_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
      \_\ \       /  \ \\ \ \   / / /     / /  \
      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
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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-ispyardswinprobabilitygsdesign 0.1.0
Propagated dependencies: r-dfba@0.1.0
Channel: guix-cran
Location: guix-cran/packages/i.scm (guix-cran packages i)
Home page: https://cran.r-project.org/package=ISPYARDSWinProbabilityGSDesign
Licenses: GPL 2
Synopsis: Trial Design in Respiratory Disease Based on the Win Probability
Description:

This package provides analysis results and trial simulation functions for the I-SPY Acute Respiratory Disease Syndrome trial based on composite ranked outcomes. The composite ranked outcome is a hierarchical outcome where trial participants are ranked first by 28 day mortality, then ventilator days, then by advanced respiratory support days. A Bayesian win probability approach is used for analysis. Trial design options include group sequential looks for safety, superiority, futility, and adjustment of randomization probabilities.

r-iatscores 0.2.8
Propagated dependencies: r-stringr@1.6.0 r-reshape2@1.4.5 r-qgraph@1.9.8 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/i.scm (guix-cran packages i)
Home page: https://cran.r-project.org/package=IATscores
Licenses: GPL 2
Synopsis: Implicit Association Test Scores Using Robust Statistics
Description:

Compute several variations of the Implicit Association Test (IAT) scores, including the D scores (Greenwald, Nosek, Banaji, 2003, <doi:10.1037/0022-3514.85.2.197>) and the new scores that were developed using robust statistics (Richetin, Costantini, Perugini, and Schonbrodt, 2015, <doi:10.1371/journal.pone.0129601>).

r-ipmbook 0.1.5
Propagated dependencies: r-abind@1.4-8
Channel: guix-cran
Location: guix-cran/packages/i.scm (guix-cran packages i)
Home page: https://www.vogelwarte.ch/de/projekte/publikationen/ipm/
Licenses: GPL 3+
Synopsis: Functions and Data for the Book 'Integrated Population Models'
Description:

This package provides functions and data sets to accompany the book Integrated Population Models: Theory and Ecological Applications with R and JAGS by Michael Schaub and Marc Kéry (ISBN: 9780128205648).

r-indelmiss 1.0.10
Propagated dependencies: r-rcpp@1.1.0 r-phangorn@2.12.1 r-numderiv@2016.8-1.1 r-ape@5.8-1
Channel: guix-cran
Location: guix-cran/packages/i.scm (guix-cran packages i)
Home page: https://cran.r-project.org/package=indelmiss
Licenses: GPL 2+
Synopsis: Insertion Deletion Analysis While Accounting for Possible Missing Data
Description:

Genome-wide gene insertion and deletion rates can be modelled in a maximum likelihood framework with the additional flexibility of modelling potential missing data using the models included within. These models simultaneously estimate insertion and deletion (indel) rates of gene families and proportions of "missing" data for (multiple) taxa of interest. The likelihood framework is utilized for parameter estimation. A phylogenetic tree of the taxa and gene presence/absence patterns (with data ordered by the tips of the tree) are required. See Dang et al. (2016) <doi:10.1534/genetics.116.191973> for more details.

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
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-isocubes 1.0.0
Propagated dependencies: r-colorfast@1.0.1
Channel: guix-cran
Location: guix-cran/packages/i.scm (guix-cran packages i)
Home page: https://github.com/coolbutuseless/isocubes
Licenses: Expat
Synopsis: Voxel Data Visualization with Isometric Cubes
Description:

This package provides a voxel is a representation of a value on a regular, three-dimensional grid; it is the 3D equivalent of a 2D pixel. Voxel data can be visualised with this package using fixed viewpoint isometric cubes for each data point. This package also provides sample voxel data and tools for transforming the data.

r-imagery 0.3.0
Propagated dependencies: r-viridis@0.6.5 r-terra@1.8-86 r-rlang@1.1.6 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/i.scm (guix-cran packages i)
Home page: https://cran.r-project.org/package=imageRy
Licenses: GPL 2+
Synopsis: Modify and Share Images
Description:

This package provides tools for manipulating, visualizing, and exporting raster images in R. Designed as an educational resource for students learning the basics of remote sensing, the package provides user-friendly functions to apply color ramps, export RGB composites, and create multi-frame visualizations. Built on top of the terra and ggplot2 packages. See <https://github.com/ducciorocchini/imageRy> for more details and examples.

r-ism 0.1.0
Propagated dependencies: r-xlsxjars@0.9.0 r-xlsx@0.6.5 r-rjava@1.0-11
Channel: guix-cran
Location: guix-cran/packages/i.scm (guix-cran packages i)
Home page: https://cran.r-project.org/package=ISM
Licenses: GPL 3
Synopsis: Interpretive Structural Modelling (ISM)
Description:

The development of ISM was made by Warfield in 1974. ISM is the process of collaborating distinct or related essentials into a simplified and an organized format. Hence, ISM is a methodology that seeks the interrelationships among the various elements considered and endows with a hierarchical and multilevel structure. To run this package user needs to provide a matrix (VAXO) converted into 0's and 1's. Warfield,J.N. (1974) <doi:10.1109/TSMC.1974.5408524> Warfield,J.N. (1974, E-ISSN:2168-2909).

r-imrmc 2.1.0
Propagated dependencies: r-matrix@1.7-4
Channel: guix-cran
Location: guix-cran/packages/i.scm (guix-cran packages i)
Home page: https://github.com/DIDSR/iMRMC
Licenses: CC0
Synopsis: Multi-Reader, Multi-Case Analysis Methods (ROC, Agreement, and Other Metrics)
Description:

This software does Multi-Reader, Multi-Case (MRMC) analyses of data from imaging studies where clinicians (readers) evaluate patient images (cases). What does this mean? ... Many imaging studies are designed so that every reader reads every case in all modalities, a fully-crossed study. In this case, the data is cross-correlated, and we consider the readers and cases to be cross-correlated random effects. An MRMC analysis accounts for the variability and correlations from the readers and cases when estimating variances, confidence intervals, and p-values. The functions in this package can treat arbitrary study designs and studies with missing data, not just fully-crossed study designs. An overview of this software, including references presenting details on the methods, can be found here: <https://www.fda.gov/medical-devices/science-and-research-medical-devices/imrmc-software-do-multi-reader-multi-case-statistical-analysis-reader-studies>.

r-ippp 1.1
Channel: guix-cran
Location: guix-cran/packages/i.scm (guix-cran packages i)
Home page: https://cran.r-project.org/package=IPPP
Licenses: FSDG-compatible
Synopsis: Inhomogeneous Poisson Point Processes
Description:

Generates random numbers corresponding to the events on a Poisson point process with changing event rates. This includes the possibility to incorporate additional information such as the number of events occurring or the location of an already known event. It can also generate the probability density functions of specific events in the cases where additional information is available. Based on Hohmann (2019) <arXiv:1901.10754>.

r-incubate 1.3.0
Propagated dependencies: r-tibble@3.3.0 r-survival@3.8-3 r-rlang@1.1.6 r-purrr@1.2.0 r-mass@7.3-65 r-glue@1.8.0 r-future-apply@1.20.0 r-future@1.68.0
Channel: guix-cran
Location: guix-cran/packages/i.scm (guix-cran packages i)
Home page: https://gitlab.com/imb-dev/incubate/
Licenses: LGPL 3+
Synopsis: Parametric Time-to-Event Analysis with Variable Incubation Phases
Description:

Fit parametric models for time-to-event data that show an initial incubation period', i.e., a variable delay phase where the hazard is zero. The delayed Weibull distribution serves as foundational data model. The specific method of MPSE (maximum product of spacings estimation) and MLE-based methods are used for parameter estimation. Bootstrap confidence intervals for parameters and significance tests in a two group setting are provided.

r-interleave 0.1.2
Propagated dependencies: r-rcpp@1.1.0 r-geometries@0.2.5
Channel: guix-cran
Location: guix-cran/packages/i.scm (guix-cran packages i)
Home page: https://cran.r-project.org/package=interleave
Licenses: Expat
Synopsis: Converts Tabular Data to Interleaved Vectors
Description:

Converts matrices and lists of matrices into a single vector by interleaving their values. That is, each element of the result vector is filled from the input matrices one row at a time. This is the same as transposing a matrix, then removing the dimension attribute, but is designed to operate on matrices in nested list structures.

r-incidenceprevalence 1.2.1
Propagated dependencies: r-tidyr@1.3.1 r-stringr@1.6.0 r-rlang@1.1.6 r-purrr@1.2.0 r-patientprofiles@1.4.4 r-omopgenerics@1.3.5 r-magrittr@2.0.4 r-glue@1.8.0 r-dplyr@1.1.4 r-clock@0.7.3 r-cli@3.6.5 r-cdmconnector@2.2.0
Channel: guix-cran
Location: guix-cran/packages/i.scm (guix-cran packages i)
Home page: https://darwin-eu.github.io/IncidencePrevalence/
Licenses: FSDG-compatible
Synopsis: Estimate Incidence and Prevalence using the OMOP Common Data Model
Description:

Calculate incidence and prevalence using data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model. Incidence and prevalence can be estimated for the total population in a database or for a stratification cohort.

