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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-diflasso 1.0-5
Propagated dependencies: r-penalized@0.9-53 r-misctools@0.6-28 r-grplasso@0.4-7
Channel: guix-cran
Location: guix-cran/packages/d.scm (guix-cran packages d)
Home page: https://cran.r-project.org/package=DIFlasso
Licenses: GPL 2
Build system: r
Synopsis: Penalty Approach to Differential Item Functioning in Rasch Models
Description:

This package performs DIFlasso as proposed by Tutz and Schauberger (2015) <doi:10.1007/s11336-013-9377-6>, a method to detect DIF (Differential Item Functioning) in Rasch Models. It can handle settings with many variables and also metric variables.

r-disttools 0.1.8
Channel: guix-cran
Location: guix-cran/packages/d.scm (guix-cran packages d)
Home page: https://cran.r-project.org/package=disttools
Licenses: Expat
Build system: r
Synopsis: Distance Object Manipulation Tools
Description:

This package provides convenient methods for accessing the data in dist objects with minimal memory and computational overhead. disttools can be used to extract the distance between any pair or combination of points encoded by a dist object using only the indices of those points. This is an improvement over existing functionality, which requires either coercing a dist object into a matrix or calculating the one dimensional index corresponding to a pair of observations. Coercion to a matrix is undesirable because doing so doubles the amount of memory required for storage. In contrast, there is no inherent downside to the latter solution. However, in part due to several edge cases, correctly and efficiently implementing such a solution can be challenging. disttools abstracts away these challenges and provides a simple interface to access the data in a dist object using the latter approach.

r-dpgmm 0.2.2
Propagated dependencies: r-reshape2@1.4.5 r-rcolorbrewer@1.1-3 r-pracma@2.4.6 r-mvtnorm@1.3-3 r-matrix@1.7-4 r-ggpubr@0.6.2 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/d.scm (guix-cran packages d)
Home page: https://cran.r-project.org/package=dpGMM
Licenses: GPL 3
Build system: r
Synopsis: Dynamic Programming Based Gaussian Mixture Modelling Tool for 1D and 2D Data
Description:

Gaussian mixture modeling of one- and two-dimensional data, provided in original or binned form, with an option to estimate the number of model components. The method uses Gaussian Mixture Models (GMM) with initial parameters determined by a dynamic programming algorithm, leading to stable and reproducible model fitting.

r-dataprep 0.1.5
Propagated dependencies: r-zoo@1.8-14 r-scales@1.4.0 r-reshape2@1.4.5 r-ggplot2@4.0.1 r-foreach@1.5.2 r-dplyr@1.1.4 r-doparallel@1.0.17 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/d.scm (guix-cran packages d)
Home page: https://cran.r-project.org/package=dataprep
Licenses: GPL 2+
Build system: r
Synopsis: Efficient and Flexible Data Preprocessing Tools
Description:

Efficiently and flexibly preprocess data using a set of data filtering, deletion, and interpolation tools. These data preprocessing methods are developed based on the principles of completeness, accuracy, threshold method, and linear interpolation and through the setting of constraint conditions, time completion & recovery, and fast & efficient calculation and grouping. Key preprocessing steps include deletions of variables and observations, outlier removal, and missing values (NA) interpolation, which are dependent on the incomplete and dispersed degrees of raw data. They clean data more accurately, keep more samples, and add no outliers after interpolation, compared with ordinary methods. Auto-identification of consecutive NA via run-length based grouping is used in observation deletion, outlier removal, and NA interpolation; thus, new outliers are not generated in interpolation. Conditional extremum is proposed to realize point-by-point weighed outlier removal that saves non-outliers from being removed. Plus, time series interpolation with values to refer to within short periods further ensures reliable interpolation. These methods are based on and improved from the reference: Liang, C.-S., Wu, H., Li, H.-Y., Zhang, Q., Li, Z. & He, K.-B. (2020) <doi:10.1016/j.scitotenv.2020.140923>.

r-ddplot 0.0.2
Propagated dependencies: r-r2d3@0.2.6
Channel: guix-cran
Location: guix-cran/packages/d.scm (guix-cran packages d)
Home page: https://github.com/feddelegrand7/ddplot
Licenses: GPL 3+
Build system: r
Synopsis: Create D3 Based SVG Graphics
Description:

Create D3 based SVG ('Scalable Vector Graphics') graphics using a simple R API. The package aims to simplify the creation of many SVG plot types using a straightforward R API. The package relies on the r2d3 R package and the D3 JavaScript library. See <https://rstudio.github.io/r2d3/> and <https://d3js.org/> respectively.

r-d4talink-light 2.1.21
Propagated dependencies: r-rsqlite@2.4.4 r-rmarkdown@2.30 r-rjson@0.2.23 r-reticulate@1.44.1 r-quarto@1.5.1 r-openxlsx@4.2.8.1 r-openssl@2.3.4 r-officedown@0.4.1 r-getpass@0.2-4 r-feather@0.3.5 r-dbi@1.2.3 r-biobase@2.70.0
Channel: guix-cran
Location: guix-cran/packages/d.scm (guix-cran packages d)
Home page: https://bitbucket.org/SQ4/d4talink.light
Licenses: GPL 3
Build system: r
Synopsis: GDP - Workflow Management
Description:

Tools, methods and processes for the management of analysis workflows. These lightweight solutions facilitate structuring R&D activities. These solutions were developed to comply with Good Documentation Practice (GDP), with ALCOA+ principles as proposed by the U.S. FDA, and with FAIR principles as discussed by Jacobsen et al. (2017) <doi:10.1162/dint_r_00024>.

r-demoshiny 0.1
Propagated dependencies: r-shiny@1.11.1
Channel: guix-cran
Location: guix-cran/packages/d.scm (guix-cran packages d)
Home page: https://cran.r-project.org/package=demoShiny
Licenses: GPL 3
Build system: r
Synopsis: Runs a 'Shiny' App as Demo or Lists All Demo 'Shiny' Apps
Description:

Mimics the demo functionality for Shiny apps in a package. Apps stored to the package subdirectory inst/shiny can be called by demoShiny(topic).

r-distatisr 1.1.2
Propagated dependencies: r-tidytext@0.4.3 r-readxl@1.4.5 r-prettygraphs@2.2.0 r-dplyr@1.1.4 r-car@3.1-3
Channel: guix-cran
Location: guix-cran/packages/d.scm (guix-cran packages d)
Home page: https://cran.r-project.org/package=DistatisR
Licenses: GPL 2
Build system: r
Synopsis: DiSTATIS Three Way Metric Multidimensional Scaling
Description:

Implement DiSTATIS and CovSTATIS (three-way multidimensional scaling). DiSTATIS and CovSTATIS are used to analyze multiple distance/covariance matrices collected on the same set of observations. These methods are based on Abdi, H., Williams, L.J., Valentin, D., & Bennani-Dosse, M. (2012) <doi:10.1002/wics.198>.

r-deploid 0.5.7
Dependencies: zlib@1.3.1
Propagated dependencies: r-scales@1.4.0 r-rmarkdown@2.30 r-rcpp@1.1.0 r-plotly@4.11.0 r-magrittr@2.0.4 r-htmlwidgets@1.6.4 r-deploid-utils@0.0.1
Channel: guix-cran
Location: guix-cran/packages/d.scm (guix-cran packages d)
Home page: https://github.com/DEploid-dev/DEploid-r
Licenses: GPL 3+
Build system: r
Synopsis: Deconvolute Mixed Genomes with Unknown Proportions
Description:

Traditional phasing programs are limited to diploid organisms. Our method modifies Li and Stephens algorithm with Markov chain Monte Carlo (MCMC) approaches, and builds a generic framework that allows haplotype searches in a multiple infection setting. This package is primarily developed as part of the Pf3k project, which is a global collaboration using the latest sequencing technologies to provide a high-resolution view of natural variation in the malaria parasite Plasmodium falciparum. Parasite DNA are extracted from patient blood sample, which often contains more than one parasite strain, with unknown proportions. This package is used for deconvoluting mixed haplotypes, and reporting the mixture proportions from each sample.

