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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-pclasso 1.2
Propagated dependencies: r-svd@0.5.8
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://arxiv.org/abs/1810.04651
Licenses: GPL 3
Build system: r
Synopsis: Principal Components Lasso
Description:

This package provides a method for fitting the entire regularization path of the principal components lasso for linear and logistic regression models. The algorithm uses cyclic coordinate descent in a path-wise fashion. See URL below for more information on the algorithm. See Tay, K., Friedman, J. ,Tibshirani, R., (2014) Principal component-guided sparse regression <arXiv:1810.04651>.

r-patternize 0.0.5
Propagated dependencies: r-vegan@2.7-2 r-sp@2.2-0 r-sf@1.0-23 r-rniftyreg@2.8.5 r-raster@3.6-32 r-purrr@1.2.0 r-morpho@2.13 r-magrittr@2.0.4 r-imager@1.0.5 r-geomorph@4.0.10 r-dplyr@1.1.4 r-clusterr@1.3.5 r-abind@1.4-8
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://github.com/StevenVB12/patternize
Licenses: GPL 3
Build system: r
Synopsis: Quantification of Color Pattern Variation
Description:

Quantification of variation in organismal color patterns as obtained from image data. Patternize defines homology between pattern positions across images either through fixed landmarks or image registration. Pattern identification is performed by categorizing the distribution of colors using RGB thresholds or image segmentation.

r-phylopath 1.3.1
Propagated dependencies: r-tibble@3.3.0 r-purrr@1.2.0 r-phylolm@2.6.5 r-igraph@2.2.1 r-ggraph@2.2.2 r-ggplot2@4.0.1 r-ggm@2.5.2 r-future-apply@1.20.0 r-ape@5.8-1
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://Ax3man.github.io/phylopath/
Licenses: GPL 3
Build system: r
Synopsis: Perform Phylogenetic Path Analysis
Description:

This package provides a comprehensive and easy to use R implementation of confirmatory phylogenetic path analysis as described by Von Hardenberg and Gonzalez-Voyer (2012) <doi:10.1111/j.1558-5646.2012.01790.x>.

r-pemultinom 0.1.1
Propagated dependencies: r-rcpp@1.1.0 r-nnet@7.3-20 r-magrittr@2.0.4 r-lpsolve@5.6.23 r-foreach@1.5.2 r-doparallel@1.0.17
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://cran.r-project.org/package=pemultinom
Licenses: GPL 2
Build system: r
Synopsis: L1-Penalized Multinomial Regression with Statistical Inference
Description:

We aim for fitting a multinomial regression model with Lasso penalty and doing statistical inference (calculating confidence intervals of coefficients and p-values for individual variables). It implements 1) the coordinate descent algorithm to fit an l1-penalized multinomial regression model (parameterized with a reference level); 2) the debiasing approach to obtain the inference results, which is described in "Tian, Y., Rusinek, H., Masurkar, A. V., & Feng, Y. (2024). L1â Penalized Multinomial Regression: Estimation, Inference, and Prediction, With an Application to Risk Factor Identification for Different Dementia Subtypes. Statistics in Medicine, 43(30), 5711-5747.".

r-plot3drgl 1.0.5
Propagated dependencies: r-rgl@1.3.31 r-plot3d@1.4.2
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://cran.r-project.org/package=plot3Drgl
Licenses: GPL 3+
Build system: r
Synopsis: Plotting Multi-Dimensional Data - Using 'rgl'
Description:

The rgl implementation of plot3D functions.

r-plotscale 0.1.6
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://cran.r-project.org/package=plotscale
Licenses: GPL 3
Build system: r
Synopsis: Scale Graphics Devices Using Plot Dimensions
Description:

Figures rendered on graphics devices are usually rescaled to fit pre-determined device dimensions. plotscale implements the reverse: desired plot dimensions are specified and device dimensions are calculated to accommodate marginal material, giving consistent proportions for plot elements. Default methods support grid graphics such as lattice and ggplot. See "example('devsize')" and "vignette('plotscale')".

r-poisbinordnor 1.6.3
Propagated dependencies: r-psych@2.5.6 r-mvtnorm@1.3-3 r-matrix@1.7-4 r-genord@2.0.0 r-corpcor@1.6.10
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://cran.r-project.org/package=PoisBinOrdNor
Licenses: GPL 2 GPL 3
Build system: r
Synopsis: Data Generation with Poisson, Binary, Ordinal and Normal Components
Description:

Generation of multiple count, binary, ordinal and normal variables simultaneously given the marginal characteristics and association structure. The details of the method are explained in Demirtas et al. (2012) <DOI:10.1002/sim.5362>.

