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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-multicoll 2.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: http://colldetreat.r-forge.r-project.org/
Licenses: GPL 2+
Build system: r
Synopsis: Collinearity Detection in a Multiple Linear Regression Model
Description:

The detection of worrying approximate collinearity in a multiple linear regression model is a problem addressed in all existing statistical packages. However, we have detected deficits regarding to the incorrect treatment of qualitative independent variables and the role of the intercept of the model. The objective of this package is to correct these deficits. In this package will be available detection and treatment techniques traditionally used as the recently developed.

r-mvskmod 0.1.0
Propagated dependencies: r-truncnorm@1.0-9 r-pracma@2.4.6 r-maxlik@1.5-2.1 r-matlib@1.0.1 r-distributionutils@0.6-2 r-clustergeneration@1.3.8 r-bessel@0.7-0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/soonsk-vcu/MVSKmod
Licenses: Expat
Build system: r
Synopsis: Matrix-Variate Skew Linear Regression Models
Description:

An implementation of the alternating expectation conditional maximization (AECM) algorithm for matrix-variate variance gamma (MVVG) and normal-inverse Gaussian (MVNIG) linear models. These models are designed for settings of multivariate analysis with clustered non-uniform observations and correlated responses. The package includes fitting and prediction functions for both models, and an example dataset from a periodontal on Gullah-speaking African Americans, with responses in gaad_res, and covariates in gaad_cov. For more details on the matrix-variate distributions used, see Gallaugher & McNicholas (2019) <doi:10.1016/j.spl.2018.08.012>.

r-marmot 0.0.4
Propagated dependencies: r-parsec@1.2.9
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MARMoT
Licenses: GPL 3+
Build system: r
Synopsis: Matching on Poset-Based Average Rank for Multiple Treatments (MARMoT)
Description:

It contains the function to apply MARMoT balancing technique discussed in: Silan, Boccuzzo, Arpino (2021) <DOI:10.1002/sim.9192>, Silan, Belloni, Boccuzzo, (2023) <DOI:10.1007/s10260-023-00695-0>; furthermore it contains a function for computing the Deloof's approximation of the average rank (and also a parallelized version) and a function to compute the Absolute Standardized Bias.

r-mrfdepth 1.0.17
Propagated dependencies: r-reshape2@1.4.5 r-rcppeigen@0.3.4.0.2 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-matrixstats@1.5.0 r-ggplot2@4.0.1 r-geometry@0.5.2 r-abind@1.4-8
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mrfDepth
Licenses: GPL 2+
Build system: r
Synopsis: Depth Measures in Multivariate, Regression and Functional Settings
Description:

This package provides tools to compute depth measures and implementations of related tasks such as outlier detection, data exploration and classification of multivariate, regression and functional data.

r-mapstats 3.2
Propagated dependencies: r-ttutils@1.0-1.1 r-survey@4.4-8 r-sp@2.2-0 r-sf@1.0-23 r-reshape2@1.4.5 r-rcolorbrewer@1.1-3 r-lattice@0.22-7 r-hmisc@5.2-4 r-colorspace@2.1-2 r-classint@0.4-11
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mapStats
Licenses: GPL 2+
Build system: r
Synopsis: Geographic Display of Survey Data Statistics
Description:

Automated calculation and visualization of survey data statistics on a color-coded (choropleth) map.

r-mcsim 1.0
Propagated dependencies: r-mass@7.3-65 r-circstats@0.2-7
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MCSim
Licenses: GPL 2
Build system: r
Synopsis: Determine the Optimal Number of Clusters
Description:

Identifies the optimal number of clusters by calculating the similarity between two clustering methods at the same number of clusters using the corrected indices of Rand and Jaccard as described in Albatineh and Niewiadomska-Bugaj (2011). The number of clusters at which the index attain its maximum more frequently is a candidate for being the optimal number of clusters.

r-mvglmmrank 1.2-4
Propagated dependencies: r-numderiv@2016.8-1.1 r-matrix@1.7-4 r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mvglmmRank
Licenses: GPL 2
Build system: r
Synopsis: Multivariate Generalized Linear Mixed Models for Ranking Sports Teams
Description:

