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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-msmu 0.1.2
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MSMU
Licenses: Expat
Build system: r
Synopsis: Descriptive Statistics Functions for Numeric Data
Description:

This package provides fundamental functions for descriptive statistics, including MODE(), estimate_mode(), center_stats(), position_stats(), pct(), spread_stats(), kurt(), skew(), and shape_stats(), which assist in summarizing the center, spread, and shape of numeric data. For more details, see McCurdy (2025), "Introduction to Data Science with R" <https://jonmccurdy.github.io/Introduction-to-Data-Science/>.

r-mcpmod 1.0-10.1
Propagated dependencies: r-mvtnorm@1.3-3 r-lattice@0.22-7
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MCPMod
Licenses: GPL 3
Build system: r
Synopsis: Design and Analysis of Dose-Finding Studies
Description:

This package implements a methodology for the design and analysis of dose-response studies that combines aspects of multiple comparison procedures and modeling approaches (Bretz, Pinheiro and Branson, 2005, Biometrics 61, 738-748, <doi: 10.1111/j.1541-0420.2005.00344.x>). The package provides tools for the analysis of dose finding trials as well as a variety of tools necessary to plan a trial to be conducted with the MCP-Mod methodology. Please note: The MCPMod package will not be further developed, all future development of the MCP-Mod methodology will be done in the DoseFinding R-package.

r-multispatialccm 1.3
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=multispatialCCM
Licenses: GPL 2+
Build system: r
Synopsis: Multispatial Convergent Cross Mapping
Description:

The multispatial convergent cross mapping algorithm can be used as a test for causal associations between pairs of processes represented by time series. This is a combination of convergent cross mapping (CCM), described in Sugihara et al., 2012, Science, 338, 496-500, and dew-drop regression, described in Hsieh et al., 2008, American Naturalist, 171, 71â 80. The algorithm allows CCM to be implemented on data that are not from a single long time series. Instead, data can come from many short time series, which are stitched together using bootstrapping.

r-mbr 0.0.1
Propagated dependencies: r-rfast@2.1.5.2 r-matrix@1.7-4 r-mass@7.3-65 r-dplr@1.7.8 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/ntthung/mbr
Licenses: GPL 2+
Build system: r
Synopsis: Mass Balance Reconstruction
Description:

Mass-balance-adjusted Regression algorithm for streamflow reconstruction at sub-annual resolution (e.g., seasonal or monthly). The algorithm implements a penalty term to minimize the differences between the total sub-annual flows and the annual flow. The method is described in Nguyen et al (2020) <DOI:10.1002/essoar.10504791.1>.

r-mm2sdata 1.0.3
Propagated dependencies: r-biobase@2.70.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MM2Sdata
Licenses: GPL 3
Build system: r
Synopsis: Gene Expression Datasets for the 'MM2S' Package
Description:

Gene Expression datasets for the MM2S package. Contains normalized expression data for Human Medulloblastoma ('GSE37418') as well as Mouse Medulloblastoma models ('GSE36594'). Deena Gendoo et al. (2015) <doi:10.1016/j.ygeno.2015.05.002>.

r-moodlequizr 2.1.1
Propagated dependencies: r-shiny@1.11.1 r-nmcalc@0.0.4 r-mvtnorm@1.3-3 r-base64@2.0.2
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=moodlequizR
Licenses: GPL 2+
Build system: r
Synopsis: Easily Create Fully Randomized 'Moodle' Test Questions
Description:

Routines to generate fully randomized moodle quizzes. It also contains 15 examples and a shiny app.

r-mccf1 1.1
Propagated dependencies: r-rocr@1.0-11 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://bitbucket.org/hoffmanlab/mccf1/
Licenses: GPL 2+
Build system: r
Synopsis: Creates the MCC-F1 Curve and Calculates the MCC-F1 Metric and the Best Threshold
Description:

The MCC-F1 analysis is a method to evaluate the performance of binary classifications. The MCC-F1 curve is more reliable than the Receiver Operating Characteristic (ROC) curve and the Precision-Recall (PR)curve under imbalanced ground truth. The MCC-F1 analysis also provides the MCC-F1 metric that integrates classifier performance over varying thresholds, and the best threshold of binary classification.

r-midi 0.1.0
Propagated dependencies: r-withr@3.0.2 r-rlang@1.1.6 r-r6@2.6.1 r-purrr@1.2.0 r-plotly@4.11.0 r-ggplot2@4.0.1 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/lmjl-alea/midi
Licenses: Expat
Build system: r
Synopsis: Microstructure Information from Diffusion Imaging
Description:

An implementation of a taxonomy of models of restricted diffusion in biological tissues parametrized by the tissue geometry (axis, diameter, density, etc.). This is primarily used in the context of diffusion magnetic resonance (MR) imaging to model the MR signal attenuation in the presence of diffusion gradients. The goal is to provide tools to simulate the MR signal attenuation predicted by these models under different experimental conditions. The package feeds a companion shiny app available at <https://midi-pastrami.apps.math.cnrs.fr> that serves as a graphical interface to the models and tools provided by the package. Models currently available are the ones in Neuman (1974) <doi:10.1063/1.1680931>, Van Gelderen et al. (1994) <doi:10.1006/jmrb.1994.1038>, Stanisz et al. (1997) <doi:10.1002/mrm.1910370115>, Soderman & Jonsson (1995) <doi:10.1006/jmra.1995.0014> and Callaghan (1995) <doi:10.1006/jmra.1995.1055>.

r-modernboot 0.1.1
Propagated dependencies: r-future-apply@1.20.0 r-future@1.68.0 r-boot@1.3-32
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/ikrakib/modernBoot
Licenses: Expat
Build system: r
Synopsis: Modern Resampling Methods: Bootstraps, Wild, Block, Permutation, and Selection Guidance
Description:

This package implements modern resampling and permutation methods for robust statistical inference without restrictive parametric assumptions. Provides bias-corrected and accelerated (BCa) bootstrap (Efron and Tibshirani (1993) <doi:10.1201/9780429246593>), wild bootstrap for heteroscedastic regression (Liu (1988) <doi:10.1214/aos/1176351062>, Davidson and Flachaire (2008) <doi:10.1016/j.jeconom.2008.08.003>), block bootstrap for time series (Politis and Romano (1994) <doi:10.1080/01621459.1994.10476870>), and permutation-based multiple testing correction (Westfall and Young (1993) <ISBN:0-471-55761-7>). Methods handle non-normal data, heteroscedasticity, time series correlation, and multiple comparisons.

r-measurementdiagnostics 0.3.0
Propagated dependencies: r-tidyr@1.3.1 r-stringr@1.6.0 r-rlang@1.1.6 r-purrr@1.2.0 r-patientprofiles@1.5.0 r-omopgenerics@1.3.7 r-glue@1.8.0 r-dplyr@1.1.4 r-dbi@1.2.3 r-cohortconstructor@0.6.1 r-cohortcharacteristics@1.1.1 r-clock@0.7.3 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://ohdsi.github.io/MeasurementDiagnostics/
Licenses: FSDG-compatible
Build system: r
Synopsis: Diagnostics for Lists of Codes Based on Measurements
Description:

Diagnostics of list of codes based on concepts from the domains measurement and observation. This package works for data mapped to the Observational Medical Outcomes Partnership Common Data Model.

r-mixl 1.3.5
Propagated dependencies: r-stringr@1.6.0 r-sandwich@3.1-1 r-readr@2.1.6 r-rcpp@1.1.0 r-randtoolbox@2.0.5 r-numderiv@2016.8-1.1 r-maxlik@1.5-2.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/joemolloy/fast-mixed-mnl
Licenses: GPL 2+
Build system: r
Synopsis: Simulated Maximum Likelihood Estimation of Mixed Logit Models for Large Datasets
Description:

Specification and estimation of multinomial logit models. Large datasets and complex models are supported, with an intuitive syntax. Multinomial Logit Models, Mixed models, random coefficients and Hybrid Choice are all supported. For more information, see Molloy et al. (2021) <https://www.research-collection.ethz.ch/handle/20.500.11850/477416>.

r-metamedian 1.2.1
Propagated dependencies: r-metafor@4.8-0 r-metablue@1.0.0 r-hmisc@5.2-4 r-estmeansd@1.0.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/stmcg/metamedian
Licenses: GPL 3+
Build system: r
Synopsis: Meta-Analysis of Medians
Description:

This package implements several methods to meta-analyze studies that report the sample median of the outcome. The methods described by McGrath et al. (2019) <doi:10.1002/sim.8013>, Ozturk and Balakrishnan (2020) <doi:10.1002/sim.8738>, and McGrath et al. (2020a) <doi:10.1002/bimj.201900036> can be applied to directly meta-analyze the median or difference of medians between groups. Additionally, a number of methods (e.g., McGrath et al. (2020b) <doi:10.1177/0962280219889080>, Cai et al. (2021) <doi:10.1177/09622802211047348>, and McGrath et al. (2023) <doi:10.1177/09622802221139233>) are implemented to estimate study-specific (difference of) means and their standard errors in order to estimate the pooled (difference of) means. Methods for meta-analyzing median survival times (McGrath et al. (2025) <doi:10.48550/arXiv.2503.03065>) are also implemented. See McGrath et al. (2024) <doi:10.1002/jrsm.1686> for a detailed guide on using the package.

r-modisfast 1.0.2
Propagated dependencies: r-xml2@1.5.0 r-terra@1.8-86 r-stringr@1.6.0 r-sf@1.0-23 r-rvest@1.0.5 r-purrr@1.2.0 r-magrittr@2.0.4 r-lubridate@1.9.4 r-httr@1.4.7 r-dplyr@1.1.4 r-curl@7.0.0 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/ptaconet/modisfast
Licenses: GPL 3+
Build system: r
Synopsis: Fast and Efficient Access to MODIS Earth Observation Data
Description:

Programmatic interface to several NASA Earth Observation OPeNDAP servers (Open-source Project for a Network Data Access Protocol) (<https://www.opendap.org/>). Allows for easy downloads of MODIS subsets, as well as other Earth Observation datacubes, in a time-saving and efficient way : by sampling it at the very downloading phase (spatially, temporally and dimensionally).

r-mau 0.4.0
Propagated dependencies: r-stringr@1.6.0 r-rdpack@2.6.4 r-rcolorbrewer@1.1-3 r-igraph@2.2.1 r-gtools@3.9.5 r-ggplot2@4.0.1 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/pedroguarderas/mau
Licenses: LGPL 3
Build system: r
Synopsis: Decision Models with Multi Attribute Utility Theory
Description:

This package provides functions for the creation, evaluation and test of decision models based in Multi Attribute Utility Theory (MAUT). Can process and evaluate local risk aversion utilities for a set of indexes, compute utilities and weights for the whole decision tree defining the decision model and simulate weights employing Dirichlet distributions under addition constraints in weights. Also includes other rating analysis methods as for example the Colley, Offensive - Defensive ratings and the ranking aggregation with Borda count.

r-metasvr 0.1.0
Propagated dependencies: r-hms@1.1.4 r-e1071@1.7-16
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/rechtianaputri/metaSVR
Licenses: GPL 3+
Build system: r
Synopsis: Support Vector Regression with Metaheuristic Algorithms Optimization
Description:

This package provides a hybrid modeling framework combining Support Vector Regression (SVR) with metaheuristic optimization algorithms, including the Archimedes Optimization Algorithm (AO) (Hashim et al. (2021) <doi:10.1007/s10489-020-01893-z>), Coot Bird Optimization (CBO) (Naruei & Keynia (2021) <doi:10.1016/j.eswa.2021.115352>), and their hybrid (AOCBO), as well as several others such as Harris Hawks Optimization (HHO) (Heidari et al. (2019) <doi:10.1016/j.future.2019.02.028>), Gray Wolf Optimizer (GWO) (Mirjalili et al. (2014) <doi:10.1016/j.advengsoft.2013.12.007>), Ant Lion Optimization (ALO) (Mirjalili (2015) <doi:10.1016/j.advengsoft.2015.01.010>), and Enhanced Harris Hawk Optimization with Coot Bird Optimization (EHHOCBO) (Cui et al. (2023) <doi:10.32604/cmes.2023.026019>). The package enables automatic tuning of SVR hyperparameters (cost, gamma, and epsilon) to enhance prediction performance. Suitable for regression tasks in domains such as renewable energy forecasting and hourly data prediction. For more details about implementation and parameter bounds see: Setiawan et al. (2021) <doi:10.1016/j.procs.2020.12.003> and Liu et al. (2018) <doi:10.1155/2018/6076475>.

r-mulvariaterandomforestvarimp 0.0.2
Propagated dependencies: r-multivariaterandomforest@1.1.5 r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/Megatvini/VIM/
Licenses: GPL 3+
Build system: r
Synopsis: Variable Importance Measures for Multivariate Random Forests
Description:

Calculates two sets of post-hoc variable importance measures for multivariate random forests. The first set of variable importance measures are given by the sum of mean split improvements for splits defined by feature j measured on user-defined examples (i.e., training or testing samples). The second set of importance measures are calculated on a per-outcome variable basis as the sum of mean absolute difference of node values for each split defined by feature j measured on user-defined examples (i.e., training or testing samples). The user can optionally threshold both sets of importance measures to include only splits that are statistically significant as measured using an F-test.

r-mmconvert 0.12
Propagated dependencies: r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/rqtl/mmconvert
Licenses: GPL 3
Build system: r
Synopsis: Mouse Map Converter
Description:

Convert mouse genome positions between the build 39 physical map and the genetic map of Cox et al. (2009) <doi:10.1534/genetics.109.105486>.

r-mglm 0.2.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MGLM
Licenses: GPL 2+
Build system: r
Synopsis: Multivariate Response Generalized Linear Models
Description:

This package provides functions that (1) fit multivariate discrete distributions, (2) generate random numbers from multivariate discrete distributions, and (3) run regression and penalized regression on the multivariate categorical response data. Implemented models include: multinomial logit model, Dirichlet multinomial model, generalized Dirichlet multinomial model, and negative multinomial model. Making the best of the minorization-maximization (MM) algorithm and Newton-Raphson method, we derive and implement stable and efficient algorithms to find the maximum likelihood estimates. On a multi-core machine, multi-threading is supported.

r-marginalmediation 0.7.3
Propagated dependencies: r-tibble@3.3.0 r-stringr@1.6.0 r-rstudioapi@0.17.1 r-purrr@1.2.0 r-magrittr@2.0.4 r-furniture@1.11.0 r-crayon@1.5.3 r-cli@3.6.5 r-boot@1.3-32
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MarginalMediation
Licenses: GPL 3
Build system: r
Synopsis: Marginal Mediation
Description:

This package provides the ability to perform "Marginal Mediation"--mediation wherein the indirect and direct effects are in terms of the average marginal effects (Bartus, 2005, <https://EconPapers.repec.org/RePEc:tsj:stataj:v:5:y:2005:i:3:p:309-329>). The style of the average marginal effects stems from Thomas Leeper's work on the "margins" package. This framework allows the use of categorical mediators and outcomes with little change in interpretation from the continuous mediators/outcomes. See <doi:10.13140/RG.2.2.18465.92001> for more details on the method.