r-poisferobust 2.0.0
Propagated dependencies: r-glmmml@1.1.7 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://bitbucket.org/ew-btb/poisson-fe-robust
Licenses: Expat
Build system: r
Synopsis: Poisson Fixed Effects Robust
Description:

Computation of robust standard errors of Poisson fixed effects models, following Wooldridge (1999).

r-packetllm 0.1.1
Propagated dependencies: r-shinyjs@2.1.0 r-shiny@1.11.1 r-readtext@0.92.1 r-promises@1.5.0 r-pdftools@3.6.0 r-httr@1.4.7 r-future@1.68.0
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://github.com/AntoniCzolgowski/PacketLLM
Licenses: Expat
Build system: r
Synopsis: Interactive 'OpenAI' Model Integration in 'RStudio'
Description:

Offers an interactive RStudio gadget interface for communicating with OpenAI large language models (e.g., gpt-5', gpt-5-mini', gpt-5-nano') (<https://platform.openai.com/docs/api-reference>). Enables users to conduct multiple chat conversations simultaneously in separate tabs. Supports uploading local files (R, PDF, DOCX) to provide context for the models. Allows per-conversation configuration of system messages (where supported by the model). API interactions via the httr package are performed asynchronously using promises and future to avoid blocking the R console. Useful for tasks like code generation, text summarization, and document analysis directly within the RStudio environment. Requires an OpenAI API key set as an environment variable.

r-psychmeta 2.7.0
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-rlang@1.1.6 r-purrr@1.2.0 r-progress@1.2.3 r-metafor@4.8-0 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-curl@7.0.0 r-boot@1.3-32
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://cran.r-project.org/package=psychmeta
Licenses: GPL 3+
Build system: r
Synopsis: Psychometric Meta-Analysis Toolkit
Description:

This package provides tools for computing bare-bones and psychometric meta-analyses and for generating psychometric data for use in meta-analysis simulations. Supports bare-bones, individual-correction, and artifact-distribution methods for meta-analyzing correlations and d values. Includes tools for converting effect sizes, computing sporadic artifact corrections, reshaping meta-analytic databases, computing multivariate corrections for range variation, and more. Bugs can be reported to <https://github.com/psychmeta/psychmeta/issues> or <issues@psychmeta.com>.

r-pcra 1.2
Propagated dependencies: r-zoo@1.8-14 r-xts@0.14.1 r-robustbase@0.99-6 r-robstattm@1.0.11 r-r-cache@0.17.0 r-quadprog@1.5-8 r-portfolioanalytics@2.1.1 r-performanceanalytics@2.0.8 r-lattice@0.22-7 r-data-table@1.17.8 r-corpcor@1.6.10 r-boot@1.3-32
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://cran.r-project.org/package=PCRA
Licenses: GPL 2
Build system: r
Synopsis: Companion to Portfolio Construction and Risk Analysis
Description:

This package provides a collection of functions and data sets that support teaching a quantitative finance MS level course on Portfolio Construction and Risk Analysis, and the writing of a textbook for such a course. The package is unique in providing several real-world data sets that may be used for problem assignments and student projects. The data sets include cross-sections of stock data from the Center for Research on Security Prices, LLC (CRSP), corresponding factor exposures data from S&P Global, and several SP500 data sets.

r-pssmooth 1.0.3
Propagated dependencies: r-osdesign@1.8 r-np@0.60-18 r-mass@7.3-65 r-chngpt@2024.11-15
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://github.com/mjuraska/pssmooth
Licenses: GPL 2
Build system: r
Synopsis: Flexible and Efficient Evaluation of Principal Surrogates/Treatment Effect Modifiers
Description:

This package implements estimation and testing procedures for evaluating an intermediate biomarker response as a principal surrogate of a clinical response to treatment (i.e., principal stratification effect modification analysis), as described in Juraska M, Huang Y, and Gilbert PB (2020), Inference on treatment effect modification by biomarker response in a three-phase sampling design, Biostatistics, 21(3): 545-560 <doi:10.1093/biostatistics/kxy074>. The methods avoid the restrictive placebo structural risk modeling assumption common to past methods and further improve robustness by the use of nonparametric kernel smoothing for biomarker density estimation. A randomized controlled two-group clinical efficacy trial is assumed with an ordered categorical or continuous univariate biomarker response measured at a fixed timepoint post-randomization and with a univariate baseline surrogate measure allowed to be observed in only a subset of trial participants with an observed biomarker response (see the flexible three-phase sampling design in the paper for details). Bootstrap-based procedures are available for pointwise and simultaneous confidence intervals and testing of four relevant hypotheses. Summary and plotting functions are provided for estimation results.