Maximum likelihood estimates are obtained via an EM algorithm with either a first-order or a fully exponential Laplace approximation as documented by Broatch and Karl (2018) <doi:10.48550/arXiv.1710.05284>, Karl, Yang, and Lohr (2014) <doi:10.1016/j.csda.2013.11.019>, and by Karl (2012) <doi:10.1515/1559-0410.1471>. Karl and Zimmerman <doi:10.1016/j.jspi.2020.06.004> use this package to illustrate how the home field effect estimator from a mixed model can be biased under nonrandom scheduling.

r-molhd 0.2
Propagated dependencies: r-fields@17.1 r-arrangements@1.1.9
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MOLHD
Licenses: LGPL 2.0+
Build system: r
Synopsis: Multiple Objective Latin Hypercube Design
Description:

Generate the optimal maximin distance, minimax distance (only for low dimensions), and maximum projection designs within the class of Latin hypercube designs efficiently for computer experiments. Generate Pareto front optimal designs for each two of the three criteria and all the three criteria within the class of Latin hypercube designs efficiently. Provide criterion computing functions. References of this package can be found in Morris, M. D. and Mitchell, T. J. (1995) <doi:10.1016/0378-3758(94)00035-T>, Lu Lu and Christine M. Anderson-CookTimothy J. Robinson (2011) <doi:10.1198/Tech.2011.10087>, Joseph, V. R., Gul, E., and Ba, S. (2015) <doi:10.1093/biomet/asv002>.

r-mazeinda 0.0.2
Propagated dependencies: r-foreach@1.5.2
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mazeinda
Licenses: GPL 3
Build system: r
Synopsis: Monotonic Association on Zero-Inflated Data
Description:

This package provides methods for calculating and testing the significance of pairwise monotonic association from and based on the work of Pimentel (2009) <doi:10.4135/9781412985291.n2>. Computation of association of vectors from one or multiple sets can be performed in parallel thanks to the packages foreach and doMC'.

r-modelcharts 0.1.0
Propagated dependencies: r-plotly@4.11.0 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=Modelcharts
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Classification Model Charts
Description:

This package provides two important functions for producing Gain chart and Lift chart for any classification model.

r-mombf 3.5.4
Propagated dependencies: r-survival@3.8-3 r-sparsematrixstats@1.22.0 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-pracma@2.4.6 r-ncvreg@3.16.0 r-mvtnorm@1.3-3 r-mgcv@1.9-4 r-mclust@6.1.2 r-matrix@1.7-4 r-intervals@0.15.5 r-glmnet@4.1-10 r-glasso@1.11 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/davidrusi/mombf
Licenses: GPL 2+ FSDG-compatible
Build system: r
Synopsis: Model Selection with Bayesian Methods and Information Criteria
Description:

Model selection and averaging for regression and mixtures, inclusing Bayesian model selection and information criteria (BIC, EBIC, AIC, GIC).

r-mlr3resampling 2025.11.19
Propagated dependencies: r-r6@2.6.1 r-pbdmpi@0.5-4 r-paradox@1.0.1 r-mlr3misc@0.19.0 r-mlr3@1.2.0 r-data-table@1.17.8 r-checkmate@2.3.3
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/tdhock/mlr3resampling
Licenses: LGPL 3
Build system: r
Synopsis: Resampling Algorithms for 'mlr3' Framework
Description:

This package provides a supervised learning algorithm inputs a train set, and outputs a prediction function, which can be used on a test set. If each data point belongs to a subset (such as geographic region, year, etc), then how do we know if subsets are similar enough so that we can get accurate predictions on one subset, after training on Other subsets? And how do we know if training on All subsets would improve prediction accuracy, relative to training on the Same subset? SOAK, Same/Other/All K-fold cross-validation, <doi:10.48550/arXiv.2410.08643> can be used to answer these questions, by fixing a test subset, training models on Same/Other/All subsets, and then comparing test error rates (Same versus Other and Same versus All). Also provides code for estimating how many train samples are required to get accurate predictions on a test set.