r-dipalm 1.2
Propagated dependencies: r-wgcna@1.73 r-limma@3.66.0 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/d.scm (guix-cran packages d)
Home page: https://cran.r-project.org/package=DiPALM
Licenses: GPL 2+
Build system: r
Synopsis: Differential Pattern Analysis via Linear Modeling
Description:

Individual gene expression patterns are encoded into a series of eigenvector patterns ('WGCNA package). Using the framework of linear model-based differential expression comparisons ('limma package), time-course expression patterns for genes in different conditions are compared and analyzed for significant pattern changes. For reference, see: Greenham K, Sartor RC, Zorich S, Lou P, Mockler TC and McClung CR. eLife. 2020 Sep 30;9(4). <doi:10.7554/eLife.58993>.

r-dctensor 1.3.1
Propagated dependencies: r-rtensor@1.4.9 r-nntensor@1.3.0 r-mass@7.3-65 r-fields@17.1
Channel: guix-cran
Location: guix-cran/packages/d.scm (guix-cran packages d)
Home page: https://github.com/rikenbit/dcTensor
Licenses: Expat
Build system: r
Synopsis: Discrete Matrix/Tensor Decomposition
Description:

Semi-Binary and Semi-Ternary Matrix Decomposition are performed based on Non-negative Matrix Factorization (NMF) and Singular Value Decomposition (SVD). For the details of the methods, see the reference section of GitHub README.md <https://github.com/rikenbit/dcTensor>.

r-ddecompose 1.0.0
Propagated dependencies: r-sandwich@3.1-1 r-rifreg@1.1.0 r-ranger@0.17.0 r-pbapply@1.7-4 r-hmisc@5.2-4 r-ggplot2@4.0.1 r-formula@1.2-5 r-fastglm@0.0.3
Channel: guix-cran
Location: guix-cran/packages/d.scm (guix-cran packages d)
Home page: https://cran.r-project.org/package=ddecompose
Licenses: GPL 3+
Build system: r
Synopsis: Detailed Distributional Decomposition
Description:

This package implements the Oaxaca-Blinder decomposition method and generalizations of it that decompose differences in distributional statistics beyond the mean. The function ob_decompose() decomposes differences in the mean outcome between two groups into one part explained by different covariates (composition effect) and into another part due to differences in the way covariates are linked to the outcome variable (structure effect). The function further divides the two effects into the contribution of each covariate and allows for weighted doubly robust decompositions. For distributional statistics beyond the mean, the function performs the recentered influence function (RIF) decomposition proposed by Firpo, Fortin, and Lemieux (2018). The function dfl_decompose() divides differences in distributional statistics into an composition effect and a structure effect using inverse probability weighting as introduced by DiNardo, Fortin, and Lemieux (1996). The function also allows to sequentially decompose the composition effect into the contribution of single covariates. References: Firpo, Sergio, Nicole M. Fortin, and Thomas Lemieux. (2018) <doi:10.3390/econometrics6020028>. "Decomposing Wage Distributions Using Recentered Influence Function Regressions." Fortin, Nicole M., Thomas Lemieux, and Sergio Firpo. (2011) <doi:10.3386/w16045>. "Decomposition Methods in Economics." DiNardo, John, Nicole M. Fortin, and Thomas Lemieux. (1996) <doi:10.2307/2171954>. "Labor Market Institutions and the Distribution of Wages, 1973-1992: A Semiparametric Approach." Oaxaca, Ronald. (1973) <doi:10.2307/2525981>. "Male-Female Wage Differentials in Urban Labor Markets." Blinder, Alan S. (1973) <doi:10.2307/144855>. "Wage Discrimination: Reduced Form and Structural Estimates.".

r-datoramar 0.1.0
Propagated dependencies: r-tibble@3.3.0 r-jsonlite@2.0.0 r-httr@1.4.7
Channel: guix-cran
Location: guix-cran/packages/d.scm (guix-cran packages d)
Home page: https://github.com/beigebrucewayne/datoramar
Licenses: Expat
Build system: r
Synopsis: Interface to the 'Datorama' API
Description:

This package provides a thin wrapper around the Datorama API. Ideal for analyzing marketing data from <https://datorama.com>.

