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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-profast 1.7
Propagated dependencies: r-seurat@5.3.1 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-precast@1.8 r-pbapply@1.7-4 r-mclust@6.1.2 r-matrix@1.7-4 r-irlba@2.3.5.1 r-harmony@1.2.4 r-gtools@3.9.5 r-ggplot2@4.0.1 r-future@1.68.0 r-furrr@0.3.1 r-dr-sc@3.7
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://github.com/feiyoung/ProFAST
Licenses: GPL 3
Build system: r
Synopsis: Probabilistic Factor Analysis for Spatially-Aware Dimension Reduction
Description:

Probabilistic factor analysis for spatially-aware dimension reduction across multi-section spatial transcriptomics data with millions of spatial locations. More details can be referred to Wei Liu, et al. (2023) <doi:10.1101/2023.07.11.548486>.

r-preciseplacement 0.2.0
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://cran.r-project.org/package=precisePlacement
Licenses: Expat
Build system: r
Synopsis: Suite of Functions to Help Get Plot Elements Exactly Where You Want Them
Description:

This package provides a selection of tools that make it easier to place elements onto a (base R) plot exactly where you want them. It allows users to identify points and distances on a plot in terms of inches, pixels, margin lines, data units, and proportions of the plotting space, all in a manner more simple than manipulating par().

r-preregr 0.2.9
Propagated dependencies: r-yaml@2.3.10 r-rmdpartials@0.6.5 r-jsonlite@2.0.0 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://preregr.opens.science
Licenses: GPL 3+
Build system: r
Synopsis: Specify (Pre)Registrations and Export Them Human- And Machine-Readably
Description:

Preregistrations, or more generally, registrations, enable explicit timestamped and (often but not necessarily publicly) frozen documentation of plans and expectations as well as decisions and justifications. In research, preregistrations are commonly used to clearly document plans and facilitate justifications of deviations from those plans, as well as decreasing the effects of publication bias by enabling identification of research that was conducted but not published. Like reporting guidelines, (pre)registration forms often have specific structures that facilitate systematic reporting of important items. The preregr package facilitates specifying (pre)registrations in R and exporting them to a human-readable format (using R Markdown partials or exporting to an HTML file) as well as human-readable embedded data (using JSON'), as well as importing such exported (pre)registration specifications from such embedded JSON'.

r-pmlbr 0.3.0
Propagated dependencies: r-fnn@1.1.4.1
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://github.com/EpistasisLab/pmlbr
Licenses: GPL 2 FSDG-compatible
Build system: r
Synopsis: Interface to the Penn Machine Learning Benchmarks Data Repository
Description:

Check available classification and regression data sets from the PMLB repository and download them. The PMLB repository (<https://github.com/EpistasisLab/pmlbr>) contains a curated collection of data sets for evaluating and comparing machine learning algorithms. These data sets cover a range of applications, and include binary/multi-class classification problems and regression problems, as well as combinations of categorical, ordinal, and continuous features. There are currently over 150 datasets included in the PMLB repository.

r-pysparklyr 0.2.0
Propagated dependencies: r-withr@3.0.2 r-vctrs@0.6.5 r-tidyselect@1.2.1 r-tidyr@1.3.1 r-sparklyr@1.9.3 r-rstudioapi@0.17.1 r-rlang@1.1.6 r-reticulate@1.44.1 r-purrr@1.2.0 r-processx@3.8.6 r-lifecycle@1.0.4 r-httr2@1.2.1 r-glue@1.8.0 r-fs@1.6.6 r-dplyr@1.1.4 r-dbplyr@2.5.1 r-dbi@1.2.3 r-connectcreds@0.2.0 r-cli@3.6.5 r-arrow@22.0.0
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://github.com/mlverse/pysparklyr
Licenses: Expat
Build system: r
Synopsis: Provides a 'PySpark' Back-End for the 'sparklyr' Package
Description:

It enables sparklyr to integrate with Spark Connect', and Databricks Connect by providing a wrapper over the PySpark python library.

r-politeness 0.9.4
Propagated dependencies: r-tm@0.7-16 r-tibble@3.3.0 r-textir@2.0-5 r-stringr@1.6.0 r-stringi@1.8.7 r-spacyr@1.3.0 r-quanteda@4.3.1 r-magrittr@2.0.4 r-glmnet@4.1-10 r-ggrepel@0.9.6 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://cran.r-project.org/package=politeness
Licenses: Expat
Build system: r
Synopsis: Detecting Politeness Features in Text
Description:

Detecting markers of politeness in English natural language. This package allows researchers to easily visualize and quantify politeness between groups of documents. This package combines prior research on the linguistic markers of politeness. We thank the Spencer Foundation, the Hewlett Foundation, and Harvard's Institute for Quantitative Social Science for support.

r-phscs 0.1.0
Propagated dependencies: r-tidyr@1.3.1 r-stringr@1.6.0 r-purrr@1.2.0 r-jsonlite@2.0.0 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://yng-me.github.io/phscs/
Licenses: Expat
Build system: r
Synopsis: Philippine Statistical Classification Systems
Description:

This package provides a unified interface to access and manipulate various Philippine statistical classifications. It allows users to retrieve, filter, and harmonize classification data, making it easier to work with Philippine statistical data in R.

r-predictorselect 0.1.0
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://cran.r-project.org/package=PredictorSelect
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Out-of-Sample Predictability in Predictive Regressions with Many Predictor Candidates
Description:

Consider a linear predictive regression setting with a potentially large set of candidate predictors. This work is concerned with detecting the presence of out of sample predictability based on out of sample mean squared error comparisons given in Gonzalo and Pitarakis (2023) <doi:10.1016/j.ijforecast.2023.10.005>.

r-poolr 1.2-0
Propagated dependencies: r-mathjaxr@1.8-0
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://cran.r-project.org/package=poolr
Licenses: GPL 2+
Build system: r
Synopsis: Methods for Pooling P-Values from (Dependent) Tests
Description:

This package provides functions for pooling/combining the results (i.e., p-values) from (dependent) hypothesis tests. Included are Fisher's method, Stouffer's method, the inverse chi-square method, the Bonferroni method, Tippett's method, and the binomial test. Each method can be adjusted based on an estimate of the effective number of tests or using empirically derived null distribution using pseudo replicates. For Fisher's, Stouffer's, and the inverse chi-square method, direct generalizations based on multivariate theory are also available (leading to Brown's method, Strube's method, and the generalized inverse chi-square method). An introduction can be found in Cinar and Viechtbauer (2022) <doi:10.18637/jss.v101.i01>.

r-pomaspu 1.0.0
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-matrixstats@1.5.0 r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://cran.r-project.org/package=POMaSPU
Licenses: GPL 3+
Build system: r
Synopsis: Adaptive Association Tests for Multiple Phenotypes using Proportional Odds Model (POM-aSPU)
Description:

POM-aSPU test evaluates an association between an ordinal response and multiple phenotypes, for details see Kim and Pan (2017) <DOI:10.1002/gepi.22033>.

r-pkgstats 0.2.2
Dependencies: global@6.6.14
Propagated dependencies: r-withr@3.0.2 r-sys@3.4.3 r-roxygen2@7.3.3 r-readr@2.1.6 r-memoise@2.0.1 r-igraph@2.2.1 r-fs@1.6.6 r-dplyr@1.1.4 r-cpp11@0.5.2 r-checkmate@2.3.3 r-brio@1.1.5 r-ami@0.2.1
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://docs.ropensci.org/pkgstats/
Licenses: GPL 3
Build system: r
Synopsis: Metrics of R Packages
Description:

Static code analyses for R packages using the external code-tagging libraries ctags and gtags'. Static analyses enable packages to be analysed very quickly, generally a couple of seconds at most. The package also provides access to a database generating by applying the main function to the full CRAN archive, enabling the statistical properties of any package to be compared with all other CRAN packages.

r-prettycols 1.1.0
Propagated dependencies: r-lifecycle@1.0.4 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://nrennie.rbind.io/PrettyCols/
Licenses: CC0
Build system: r
Synopsis: Pretty Colour Palettes
Description:

Defines aesthetically pleasing colour palettes.

r-perutimber 0.1.0
Propagated dependencies: r-lifecycle@1.0.4
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://github.com/PaulESantos/perutimber
Licenses: Expat
Build system: r
Synopsis: Catalogue of the Timber Forest Species of the Peruvian Amazon
Description:

Access the data of the Catalogue of the Timber Forest Species of the Peruvian Amazon Vásquez Martà nez, R., & Rojas Gonzáles, R.D.P.(2022)<doi:10.21704/rfp.v37i3.1956>.

r-pridit 1.1.0
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://github.com/rlieberthal/PRIDIT
Licenses: FSDG-compatible
Build system: r
Synopsis: Principal Component Analysis Applied to Ridit Scoring
Description:

This package implements the PRIDIT (Principal Component Analysis applied to RIDITs') scoring system described in Brockett et al. (2002) <doi:10.1111/1539-6975.00027>. Provides functions for ridit scoring originally developed by Bross (1958) <doi:10.2307/2527727>, calculating PRIDIT weights, and computing final PRIDIT scores for multivariate analysis of ordinal data.

r-palm 1.1.6
Propagated dependencies: r-rcpp@1.1.0 r-r6@2.6.1 r-mvtnorm@1.3-3 r-minqa@1.2.8 r-gsl@2.1-9
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://github.com/b-steve/palm
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Fitting Point Process Models via the Palm Likelihood
Description:

This package provides functions to fit point process models using the Palm likelihood. First proposed by Tanaka, Ogata, and Stoyan (2008) <DOI:10.1002/bimj.200610339>, maximisation of the Palm likelihood can provide computationally efficient parameter estimation for point process models in situations where the full likelihood is intractable. This package is chiefly focused on Neyman-Scott point processes, but can also fit the void processes proposed by Jones-Todd et al. (2019) <DOI:10.1002/sim.8046>. The development of this package was motivated by the analysis of capture-recapture surveys on which individuals cannot be identified---the data from which can conceptually be seen as a clustered point process (Stevenson, Borchers, and Fewster, 2019 <DOI:10.1111/biom.12983>). As such, some of the functions in this package are specifically for the estimation of cetacean density from two-camera aerial surveys.

r-phylepic 0.3.0
Propagated dependencies: r-tidygraph@1.3.1 r-scales@1.4.0 r-rlang@1.1.6 r-lifecycle@1.0.4 r-igraph@2.2.1 r-ggraph@2.2.2 r-ggplot2@4.0.1 r-ggnewscale@0.5.2 r-forcats@1.0.1 r-dplyr@1.1.4 r-cowplot@1.2.0 r-cli@3.6.5 r-ape@5.8-1
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://github.com/cidm-ph/phylepic
Licenses: Expat
Build system: r
Synopsis: Combined Visualisation of Phylogenetic and Epidemiological Data
Description:

This package provides a collection of utilities and ggplot2 extensions to assist with visualisations in genomic epidemiology. This includes the phylepic chart, a visual combination of a phylogenetic tree and a matched epidemic curve. The included ggplot2 extensions such as date axes binned by week are relevant for other applications in epidemiology and beyond. The approach is described in Suster et al. (2024) <doi:10.1101/2024.04.02.24305229>.

r-pegs 0.2
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://cran.r-project.org/package=pegs
Licenses: GPL 3
Build system: r
Synopsis: Pseudo-Expectation Gauss-Seidel
Description:

This package provides a lightweight, dependency-free, and simplified implementation of the Pseudo-Expectation Gauss-Seidel (PEGS) algorithm. It fits the multivariate ridge regression model for genomic prediction Xavier and Habier (2022) <doi:10.1186/s12711-022-00730-w> and Xavier et al. (2025) <doi:10.1093/genetics/iyae179>, providing heritability estimates, genetic correlations, breeding values, and regression coefficient estimates for prediction. This package provides an alternative to the bWGR package by Xavier et al. (2019) <doi:10.1093/bioinformatics/btz794> by using LAPACK for its algebraic operations.

r-pawscore 1.0.3
Propagated dependencies: r-signal@1.8-1 r-brglm2@1.0.1
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://cran.r-project.org/package=pawscore
Licenses: GPL 3
Build system: r
Synopsis: Pain Assessment at Withdrawal Speeds (PAWS)
Description:

Automated pain scoring from paw withdrawal tracking data. Based on Jones et al. (2020) "A machine-vision approach for automated pain measurement at millisecond timescales" <doi:10.7554/eLife.57258>.

r-paintmap 1.0
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://cran.r-project.org/package=paintmap
Licenses: GPL 2+
Build system: r
Synopsis: Plotting Paintmaps
Description:

Plots matrices of colours as grids of coloured squares - aka heatmaps, guaranteeing legible row and column names, without transformation of values, without re-ordering rows or columns, and without dendrograms.

r-pac 1.1.6
Propagated dependencies: r-rtsne@0.17 r-rcpp@1.1.0 r-parmigene@1.1.1 r-infotheo@1.2.0.1 r-igraph@2.2.1 r-ggrepel@0.9.6 r-ggplot2@4.0.1 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://doi.org/10.1371/journal.pcbi.1005875
Licenses: GPL 3
Build system: r
Synopsis: Partition-Assisted Clustering and Multiple Alignments of Networks
Description:

This package implements partition-assisted clustering and multiple alignments of networks. It 1) utilizes partition-assisted clustering to find robust and accurate clusters and 2) discovers coherent relationships of clusters across multiple samples. It is particularly useful for analyzing single-cell data set. Please see Li et al. (2017) <doi:10.1371/journal.pcbi.1005875> for detail method description.

r-predict3d 0.1.6
Propagated dependencies: r-tidyr@1.3.1 r-stringr@1.6.0 r-rlang@1.1.6 r-rgl@1.3.31 r-reshape2@1.4.5 r-purrr@1.2.0 r-plyr@1.8.9 r-modelr@0.1.11 r-magrittr@2.0.4 r-ggplot2@4.0.1 r-ggiraphextra@0.3.0 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://github.com/cardiomoon/predict3d
Licenses: GPL 2
Build system: r
Synopsis: Draw Three Dimensional Predict Plot Using Package 'rgl'
Description:

Draw 2 dimensional and three dimensional plot for multiple regression models using package ggplot2 and rgl'. Supports linear models (lm), generalized linear models (glm) and local polynomial regression fittings (loess).

r-personalized 0.2.8
Propagated dependencies: r-xgboost@1.7.11.1 r-survival@3.8-3 r-plotly@4.11.0 r-mgcv@1.9-4 r-kernlab@0.9-33 r-glmnet@4.1-10 r-ggplot2@4.0.1 r-foreach@1.5.2 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://jaredhuling.org/personalized/
Licenses: GPL 2
Build system: r
Synopsis: Estimation and Validation Methods for Subgroup Identification and Personalized Medicine
Description:

This package provides functions for fitting and validation of models for subgroup identification and personalized medicine / precision medicine under the general subgroup identification framework of Chen et al. (2017) <doi:10.1111/biom.12676>. This package is intended for use for both randomized controlled trials and observational studies and is described in detail in Huling and Yu (2021) <doi:10.18637/jss.v098.i05>.

r-patterns 1.7
Propagated dependencies: r-wgcna@1.73 r-vgam@1.1-13 r-tnet@3.0.16 r-selectboost@2.3.0 r-plotrix@3.8-13 r-nnls@1.6 r-movmf@0.2-10 r-mfuzz@2.70.0 r-limma@3.66.0 r-lattice@0.22-7 r-lars@1.3 r-igraph@2.2.1 r-gplots@3.2.0 r-e1071@1.7-16 r-cluster@2.1.8.1 r-abind@1.4-8
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://fbertran.github.io/Patterns/
Licenses: GPL 2+
Build system: r
Synopsis: Deciphering Biological Networks with Patterned Heterogeneous Measurements
Description:

This package provides a modeling tool dedicated to biological network modeling (Bertrand and others 2020, <doi:10.1093/bioinformatics/btaa855>). It allows for single or joint modeling of, for instance, genes and proteins. It starts with the selection of the actors that will be the used in the reverse engineering upcoming step. An actor can be included in that selection based on its differential measurement (for instance gene expression or protein abundance) or on its time course profile. Wrappers for actors clustering functions and cluster analysis are provided. It also allows reverse engineering of biological networks taking into account the observed time course patterns of the actors. Many inference functions are provided and dedicated to get specific features for the inferred network such as sparsity, robust links, high confidence links or stable through resampling links. Some simulation and prediction tools are also available for cascade networks (Jung and others 2014, <doi:10.1093/bioinformatics/btt705>). Example of use with microarray or RNA-Seq data are provided.

r-ppci 0.1.5
Propagated dependencies: r-rarpack@0.11-0
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://cran.r-project.org/package=PPCI
Licenses: GPL 3
Build system: r
Synopsis: Projection Pursuit for Cluster Identification
Description:

This package implements recently developed projection pursuit algorithms for finding optimal linear cluster separators. The clustering algorithms use optimal hyperplane separators based on minimum density, Pavlidis et. al (2016) <http://jmlr.org/papers/volume17/15-307/15-307.pdf>; minimum normalised cut, Hofmeyr (2017) <doi:10.1109/TPAMI.2016.2609929>; and maximum variance ratio clusterability, Hofmeyr and Pavlidis (2015) <doi:10.1109/SSCI.2015.116>.

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