r-maditr 0.8.7
Propagated dependencies: r-magrittr@2.0.4 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/gdemin/maditr
Licenses: GPL 2
Build system: r
Synopsis: Fast Data Aggregation, Modification, and Filtering with Pipes and 'data.table'
Description:

This package provides pipe-style interface for data.table'. Package preserves all data.table features without significant impact on performance. let and take functions are simplified interfaces for most common data manipulation tasks. For example, you can write take(mtcars, mean(mpg), by = am) for aggregation or let(mtcars, hp_wt = hp/wt, hp_wt_mpg = hp_wt/mpg) for modification. Use take_if/let_if for conditional aggregation/modification. Additionally there are some conveniences such as automatic data.frame conversion to data.table'.

r-missforestpredict 1.0.1
Propagated dependencies: r-ranger@0.17.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/sibipx/missForestPredict
Licenses: GPL 2+
Build system: r
Synopsis: Missing Value Imputation using Random Forest for Prediction Settings
Description:

Missing data imputation based on the missForest algorithm (Stekhoven, Daniel J (2012) <doi:10.1093/bioinformatics/btr597>) with adaptations for prediction settings. The function missForest() is used to impute a (training) dataset with missing values and to learn imputation models that can be later used for imputing new observations. The function missForestPredict() is used to impute one or multiple new observations (test set) using the models learned on the training data. For more details see Albu, E., Gao, S., Wynants, L., & Van Calster, B. (2024). missForestPredict--Missing data imputation for prediction settings <doi:10.48550/arXiv.2407.03379>.

r-mbres 0.1.7
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-scales@1.4.0 r-purrr@1.2.0 r-magrittr@2.0.4 r-ggplot2@4.0.1 r-forcats@1.0.1 r-dplyr@1.1.4 r-data-table@1.17.8 r-cowplot@1.2.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mbRes
Licenses: GPL 3
Build system: r
Synopsis: Exploration of Multiple Biomarker Responses using Effect Size
Description:

Summarize multiple biomarker responses of aquatic organisms to contaminants using Cliffâ s delta, as described in Pham & Sokolova (2023) <doi:10.1002/ieam.4676>.

r-maxcombo 1.0
Propagated dependencies: r-survival@3.8-3 r-rlang@1.1.6 r-purrr@1.2.0 r-mvtnorm@1.3-3 r-mstate@0.3.3 r-mcmcpack@1.7-1 r-magrittr@2.0.4 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=maxcombo
Licenses: GPL 2
Build system: r
Synopsis: The Group Sequential Max-Combo Test for Comparing Survival Curves
Description:

This package provides functions for comparing survival curves using the max-combo test at a single timepoint or repeatedly at successive respective timepoints while controlling type I error (i.e., the group sequential setting), as published by Prior (2020) <doi:10.1177/0962280220931560>. The max-combo test is a generalization of the weighted log-rank test, which itself is a generalization of the log-rank test, which is a commonly used statistical test for comparing survival curves, e.g., during or after a clinical trial as part of an effort to determine if a new drug or therapy is more effective at delaying undesirable outcomes than an established drug or therapy or a placebo.

r-medicaldata 0.2.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://higgi13425.github.io/medicaldata/
Licenses: Expat
Build system: r
Synopsis: Data Package for Medical Datasets
Description:

This package provides access to well-documented medical datasets for teaching. Featuring several from the Teaching of Statistics in the Health Sciences website <https://www.causeweb.org/tshs/category/dataset/>, a few reconstructed datasets of historical significance in medical research, some reformatted and extended from existing R packages, and some data donations.

r-mmc 0.0.3
Propagated dependencies: r-survival@3.8-3 r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mmc
Licenses: GPL 3+
Build system: r
Synopsis: Multivariate Measurement Error Correction
Description:

This package provides routines for multivariate measurement error correction. Includes procedures for linear, logistic and Cox regression models. Bootstrapped standard errors and confidence intervals can be obtained for corrected estimates.

