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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-multiway 1.0-7
Propagated dependencies: r-cmls@1.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=multiway
Licenses: GPL 2+
Build system: r
Synopsis: Component Models for Multi-Way Data
Description:

Fits multi-way component models via alternating least squares algorithms with optional constraints. Fit models include N-way Canonical Polyadic Decomposition, Individual Differences Scaling, Multiway Covariates Regression, Parallel Factor Analysis (1 and 2), Simultaneous Component Analysis, and Tucker Factor Analysis.

r-mixedlevelrsds 1.0.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MixedLevelRSDs
Licenses: GPL 2+
Build system: r
Synopsis: Mixed Level Response Surface Designs
Description:

Response Surface Designs (RSDs) involving factors not all at same levels are called Mixed Level RSDs (or Asymmetric RSDs). In many practical situations, RSDs with asymmetric levels will be more suitable as it explores more regions in the design space. (J.S. Mehta and M.N. Das (1968) <doi:10.2307/1267046>. "Asymmetric rotatable designs and orthogonal transformations").This package contains function named ATORDs_I() for generating asymmetric third order rotatable designs (ATORDs) based on third order designs given by Das and Narasimham (1962). Function ATORDs_II() generates asymmetric third order rotatable designs developed using t-design of unequal set sizes, which are smaller in size as compared to design generated by function ATORDs_I(). In general, third order rotatable designs can be classified into two classes viz., designs that are suitable for sequential experimentation and designs for non-sequential experimentation. The sequential experimentation approach involves conducting the trials step by step whereas, in the non-sequential experimentation approach, the entire runs are executed in one go (M. N. Das and V. Narasimham (1962) <doi:10.1214/AOMS/1177704374>. "Construction of Rotatable Designs through Balanced Incomplete Block Designs"). ATORDs_I() and ATORDs_II() functions generate non-sequential asymmetric third order designs. Function named SeqTORD() generates symmetric sequential third order design in blocks and also gives G-efficiency of the given design. Function named Asymseq() generates asymmetric sequential third order designs in blocks (M. Hemavathi, Eldho Varghese, Shashi Shekhar and Seema Jaggi (2020) <doi:10.1080/02664763.2020.1864817>. "Sequential asymmetric third order rotatable designs (SATORDs)"). In response surface design, situations may arise in which some of the factors are qualitative in nature (Jyoti Divecha and Bharat Tarapara (2017) <doi:10.1080/08982112.2016.1217338>. "Small, balanced, efficient, optimal, and near rotatable response surface designs for factorial experiments asymmetrical in some quantitative, qualitative factors"). The Function named QualRSD() generates second order design with qualitative factors along with their D-efficiency and G-efficiency. The function named RotatabilityQ() calculates a measure of rotatability (measure Q, 0 <= Q <= 1) given by Draper and Pukelshiem(1990) for given a design based on a second order model, (Norman R. Draper and Friedrich Pukelsheim(1990) <doi:10.1080/00401706.1990.10484635>. "Another look at rotatability").

r-mainexistingdatasets 1.0.2
Propagated dependencies: r-tmaptools@3.3 r-tmap@4.2 r-tidyr@1.3.1 r-spdata@2.3.4 r-shiny@1.11.1 r-sf@1.0-23 r-rlang@1.1.6 r-processx@3.8.6 r-pkgload@1.4.1 r-openxlsx@4.2.8.1 r-magrittr@2.0.4 r-htmlwidgets@1.6.4 r-htmltools@0.5.8.1 r-golem@0.5.1 r-glue@1.8.0 r-dt@0.34.0 r-dplyr@1.1.4 r-config@0.3.2
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MainExistingDatasets
Licenses: GPL 3
Build system: r
Synopsis: Main Existing Human Datasets
Description:

Shiny for Open Science to visualize, share, and inventory the main existing human datasets for researchers.

r-mrc 0.1.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/k-dettloff/mRc
Licenses: Expat
Build system: r
Synopsis: Multi-Visit Closed Population Mark-Recapture Estimates
Description:

Compute bootstrap confidence intervals for the adjusted Schnabel and Schumacher-Eschmeyer multi-visit mark-recapture estimators based on Dettloff (2023) <doi:10.1016/j.fishres.2023.106756>.

r-methcon5 0.1.0
Propagated dependencies: r-rlang@1.1.6 r-purrr@1.2.0 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://github.com/EmilHvitfeldt/methcon5
Licenses: Expat
Build system: r
Synopsis: Identify and Rank CpG DNA Methylation Conservation Along the Human Genome
Description:

Identify and rank CpG DNA methylation conservation along the human genome. Specifically it includes bootstrapping methods to provide ranking which should adjust for the differences in length as without it short regions tend to get higher conservation scores.

r-mtvc 1.1.0
Propagated dependencies: r-tidyr@1.3.1 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/egonzato/mtvc
Licenses: Expat
Build system: r
Synopsis: Multiple Counting Process Structure for Survival Analysis
Description:

Counting process structure is fundamental to model time varying covariates. This package restructures dataframes in the counting process format for one or more variables. F. W. Dekker, et al. (2008) <doi:10.1038/ki.2008.328>.

r-mcclust 1.0.1
Propagated dependencies: r-lpsolve@5.6.23
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mcclust
Licenses: GPL 2+
Build system: r
Synopsis: Process an MCMC Sample of Clusterings
Description:

This package implements methods for processing a sample of (hard) clusterings, e.g. the MCMC output of a Bayesian clustering model. Among them are methods that find a single best clustering to represent the sample, which are based on the posterior similarity matrix or a relabelling algorithm.

r-magi 1.2.5
Propagated dependencies: r-roptim@0.1.7 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-gridextra@2.3 r-gridbase@0.4-7 r-desolve@1.40 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://doi.org/10.18637/jss.v109.i04
Licenses: Expat
Build system: r
Synopsis: MAnifold-Constrained Gaussian Process Inference
Description:

This package provides fast and accurate inference for the parameter estimation problem in Ordinary Differential Equations, including the case when there are unobserved system components. Implements the MAGI method (MAnifold-constrained Gaussian process Inference) of Yang, Wong, and Kou (2021) <doi:10.1073/pnas.2020397118>. A user guide is provided by the accompanying software paper Wong, Yang, and Kou (2024) <doi:10.18637/jss.v109.i04>.

r-mt 2.0-1.21
Propagated dependencies: r-randomforest@4.7-1.2 r-pls@2.8-5 r-mass@7.3-65 r-latticeextra@0.6-31 r-lattice@0.22-7 r-ellipse@0.5.0 r-e1071@1.7-16 r-class@7.3-23
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/wanchanglin/mt
Licenses: GPL 2+
Build system: r
Synopsis: Metabolomics Data Analysis Toolbox
Description:

This package provides functions for metabolomics data analysis: data preprocessing, orthogonal signal correction, PCA analysis, PCA-DA analysis, PLS-DA analysis, classification, feature selection, correlation analysis, data visualisation and re-sampling strategies.

r-mllrnrs 0.0.8
Propagated dependencies: r-r6@2.6.1 r-mlexperiments@1.0.0 r-kdry@0.0.3 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/kapsner/mllrnrs
Licenses: GPL 3+
Build system: r
Synopsis: R6-Based ML Learners for 'mlexperiments'
Description:

Enhances mlexperiments <https://CRAN.R-project.org/package=mlexperiments> with additional machine learning ('ML') learners. The package provides R6-based learners for the following algorithms: glmnet <https://CRAN.R-project.org/package=glmnet>, ranger <https://CRAN.R-project.org/package=ranger>, xgboost <https://CRAN.R-project.org/package=xgboost>, and lightgbm <https://CRAN.R-project.org/package=lightgbm>. These can be used directly with the mlexperiments R package.

r-memery 0.6.0
Propagated dependencies: r-sysfonts@0.8.9 r-showtext@0.9-7 r-shinycssloaders@1.1.0 r-shinybs@0.61.1 r-shiny@1.11.1 r-purrr@1.2.0 r-png@0.1-8 r-magrittr@2.0.4 r-jpeg@0.1-11 r-ggplot2@4.0.1 r-cowplot@1.2.0 r-colourpicker@1.3.0 r-cairo@1.7-0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/leonawicz/memery
Licenses: Expat
Build system: r
Synopsis: Internet Memes for Data Analysts
Description:

Generates internet memes that optionally include a superimposed inset plot and other atypical features, combining the visual impact of an attention-grabbing meme with graphic results of data analysis. The package differs from related packages that focus on imitating and reproducing standard memes. Some packages do this by interfacing with online meme generators whereas others achieve this natively. This package takes the latter approach. It does not interface with online meme generators or require any authentication with external websites. It reads images directly from local files or via URL and meme generation is done by the package. While this is similar to the meme package available on CRAN, it differs in that the focus is on allowing for non-standard meme layouts and hybrids of memes mixed with graphs. While this package can be used to make basic memes like an online meme generator would produce, it caters primarily to hybrid graph-meme plots where the meme presentation can be seen as a backdrop highlighting foreground graphs of data analysis results. The package also provides support for an arbitrary number of meme text labels with arbitrary size, position and other attributes rather than restricting to the standard top and/or bottom text placement. This is useful for proper aesthetic interleaving of plots of data between meme image backgrounds and overlain text labels. The package offers a selection of templates for graph placement and appearance with respect to the underlying meme. Graph templates also permit additional template-specific customization. Animated gif support is provided but this is optional and functional only if the magick package is installed. magick is not required unless gif functionality is desired.