r-dlagm 1.1.13
Propagated dependencies: r-wavethresh@4.7.3 r-strucchange@1.5-4 r-sandwich@3.1-1 r-roll@1.2.0 r-plyr@1.8.9 r-nardl@0.1.6 r-mass@7.3-65 r-lmtest@0.9-40 r-formula-tools@1.7.1 r-dynlm@0.3-6 r-aer@1.2-15
Channel: guix-cran
Location: guix-cran/packages/d.scm (guix-cran packages d)
Home page: https://cran.r-project.org/package=dLagM
Licenses: GPL 3
Build system: r
Synopsis: Time Series Regression Models with Distributed Lag Models
Description:

This package provides time series regression models with one predictor using finite distributed lag models, polynomial (Almon) distributed lag models, geometric distributed lag models with Koyck transformation, and autoregressive distributed lag models. It also consists of functions for computation of h-step ahead forecasts from these models. See Demirhan (2020)(<doi:10.1371/journal.pone.0228812>) and Baltagi (2011)(<doi:10.1007/978-3-642-20059-5>) for more information.

r-depower 2026.1.30
Propagated dependencies: r-scales@1.4.0 r-rdpack@2.6.4 r-mvnfast@0.2.8 r-multidplyr@0.1.4 r-glmmtmb@1.1.13 r-ggplot2@4.0.1 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/d.scm (guix-cran packages d)
Home page: https://brettklamer.com/work/depower/
Licenses: Expat
Build system: r
Synopsis: Power Analysis for Differential Expression Studies
Description:

This package provides a convenient framework to simulate, test, power, and visualize data for differential expression studies with lognormal or negative binomial outcomes. Supported designs are two-sample comparisons of independent or dependent outcomes. Power may be summarized in the context of controlling the per-family error rate or family-wise error rate. Negative binomial methods are described in Yu, Fernandez, and Brock (2017) <doi:10.1186/s12859-017-1648-2> and Yu, Fernandez, and Brock (2020) <doi:10.1186/s12859-020-3541-7>.

r-door 0.0.3
Propagated dependencies: r-tidyr@1.3.1 r-scales@1.4.0 r-labeling@0.4.3 r-ggplot2@4.0.1 r-forestplot@3.1.7 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/d.scm (guix-cran packages d)
Home page: https://cran.r-project.org/package=door
Licenses: GPL 3+
Build system: r
Synopsis: Analysis of Clinical Trials with the Desirability of Outcome Ranking Methodology
Description:

Statistical methods and related graphical representations for the Desirability of Outcome Ranking (DOOR) methodology. The DOOR is a paradigm for the design, analysis, interpretation of clinical trials and other research studies based on the patient centric benefit risk evaluation. The package provides functions for generating summary statistics from individual level/summary level datasets, conduct DOOR probability-based inference, and visualization of the results. For more details of DOOR methodology, see Hamasaki and Evans (2025) <doi:10.1201/9781003390855>. For more explanation of the statistical methods and the graphics, see the technical document and user manual of the DOOR Shiny apps at <https://methods.bsc.gwu.edu>.

r-dpcid 1.1
Channel: guix-cran
Location: guix-cran/packages/d.scm (guix-cran packages d)
Home page: https://sites.google.com/site/dhyeonyu/software
Licenses: GPL 2+
Build system: r
Synopsis: Differential Partial Correlation IDentification
Description:

Differential partial correlation identification with the ridge and the fusion penalties.

r-debbi 0.1.0
Propagated dependencies: r-randtoolbox@2.0.5 r-numderiv@2016.8-1.1 r-doparallel@1.0.17
Channel: guix-cran
Location: guix-cran/packages/d.scm (guix-cran packages d)
Home page: https://github.com/bmgaldo/DEBBI
Licenses: Expat
Build system: r
Synopsis: Differential Evolution-Based Bayesian Inference
Description:

Bayesian inference algorithms based on the population-based "differential evolution" (DE) algorithm. Users can obtain posterior mode (MAP) estimates via DEMAP, posterior samples via DEMCMC, and variational approximations via DEVI.