r-microinverterdata 0.4.0
Propagated dependencies: r-units@1.0-0 r-tidyr@1.3.1 r-rlang@1.1.6 r-purrr@1.2.0 r-httr2@1.2.1 r-glue@1.8.0 r-dplyr@1.1.4 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://camembr.github.io/microinverterdata/
Licenses: Expat
Build system: r
Synopsis: Collect your Microinverter Data
Description:

Collect and normalize local microinverter energy and power production data through off-cloud API requests. Currently supports APSystems', Enphase', and Fronius microinverters.

r-medfate 4.8.4
Propagated dependencies: r-shiny@1.11.1 r-rcpp@1.1.0 r-meteoland@2.2.5 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://emf-creaf.github.io/medfate/
Licenses: GPL 2+ LGPL 3+
Build system: r
Synopsis: Mediterranean Forest Simulation
Description:

Simulate Mediterranean forest functioning and dynamics using cohort-based description of vegetation [De Caceres et al. (2015) <doi:10.1016/j.agrformet.2015.06.012>; De Caceres et al. (2021) <doi:10.1016/j.agrformet.2020.108233>].

r-mr-mashr 0.3.44
Propagated dependencies: r-rcppparallel@5.1.11-1 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-mvtnorm@1.3-3 r-matrixstats@1.5.0 r-matrix@1.7-4 r-mashr@0.2.79 r-flashier@1.0.7 r-ebnm@1.1-42
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/stephenslab/mr.mashr
Licenses: Expat
Build system: r
Synopsis: Multiple Regression with Multivariate Adaptive Shrinkage
Description:

This package provides an implementation of methods for multivariate multiple regression with adaptive shrinkage priors as described in F. Morgante et al (2023) <doi:10.1371/journal.pgen.1010539>.

r-mantis 1.0.2
Propagated dependencies: r-xts@0.14.1 r-tidyr@1.3.1 r-scales@1.4.0 r-rmarkdown@2.30 r-reactable@0.4.5 r-purrr@1.2.0 r-lubridate@1.9.4 r-knitr@1.50 r-htmltools@0.5.8.1 r-ggplot2@4.0.1 r-dygraphs@1.1.1.6 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/ropensci/mantis
Licenses: GPL 3+
Build system: r
Synopsis: Multiple Time Series Scanner
Description:

Generate interactive html reports that enable quick visual review of multiple related time series stored in a data frame. For static datasets, this can help to identify any temporal artefacts that may affect the validity of subsequent analyses. For live data feeds, regularly scheduled reports can help to pro-actively identify data feed problems or unexpected trends that may require action. The reports are self-contained and shareable without a web server.

r-managelocalrepo 0.1.5
Propagated dependencies: r-stringr@1.6.0 r-assertthat@0.2.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=managelocalrepo
Licenses: GPL 2
Build system: r
Synopsis: Manage a CRAN-Style Local Repository
Description:

This will allow easier management of a CRAN-style repository on local networks (i.e. not on CRAN). This might be necessary where hosted packages contain intellectual property owned by a corporation.

r-mlrintermbo 0.5.1-1
Propagated dependencies: r-r6@2.6.1 r-paradox@1.0.1 r-mlr3tuning@1.5.0 r-mlr3misc@0.19.0 r-lhs@1.2.0 r-data-table@1.17.8 r-checkmate@2.3.3 r-callr@3.7.6 r-bbotk@1.8.1 r-backports@1.5.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/mb706/mlrintermbo
Licenses: LGPL 3
Build system: r
Synopsis: Model-Based Optimization for 'mlr3' Through 'mlrMBO'
Description:

The mlrMBO package can ordinarily not be used for optimization within mlr3', because of incompatibilities of their respective class systems. mlrintermbo offers a compatibility interface that provides mlrMBO as an mlr3tuning Tuner object, for tuning of machine learning algorithms within mlr3', as well as a bbotk Optimizer object for optimization of general objective functions using the bbotk black box optimization framework. The control parameters of mlrMBO are faithfully reproduced as a paradox ParamSet'.

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