELM model. Using functions within this package, the proposed model can also be compared with an existing alternative based on the Principal Component Analysis (PCA) algorithm given by Pearson (1901) <doi:10.1080/14786440109462720>, i.e., the PCA based ELM model given by Castano et al. (2013) <doi:10.1007/s11063-012-9253-x>, from which the implemented ICA based algorithm is greatly inspired.

r-iblm 1.0.2
Propagated dependencies: r-xgboost@1.7.11.1 r-withr@3.0.2 r-statmod@1.5.1 r-scales@1.4.0 r-purrr@1.2.0 r-ggplot2@4.0.1 r-ggextra@0.11.0 r-fastdummies@1.7.5 r-dplyr@1.1.4 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/i.scm (guix-cran packages i)
Home page: https://ifoa-adswp.github.io/IBLM/
Licenses: Expat
Build system: r
Synopsis: Interpretable Boosted Linear Models
Description:

This package implements Interpretable Boosted Linear Models (IBLMs). These combine a conventional generalized linear model (GLM) with a machine learning component, such as XGBoost. The package also provides tools within for explaining and analyzing these models. For more details see Gawlowski and Wang (2025) <https://ifoa-adswp.github.io/IBLM/reference/figures/iblm_paper.pdf>.

r-ipumsr 0.9.0
Propagated dependencies: r-zeallot@0.2.0 r-xml2@1.5.0 r-tidyselect@1.2.1 r-tibble@3.3.0 r-rlang@1.1.6 r-readr@2.1.6 r-r6@2.6.1 r-purrr@1.2.0 r-lifecycle@1.0.4 r-jsonlite@2.0.0 r-httr@1.4.7 r-hipread@0.2.5 r-haven@2.5.5 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/i.scm (guix-cran packages i)
Home page: https://tech.popdata.org/ipumsr/
Licenses: FSDG-compatible
Build system: r
Synopsis: An R Interface for Downloading, Reading, and Handling IPUMS Data
Description:

An easy way to work with census, survey, and geographic data provided by IPUMS in R. Generate and download data through the IPUMS API and load IPUMS files into R with their associated metadata to make analysis easier. IPUMS data describing 1.4 billion individuals drawn from over 750 censuses and surveys is available free of charge from the IPUMS website <https://www.ipums.org>.

r-iai 1.10.2
Propagated dependencies: r-stringr@1.6.0 r-rlang@1.1.6 r-rjson@0.2.23 r-rappdirs@0.3.3 r-lifecycle@1.0.4 r-juliacall@0.17.6 r-ggplot2@4.0.1 r-cowplot@1.2.0
Channel: guix-cran
Location: guix-cran/packages/i.scm (guix-cran packages i)
Home page: https://www.interpretable.ai
Licenses: Expat
Build system: r
Synopsis: Interface to 'Interpretable AI' Modules
Description:

An interface to the algorithms of Interpretable AI <https://www.interpretable.ai> from the R programming language. Interpretable AI provides various modules, including Optimal Trees for classification, regression, prescription and survival analysis, Optimal Imputation for missing data imputation and outlier detection, and Optimal Feature Selection for exact sparse regression. The iai package is an open-source project. The Interpretable AI software modules are proprietary products, but free academic and evaluation licenses are available.

r-imix 1.1.5
Propagated dependencies: r-mvtnorm@1.3-3 r-mixtools@2.0.0.1 r-mclust@6.1.2 r-mass@7.3-65 r-ggplot2@4.0.1 r-crayon@1.5.3
Channel: guix-cran
Location: guix-cran/packages/i.scm (guix-cran packages i)
Home page: https://github.com/ziqiaow/IMIX
Licenses: GPL 2
Build system: r
Synopsis: Gaussian Mixture Model for Multi-Omics Data Integration
Description:

This package provides a multivariate Gaussian mixture model framework to integrate multiple types of genomic data and allow modeling of inter-data-type correlations for association analysis. IMIX can be implemented to test whether a disease is associated with genes in multiple genomic data types, such as DNA methylation, copy number variation, gene expression, etc. It can also study the integration of multiple pathways. IMIX uses the summary statistics of association test outputs and conduct integration analysis for two or three types of genomics data. IMIX features statistically-principled model selection, global FDR control and computational efficiency. Details are described in Ziqiao Wang and Peng Wei (2020) <doi:10.1093/bioinformatics/btaa1001>.

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