r-derezende-ferreira 0.1.2
Propagated dependencies: r-xts@0.14.1
Channel: guix-cran
Location: guix-cran/packages/d.scm (guix-cran packages d)
Home page: https://cran.r-project.org/package=DeRezende.Ferreira
Licenses: GPL 2+
Build system: r
Synopsis: Zero Coupon Yield Curve Modelling
Description:

Modeling the zero coupon yield curve using the dynamic De Rezende and Ferreira (2011) <doi:10.1002/for.1256> five factor model with variable or fixed decaying parameters. For explanatory purposes, the package also includes various short datasets of interest rates for the BRICS countries.

r-denseflmm 0.1.3
Propagated dependencies: r-mvtnorm@1.3-3 r-mgcv@1.9-4 r-matrix@1.7-4 r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/d.scm (guix-cran packages d)
Home page: https://cran.r-project.org/package=denseFLMM
Licenses: GPL 2
Build system: r
Synopsis: Functional Linear Mixed Models for Densely Sampled Data
Description:

Estimation of functional linear mixed models for densely sampled data based on functional principal component analysis.

r-daagbio 0.63-4
Propagated dependencies: r-limma@3.66.0
Channel: guix-cran
Location: guix-cran/packages/d.scm (guix-cran packages d)
Home page: https://github.com/jhmaindonald/DAAGbio/
Licenses: GPL 2+
Build system: r
Synopsis: Data Sets and Functions, for Demonstrations with Expression Arrays and Gene Sequences
Description:

Data sets and functions, for the display of gene expression array (microarray) data, and for demonstrations with such data.

r-designlibrary 0.1.10
Propagated dependencies: r-rlang@1.1.6 r-randomizr@1.0.0 r-glue@1.8.0 r-generics@0.1.4 r-fabricatr@1.0.2 r-estimatr@1.0.6 r-declaredesign@1.1.0
Channel: guix-cran
Location: guix-cran/packages/d.scm (guix-cran packages d)
Home page: https://declaredesign.org/r/designlibrary/
Licenses: Expat
Build system: r
Synopsis: Library of Research Designs
Description:

This package provides a simple interface to build designs using the package DeclareDesign'. In one line of code, users can specify the parameters of individual designs and diagnose their properties. The designers can also be used to compare performance of a given design across a range of combinations of parameters, such as effect size, sample size, and assignment probabilities.

r-dptm 3.0.2
Propagated dependencies: r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-r6@2.6.1 r-purrr@1.2.0 r-parabar@1.4.2 r-mass@7.3-65 r-coda@0.19-4.1 r-bayesiantools@0.1.8
Channel: guix-cran
Location: guix-cran/packages/d.scm (guix-cran packages d)
Home page: https://github.com/HujieBai/DPTM
Licenses: GPL 3+
Build system: r
Synopsis: Dynamic Panel Multiple Threshold Model with Fixed Effects
Description:

Compute the fixed effects dynamic panel threshold model suggested by Ramà rez-Rondán (2020) <doi:10.1080/07474938.2019.1624401>, and dynamic panel linear model suggested by Hsiao et al. (2002) <doi:10.1016/S0304-4076(01)00143-9>, where maximum likelihood type estimators are used. Multiple thresholds estimation based on Markov Chain Monte Carlo (MCMC) is allowed, and model selection of linear model, threshold model and multiple threshold model is also allowed.

r-dipm 1.12
Propagated dependencies: r-survival@3.8-3 r-rlang@1.1.6 r-partykit@1.2-24 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/d.scm (guix-cran packages d)
Home page: https://cran.r-project.org/package=dipm
Licenses: GPL 2+
Build system: r
Synopsis: Depth Importance in Precision Medicine (DIPM) Method
Description:

An implementation by Chen, Li, and Zhang (2022) <doi: 10.1093/bioadv/vbac041> of the Depth Importance in Precision Medicine (DIPM) method in Chen and Zhang (2022) <doi:10.1093/biostatistics/kxaa021> and Chen and Zhang (2020) <doi:10.1007/978-3-030-46161-4_16>. The DIPM method is a classification tree that searches for subgroups with especially poor or strong performance in a given treatment group.

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