r-pdsce 1.2.1
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://cran.r-project.org/package=PDSCE
Licenses: GPL 2
Build system: r
Synopsis: Positive Definite Sparse Covariance Estimators
Description:

Compute and tune some positive definite and sparse covariance estimators.

r-portn 1.0.0
Propagated dependencies: r-rsolnp@2.0.1
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://github.com/ysd2004/portn
Licenses: GPL 2+
Build system: r
Synopsis: Portfolio Analysis for Nature
Description:

The functions are designed to find the efficient mean-variance frontier or portfolio weights for static portfolio (called Markowitz portfolio) analysis in resource economics or nature conservation. Using the nonlinear programming solver ('Rsolnp'), this package deals with the quadratic minimization of the variance-covariances without shorting (i.e., non-negative portfolio weights) studied in Ando and Mallory (2012) <doi:10.1073/pnas.1114653109>. See the examples, testing versions, and more details from: <https://github.com/ysd2004/portn>.

r-probs 0.9.9
Propagated dependencies: r-reshape@0.8.10 r-mass@7.3-65 r-combinat@0.0-8
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://cran.r-project.org/package=probs
Licenses: GPL 3+
Build system: r
Synopsis: Elementary Probability on Finite Sample Spaces
Description:

This package performs elementary probability calculations on finite sample spaces, which may be represented by data frames or lists. This package is meant to rescue some widely used functions from the archived prob package (see <https://cran.r-project.org/src/contrib/Archive/prob/>). Functionality includes setting up sample spaces, counting tools, defining probability spaces, performing set algebra, calculating probability and conditional probability, tools for simulation and checking the law of large numbers, adding random variables, and finding marginal distributions. Characteristic functions for all base R distributions are included.

r-primeplus 1.0.16
Propagated dependencies: r-survival@3.8-3 r-msm@1.8.2
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://cran.r-project.org/package=PRIMEplus
Licenses: GPL 2
Build system: r
Synopsis: Study Design for Immunotherapy Clinical Trials
Description:

Perform sample size, power calculation and subsequent analysis for Immuno-oncology (IO) trials composed of responders and non-responders.

r-proteus 1.1.5
Propagated dependencies: r-torch@0.16.3 r-tictoc@1.2.1 r-stringr@1.6.0 r-scales@1.4.0 r-readr@2.1.6 r-purrr@1.2.0 r-narray@0.5.2 r-moments@0.14.1 r-modeest@2.4.0 r-lubridate@1.9.4 r-imputets@3.4 r-greybox@2.0.8 r-ggthemes@5.1.0 r-ggplot2@4.0.1 r-future@1.68.0 r-furrr@0.3.1 r-fancova@0.6-1 r-dplyr@1.1.4 r-abind@1.4-8
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://rpubs.com/giancarlo_vercellino/proteus
Licenses: GPL 3
Build system: r
Synopsis: Multiform Seq2Seq Model for Time-Feature Analysis
Description:

Seq2seq time-feature analysis based on variational model, with a wide range of distributions available for the latent variable.

r-picker 0.2.6
Propagated dependencies: r-htmlwidgets@1.6.4
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://github.com/hms-dbmi/picker
Licenses: Expat
Build system: r
Synopsis: Pick Data Points from a Deck.gl Scatterplot
Description:

Performant interactive scatterplot for ~ 1 million points. Zoom, pan, and pick points. Includes tooltips, labels, a grid overlay, legend, and coupled interactions across multiple plots.

r-pcredux 1.2-1
Propagated dependencies: r-zoo@1.8-14 r-shiny@1.11.1 r-segmented@2.1-4 r-robustbase@0.99-6 r-qpcr@1.4-2 r-pracma@2.4.6 r-pbapply@1.7-4 r-mbmca@1.1-0 r-fda-usc@2.2.0 r-ecp@3.1.6 r-chippcr@1.0-2 r-changepoint@2.3
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://CRAN.R-project.org/package=PCRedux
Licenses: Expat
Build system: r
Synopsis: Quantitative Polymerase Chain Reaction (qPCR) Data Mining and Machine Learning Toolkit as Described in Burdukiewicz (2022) <doi:10.21105/Joss.04407>
Description:

Extracts features from amplification curve data of quantitative Polymerase Chain Reactions (qPCR) according to Pabinger et al. 2014 <doi:10.1016/j.bdq.2014.08.002> for machine learning purposes. Helper functions prepare the amplification curve data for processing as functional data (e.g., Hausdorff distance) or enable the plotting of amplification curve classes (negative, ambiguous, positive). The hookreg() and hookregNL() functions of Burdukiewicz et al. (2018) <doi:10.1016/j.bdq.2018.08.001> can be used to predict amplification curves with an hook effect-like curvature. The pcrfit_single() function can be used to extract features from an amplification curve.