r-mdccure 0.1.0
Dependencies: tbb@2021.6.0
Propagated dependencies: r-survival@3.8-3 r-smcure@2.2 r-rcppparallel@5.1.11-1 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-npcure@0.1-5 r-gridextra@2.3 r-ggtext@0.1.2 r-ggplot2@4.0.1 r-future-apply@1.20.0 r-future@1.68.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/CastleMon/MDCcure
Licenses: GPL 3
Build system: r
Synopsis: Martingale Dependence Tools and Testing for Mixture Cure Models
Description:

Computes martingale difference correlation (MDC), martingale difference divergence, and their partial extensions to assess conditional mean dependence. The methods are based on Shao and Zhang (2014) <doi:10.1080/01621459.2014.887012>. Additionally, introduces a novel hypothesis test for evaluating covariate effects on the cure rate in mixture cure models, using MDC-based statistics. The methodology is described in Monroy-Castillo et al. (2025, manuscript submitted).

r-multnonparam 1.3.9
Propagated dependencies: r-icsnp@1.1-2
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MultNonParam
Licenses: GPL 2
Build system: r
Synopsis: Multivariate Nonparametric Methods
Description:

This package provides a collection of multivariate nonparametric methods, selected in part to support an MS level course in nonparametric statistical methods. Methods include adjustments for multiple comparisons, implementation of multivariate Mann-Whitney-Wilcoxon testing, inversion of these tests to produce a confidence region, some permutation tests for linear models, and some algorithms for calculating exact probabilities associated with one- and two- stage testing involving Mann-Whitney-Wilcoxon statistics. Supported by grant NSF DMS 1712839. See Kolassa and Seifu (2013) <doi:10.1016/j.acra.2013.03.006>.

r-mote 1.2.2
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/doomlab/MOTE
Licenses: LGPL 3
Build system: r
Synopsis: Effect Size and Confidence Interval Calculator
Description:

Measure of the Effect ('MOTE') is an effect size calculator, including a wide variety of effect sizes in the mean differences family (all versions of d) and the variance overlap family (eta, omega, epsilon, r). MOTE provides non-central confidence intervals for each effect size, relevant test statistics, and output for reporting in APA Style (American Psychological Association, 2010, <ISBN:1433805618>) with LaTeX'. In research, an over-reliance on p-values may conceal the fact that a study is under-powered (Halsey, Curran-Everett, Vowler, & Drummond, 2015 <doi:10.1038/nmeth.3288>). A test may be statistically significant, yet practically inconsequential (Fritz, Scherndl, & Kühberger, 2012 <doi:10.1177/0959354312436870>). Although the American Psychological Association has long advocated for the inclusion of effect sizes (Wilkinson & American Psychological Association Task Force on Statistical Inference, 1999 <doi:10.1037/0003-066X.54.8.594>), the vast majority of peer-reviewed, published academic studies stop short of reporting effect sizes and confidence intervals (Cumming, 2013, <doi:10.1177/0956797613504966>). MOTE simplifies the use and interpretation of effect sizes and confidence intervals.

r-mstknnclust 0.3.2
Propagated dependencies: r-igraph@2.2.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mstknnclust
Licenses: GPL 2
Build system: r
Synopsis: MST-kNN Clustering Algorithm
Description:

This package implements the MST-kNN clustering algorithm which was proposed by Inostroza-Ponta, M. (2008) <https://trove.nla.gov.au/work/28729389?selectedversion=NBD44634158>.

r-multinets 0.2.2
Propagated dependencies: r-rcpp@1.1.0 r-igraph@2.2.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/neylsoncrepalde/multinets
Licenses: GPL 3
Build system: r
Synopsis: Multilevel Networks Analysis
Description:

Analyze multilevel networks as described in Lazega et al (2008) <doi:10.1016/j.socnet.2008.02.001> and in Lazega and Snijders (2016, ISBN:978-3-319-24520-1). The package was developed essentially as an extension to igraph'.

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Total results: 69112