r-inzighttools 2.0.3
Propagated dependencies: r-units@1.0-0 r-tidyr@1.3.1 r-tibble@3.3.0 r-survey@4.4-8 r-stringr@1.6.0 r-srvyr@1.3.0 r-rlang@1.1.6 r-readr@2.1.6 r-purrr@1.2.0 r-magrittr@2.0.4 r-glue@1.8.0 r-forcats@1.0.1 r-dplyr@1.1.4 r-dbi@1.2.3
Channel: guix-cran
Location: guix-cran/packages/i.scm (guix-cran packages i)
Home page: https://tools.inzight.nz
Licenses: GPL 3
Synopsis: Tools for 'iNZight'
Description:

This package provides a collection of wrapper functions for common variable and dataset manipulation workflows primarily used by iNZight', a graphical user interface providing easy exploration and visualisation of data for students of statistics, available in both desktop and online versions. Additionally, many of the functions return the tidyverse code used to obtain the result in an effort to bridge the gap between GUI and coding.

r-interpolators 1.0.1
Propagated dependencies: r-rcpp@1.1.0 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/i.scm (guix-cran packages i)
Home page: https://github.com/stla/interpolators
Licenses: GPL 3
Synopsis: Some Interpolation Methods
Description:

Some interpolation methods taken from Boost': barycentric rational interpolation, modified Akima interpolation, PCHIP (piecewise cubic Hermite interpolating polynomial) interpolation, and Catmull-Rom splines.

r-iccforest 0.5.1
Propagated dependencies: r-survival@3.8-3 r-partykit@1.2-24 r-ipred@0.9-15 r-icenreg@2.0.16
Channel: guix-cran
Location: guix-cran/packages/i.scm (guix-cran packages i)
Home page: https://cran.r-project.org/package=ICcforest
Licenses: GPL 2+
Synopsis: An Ensemble Method for Interval-Censored Survival Data
Description:

This package implements the conditional inference forest approach to modeling interval-censored survival data. It also provides functions to tune the parameters and evaluate the model fit. See Yao et al. (2019) <arXiv:1901.04599>.

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
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-isocor 0.2.8
Propagated dependencies: r-shinyjs@2.1.0 r-shinyalert@3.1.0 r-shiny@1.11.1 r-plyr@1.8.9 r-markdown@2.0 r-maldiquant@1.22.3 r-golem@0.5.1 r-dt@0.34.0 r-config@0.3.2 r-bslib@0.9.0
Channel: guix-cran
Location: guix-cran/packages/i.scm (guix-cran packages i)
Home page: https://github.com/janlisec/IsoCor
Licenses: GPL 3+
Synopsis: Analyze Isotope Ratios in a 'Shiny'-App
Description:

Analyzing Inductively Coupled Plasma - Mass Spectrometry (ICP-MS) measurement data to evaluate isotope ratios (IRs) is a complex process. The IsoCor package facilitates this process and renders it reproducible by providing a function to run a Shiny'-App locally in any web browser. In this App the user can upload data files of various formats, select ion traces, apply peak detection and perform calculation of IRs and delta values. Results are provided as figures and tables and can be exported. The App, therefore, facilitates data processing of ICP-MS experiments to quickly obtain optimal processing parameters compared to traditional Excel worksheet based approaches. A more detailed description can be found in the corresponding article <doi:10.1039/D2JA00208F>. The most recent version of IsoCor can be tested online at <https://apps.bam.de/shn00/IsoCor/>.

r-iis 1.1
Propagated dependencies: r-rfit@0.27.0 r-nsm3@1.20 r-hmisc@5.2-4 r-bsda@1.2.2 r-asbio@1.12-1
Channel: guix-cran
Location: guix-cran/packages/i.scm (guix-cran packages i)
Home page: https://cran.r-project.org/package=IIS
Licenses: GPL 2
Synopsis: Datasets to Accompany Wolfe and Schneider - Intuitive Introductory Statistics
Description:

These datasets and functions accompany Wolfe and Schneider (2017) - Intuitive Introductory Statistics (ISBN: 978-3-319-56070-0) <doi:10.1007/978-3-319-56072-4>. They are used in the examples throughout the text and in the end-of-chapter exercises. The datasets are meant to cover a broad range of topics in order to appeal to the diverse set of interests and backgrounds typically present in an introductory Statistics class.

r-icams 3.0.11
Propagated dependencies: r-zip@2.3.3 r-stringi@1.8.7 r-rcolorbrewer@1.1-3 r-lifecycle@1.0.4 r-iranges@2.44.0 r-genomicranges@1.62.0 r-genomeinfodb@1.46.0 r-fuzzyjoin@0.1.6.1 r-dplyr@1.1.4 r-data-table@1.17.8 r-bsgenome@1.78.0 r-biostrings@2.78.0
Channel: guix-cran
Location: guix-cran/packages/i.scm (guix-cran packages i)
Home page: https://github.com/steverozen/ICAMS
Licenses: GPL 3 FSDG-compatible
Synopsis: In-Depth Characterization and Analysis of Mutational Signatures ('ICAMS')
Description:

Analysis and visualization of experimentally elucidated mutational signatures -- the kind of analysis and visualization in Boot et al., "In-depth characterization of the cisplatin mutational signature in human cell lines and in esophageal and liver tumors", Genome Research 2018, <doi:10.1101/gr.230219.117> and "Characterization of colibactin-associated mutational signature in an Asian oral squamous cell carcinoma and in other mucosal tumor types", Genome Research 2020 <doi:10.1101/gr.255620.119>. ICAMS stands for In-depth Characterization and Analysis of Mutational Signatures. ICAMS has functions to read in variant call files (VCFs) and to collate the corresponding catalogs of mutational spectra and to analyze and plot catalogs of mutational spectra and signatures. Handles both "counts-based" and "density-based" (i.e. representation as mutations per megabase) mutational spectra or signatures.

r-internl 0.1.0
Propagated dependencies: r-mlmetrics@1.1.3 r-forecast@8.24.0
Channel: guix-cran
Location: guix-cran/packages/i.scm (guix-cran packages i)
Home page: https://cran.r-project.org/package=InterNL
Licenses: GPL 3
Synopsis: Time Series Intervention Model Using Non-Linear Function
Description:

Intervention analysis is used to investigate structural changes in data resulting from external events. Traditional time series intervention models, viz. Autoregressive Integrated Moving Average model with exogeneous variables (ARIMA-X) and Artificial Neural Networks with exogeneous variables (ANN-X), rely on linear intervention functions such as step or ramp functions, or their combinations. In this package, the Gompertz, Logistic, Monomolecular, Richard and Hoerl function have been used as non-linear intervention function. The equation of the above models are represented as: Gompertz: A * exp(-B * exp(-k * t)); Logistic: K / (1 + ((K - N0) / N0) * exp(-r * t)); Monomolecular: A * exp(-k * t); Richard: A + (K - A) / (1 + exp(-B * (C - t)))^(1/beta) and Hoerl: a*(b^t)*(t^c).This package introduced algorithm for time series intervention analysis employing ARIMA and ANN models with a non-linear intervention function. This package has been developed using algorithm of Yeasin et al. <doi:10.1016/j.hazadv.2023.100325> and Paul and Yeasin <doi:10.1371/journal.pone.0272999>.

r-ip 0.1.6
Propagated dependencies: r-stringi@1.8.7
Channel: guix-cran
Location: guix-cran/packages/i.scm (guix-cran packages i)
Home page: https://cran.r-project.org/package=IP
Licenses: GPL 2+
Synopsis: Classes and Methods for 'IP' Addresses
Description:

This package provides S4 classes for Internet Protocol (IP) versions 4 and 6 addresses and efficient methods for IP addresses comparison, arithmetic, bit manipulation and lookup. Both IPv4 and IPv6 arbitrary ranges are also supported as well as internationalized ('IDN') domain lookup with and whois query.

r-intrinsic 1.1.2
Propagated dependencies: r-salso@0.3.57 r-rlang@1.1.6 r-reshape2@1.4.5 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-knitr@1.50 r-ggplot2@4.0.1 r-fnn@1.1.4.1 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/i.scm (guix-cran packages i)
Home page: https://github.com/Fradenti/intRinsic
Licenses: Expat
Synopsis: Likelihood-Based Intrinsic Dimension Estimators
Description:

This package provides functions to estimate the intrinsic dimension of a dataset via likelihood-based approaches. Specifically, the package implements the TWO-NN and Gride estimators and the Hidalgo Bayesian mixture model. In addition, the first reference contains an extended vignette on the usage of the TWO-NN and Hidalgo models. References: Denti (2023, <doi:10.18637/jss.v106.i09>); Allegra et al. (2020, <doi:10.1038/s41598-020-72222-0>); Denti et al. (2022, <doi:10.1038/s41598-022-20991-1>); Facco et al. (2017, <doi:10.1038/s41598-017-11873-y>); Santos-Fernandez et al. (2021, <doi:10.1038/s41598-022-20991-1>).

r-inbreedr 0.3.3
Propagated dependencies: 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=inbreedR
Licenses: GPL 2
Synopsis: Analysing Inbreeding Based on Genetic Markers
Description:

This package provides a framework for analysing inbreeding and heterozygosity-fitness correlations (HFCs) based on microsatellite and SNP markers.

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