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-path-analysis 0.1
Propagated dependencies: r-pastecs@1.4.2 r-metan@1.19.0 r-mathjaxr@1.8-0 r-hmisc@5.2-4 r-gplots@3.2.0 r-diagrammer@1.0.11 r-corrr@0.4.5 r-corrplot@0.95 r-complexheatmap@2.26.0
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://github.com/abeyran/Path.Analysis
Licenses: GPL 3
Build system: r
Synopsis: Path Coefficient Analysis
Description:

Facilitates the performance of several analyses, including simple and sequential path coefficient analysis, correlation estimate, drawing correlogram, Heatmap, and path diagram. When working with raw data, that includes one or more dependent variables along with one or more independent variables are available, the path coefficient analysis can be conducted. It allows for testing direct effects, which can be a vital indicator in path coefficient analysis. The process of preparing the dataset rule is explained in detail in the vignette file "Path.Analysis_manual.Rmd". You can find this in the folders labelled "data" and "~/inst/extdata". Also see: 1)the lavaan', 2)a sample of sequential path analysis in metan suggested by Olivoto and Lúcio (2020) <doi:10.1111/2041-210X.13384>, 3)the simple PATHSAS macro written in SAS by Cramer et al. (1999) <doi:10.1093/jhered/90.1.260>, and 4)the semPlot() function of OpenMx as initial tools for conducting path coefficient analyses and SEM (Structural Equation Modeling). To gain a comprehensive understanding of path coefficient analysis, both in theory and practice, see a Minitab macro developed by Arminian, A. in the paper by Arminian et al. (2008) <doi:10.1080/15427520802043182>.

r-pldamixture 0.1.1
Propagated dependencies: r-survival@3.8-3
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://github.com/bpriy/pldamixture
Licenses: GPL 2
Build system: r
Synopsis: Post-Linkage Data Analysis Based on Mixture Modelling
Description:

Perform inference in the secondary analysis setting with linked data potentially containing mismatch errors. Only the linked data file may be accessible and information about the record linkage process may be limited or unavailable. Implements the General Framework for Regression with Mismatched Data developed by Slawski et al. (2023) <doi:10.48550/arXiv.2306.00909>. The framework uses a mixture model for pairs of linked records whose two components reflect distributions conditional on match status, i.e., correct match or mismatch. Inference is based on composite likelihood and the Expectation-Maximization (EM) algorithm. The package currently supports Cox Proportional Hazards Regression (right-censored data only) and Generalized Linear Regression Models (Gaussian, Gamma, Poisson, and Logistic (binary models only)). Information about the underlying record linkage process can be incorporated into the method if available (e.g., assumed overall mismatch rate, safe matches, predictors of match status, or predicted probabilities of correct matches).

r-pempi 1.0.0
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://github.com/stephaneguerrier/pempi
Licenses: AGPL 3
Build system: r
Synopsis: Proportion Estimation with Marginal Proxy Information
Description:

This package provides a system contains easy-to-use tools for the conditional estimation of the prevalence of an emerging or rare infectious diseases using the methods proposed in Guerrier et al. (2023) <arXiv:2012.10745>.

r-pdspecest 1.2.6
Propagated dependencies: r-rdpack@2.6.4 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-multitaper@1.0-17 r-ddalpha@1.3.16
Channel: guix-cran
Location: guix-cran/packages/p.scm (guix-cran packages p)
Home page: https://github.com/JorisChau/pdSpecEst
Licenses: GPL 2
Build system: r
Synopsis: An Analysis Toolbox for Hermitian Positive Definite Matrices
Description:

An implementation of data analysis tools for samples of symmetric or Hermitian positive definite matrices, such as collections of covariance matrices or spectral density matrices. The tools in this package can be used to perform: (i) intrinsic wavelet transforms for curves (1D) or surfaces (2D) of Hermitian positive definite matrices with applications to dimension reduction, denoising and clustering in the space of Hermitian positive definite matrices; and (ii) exploratory data analysis and inference for samples of positive definite matrices by means of intrinsic data depth functions and rank-based hypothesis tests in the space of Hermitian positive definite matrices.

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