r-mvshapirotest 1.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mvShapiroTest
Licenses: GPL 3+
Build system: r
Synopsis: Generalized Shapiro-Wilk test for multivariate normality
Description:

This package implements the generalization of the Shapiro-Wilk test for multivariate normality proposed by Villasenor-Alva and Gonzalez-Estrada (2009).

r-mlmrev 1.0-8
Propagated dependencies: r-lme4@1.1-37
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mlmRev
Licenses: GPL 2+
Build system: r
Synopsis: Examples from Multilevel Modelling Software Review
Description:

Data and examples from a multilevel modelling software review as well as other well-known data sets from the multilevel modelling literature.

r-multibridge 1.3.0
Dependencies: mpfr@4.2.2 gmp@6.3.0
Propagated dependencies: r-stringr@1.6.0 r-rdpack@2.6.4 r-rcpp@1.1.0 r-purrr@1.2.0 r-progress@1.2.3 r-mvtnorm@1.3-3 r-magrittr@2.0.4 r-coda@0.19-4.1 r-brobdingnag@1.2-9
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/asarafoglou/multibridge/
Licenses: GPL 2
Build system: r
Synopsis: Evaluating Multinomial Order Restrictions with Bridge Sampling
Description:

Evaluate hypotheses concerning the distribution of multinomial proportions using bridge sampling. The bridge sampling routine is able to compute Bayes factors for hypotheses that entail inequality constraints, equality constraints, free parameters, and mixtures of all three. These hypotheses are tested against the encompassing hypothesis, that all parameters vary freely or against the null hypothesis that all category proportions are equal. For more information see Sarafoglou et al. (2020) <doi:10.31234/osf.io/bux7p>.

r-multirdpg 1.0.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=multiRDPG
Licenses: GPL 2
Build system: r
Synopsis: Multiple Random Dot Product Graphs
Description:

Fits the Multiple Random Dot Product Graph Model and performs a test for whether two networks come from the same distribution. Both methods are proposed in Nielsen, A.M., Witten, D., (2018) "The Multiple Random Dot Product Graph Model", arXiv preprint <arXiv:1811.12172> (Submitted to Journal of Computational and Graphical Statistics).

r-minimeta 0.3.2
Propagated dependencies: r-writexls@6.8.0 r-shinywidgets@0.9.0 r-shinyjs@2.1.0 r-shiny@1.11.1 r-rhandsontable@0.3.8 r-readxl@1.4.5 r-metafor@4.8-0 r-meta@8.2-1 r-markdown@2.0 r-jsonlite@2.0.0 r-colourpicker@1.3.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/thlytras/miniMeta
Licenses: GPL 2+
Build system: r
Synopsis: Web Application to Run Meta-Analyses
Description:

Shiny web application to run meta-analyses. Essentially a graphical front-end to package meta for R. Can be useful as an educational tool, and for quickly analyzing and sharing meta-analyses. Provides output to quickly fill in GRADE (Grading of Recommendations, Assessment, Development and Evaluations) Summary-of-Findings tables. Importantly, it allows further processing of the results inside R, in case more specific analyses are needed.

r-mlr3spatial 0.6.1
Propagated dependencies: r-terra@1.8-86 r-sf@1.0-23 r-r6@2.6.1 r-mlr3misc@0.19.0 r-mlr3@1.2.0 r-lgr@0.5.0 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://mlr3spatial.mlr-org.com
Licenses: LGPL 3
Build system: r
Synopsis: Support for Spatial Objects Within the 'mlr3' Ecosystem
Description:

Extends the mlr3 ML framework with methods for spatial objects. Data storage and prediction are supported for packages terra', raster and stars'.

r-magickgui 1.3.1
Dependencies: imagemagick@6.9.13-5
Propagated dependencies: r-magick@2.9.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/ShotaOchi/magickGUI
Licenses: GPL 3
Build system: r
Synopsis: GUI Tools for Interactive Image Processing with 'magick'
Description:

Enables us to use the functions of the package magick interactively.

r-mispr 1.0.0
Propagated dependencies: r-penalized@0.9-53 r-mass@7.3-65 r-e1071@1.7-16
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mispr
Licenses: GPL 2
Build system: r
Synopsis: Multiple Imputation with Sequential Penalized Regression
Description:

Generates multivariate imputations using sequential regression with L2 penalty. For more details see Zahid and Heumann (2018) <doi:10.1177/0962280218755574>.

r-mvgps 1.2.2
Propagated dependencies: r-weightit@1.5.1 r-sp@2.2-0 r-rdpack@2.6.4 r-matrixnormal@0.1.2 r-mass@7.3-65 r-geometry@0.5.2 r-gbm@2.2.2 r-cobalt@4.6.2 r-cbps@0.24
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/williazo/mvGPS
Licenses: Expat
Build system: r
Synopsis: Causal Inference using Multivariate Generalized Propensity Score
Description:

This package provides methods for estimating and utilizing the multivariate generalized propensity score (mvGPS) for multiple continuous exposures described in Williams, J.R, and Crespi, C.M. (2020) <arxiv:2008.13767>. The methods allow estimation of a dose-response surface relating the joint distribution of multiple continuous exposure variables to an outcome. Weights are constructed assuming a multivariate normal density for the marginal and conditional distribution of exposures given a set of confounders. Confounders can be different for different exposure variables. The weights are designed to achieve balance across all exposure dimensions and can be used to estimate dose-response surfaces.

r-mixexp 1.2.7.1
Propagated dependencies: r-lattice@0.22-7 r-daewr@1.2-11
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mixexp
Licenses: GPL 2+
Build system: r
Synopsis: Design and Analysis of Mixture Experiments
Description:

This package provides functions for creating designs for mixture experiments, making ternary contour plots, and making mixture effect plots.

r-megena 1.3.7
Propagated dependencies: r-reshape@0.8.10 r-rcpp@1.1.0 r-matrix@1.7-4 r-igraph@2.2.1 r-ggrepel@0.9.6 r-ggraph@2.2.2 r-ggplot2@4.0.1 r-fpc@2.2-13 r-foreach@1.5.2 r-doparallel@1.0.17 r-cluster@2.1.8.1 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/songw01/MEGENA
Licenses: GPL 3+
Build system: r
Synopsis: Multiscale Clustering of Geometrical Network
Description:

Co-Expression Network Analysis by adopting network embedding technique. Song W.-M., Zhang B. (2015) Multiscale Embedded Gene Co-expression Network Analysis. PLoS Comput Biol 11(11): e1004574. <doi: 10.1371/journal.pcbi.1004574>.

r-multibias 1.7.2
Propagated dependencies: r-rlang@1.1.6 r-purrr@1.2.0 r-magrittr@2.0.4 r-lifecycle@1.0.4 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-broom@1.0.10
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/pcbrendel/multibias
Licenses: Expat
Build system: r
Synopsis: Multiple Bias Analysis in Causal Inference
Description:

Quantify the causal effect of a binary exposure on a binary outcome with adjustment for multiple biases. The functions can simultaneously adjust for any combination of uncontrolled confounding, exposure/outcome misclassification, and selection bias. The underlying method generalizes the concept of combining inverse probability of selection weighting with predictive value weighting. Simultaneous multi-bias analysis can be used to enhance the validity and transparency of real-world evidence obtained from observational, longitudinal studies. Based on the work from Paul Brendel, Aracelis Torres, and Onyebuchi Arah (2023) <doi:10.1093/ije/dyad001>.

r-minilnm 0.1.0
Propagated dependencies: r-tidyselect@1.2.1 r-stanheaders@2.32.10 r-rstantools@2.5.0 r-rstan@2.32.7 r-rcppparallel@5.1.11-1 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-posterior@1.6.1 r-glue@1.8.0 r-formula-tools@1.7.1 r-fansi@1.0.7 r-dplyr@1.1.4 r-cli@3.6.5 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/krisrs1128/miniLNM/
Licenses: CC0
Build system: r
Synopsis: Miniature Logistic-Normal Multinomial Models
Description:

Logistic-normal Multinomial (LNM) models are common in problems with multivariate count data. This package gives a simple implementation with a 30 line Stan script. This lightweight implementation makes it an easy starting point for other projects, in particular for downstream tasks that require analysis of "compositional" data. It can be applied whenever a multinomial probability parameter is thought to depend linearly on inputs in a transformed, log ratio space. Additional utilities make it easy to inspect, create predictions, and draw samples using the fitted models. More about the LNM can be found in Xia et al. (2013) "A Logistic Normal Multinomial Regression Model for Microbiome Compositional Data Analysis" <doi:10.1111/biom.12079> and Sankaran and Holmes (2023) "Generative Models: An Interdisciplinary Perspective" <doi:10.1146/annurev-statistics-033121-110134>.

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