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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-mires 0.1.1
Propagated dependencies: r-truncnorm@1.0-9 r-stanheaders@2.32.10 r-rstantools@2.5.0 r-rstan@2.32.7 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-pracma@2.4.6 r-nlme@3.1-168 r-mvtnorm@1.3-3 r-logspline@2.1.22 r-hdinterval@0.2.4 r-formula@1.2-5 r-dirichletprocess@0.4.2 r-cubature@2.1.4-1 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MIRES
Licenses: Expat
Build system: r
Synopsis: Measurement Invariance Assessment Using Random Effects Models and Shrinkage
Description:

Estimates random effect latent measurement models, wherein the loadings, residual variances, intercepts, latent means, and latent variances all vary across groups. The random effect variances of the measurement parameters are then modeled using a hierarchical inclusion model, wherein the inclusion of the variances (i.e., whether it is effectively zero or non-zero) is informed by similar parameters (of the same type, or of the same item). This additional hierarchical structure allows the evidence in favor of partial invariance to accumulate more quickly, and yields more certain decisions about measurement invariance. Martin, Williams, and Rast (2020) <doi:10.31234/osf.io/qbdjt>.

r-micropop 1.6
Propagated dependencies: r-visnetwork@2.1.4 r-testthat@3.3.0 r-desolve@1.40
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.12873
Licenses: GPL 3 FSDG-compatible
Build system: r
Synopsis: Process-Based Modelling of Microbial Populations
Description:

Modelling interacting microbial populations - example applications include human gut microbiota, rumen microbiota and phytoplankton. Solves a system of ordinary differential equations to simulate microbial growth and resource uptake over time. This version contains network visualisation functions.

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-mapper 2.4.0
Propagated dependencies: r-fastcluster@1.3.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/Uiowa-Applied-Topology/mappeR
Licenses: Expat
Build system: r
Synopsis: Construct Mapper Graphs for Topological and Exploratory Data Analysis
Description:

Topological data analysis (TDA) is a method of data analysis that uses techniques from topology to analyze high-dimensional data. Here we implement Mapper, an algorithm from this area developed by Singh, Mémoli and Carlsson (2007) which generalizes the concept of a Reeb graph <https://en.wikipedia.org/wiki/Reeb_graph>.

r-mixturefitting 0.6.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MixtureFitting
Licenses: GPL 2
Build system: r
Synopsis: Fitting of Univariate Mixture Distributions to Data using Various Approaches
Description:

This package provides methods for fitting mixture distributions to univariate data using expectation maximization, HWHM and other methods. Supports Gaussian, Cauchy, Student's t and von Mises mixtures. For more details see Merkys (2018) <https://www.lvb.lt/permalink/370LABT_NETWORK/1m6ui06/alma9910036312108451>.

r-manova-rm 0.5.4
Propagated dependencies: r-plyr@1.8.9 r-plotrix@3.8-13 r-multcomp@1.4-29 r-matrix@1.7-4 r-mass@7.3-65 r-magic@1.6-1 r-ellipse@0.5.0 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/smn74/MANOVA.RM
Licenses: GPL 2 GPL 3
Build system: r
Synopsis: Resampling-Based Analysis of Multivariate Data and Repeated Measures Designs
Description:

Implemented are various tests for semi-parametric repeated measures and general MANOVA designs that do neither assume multivariate normality nor covariance homogeneity, i.e., the procedures are applicable for a wide range of general multivariate factorial designs. In addition to asymptotic inference methods, novel bootstrap and permutation approaches are implemented as well. These provide more accurate results in case of small to moderate sample sizes. Furthermore, post-hoc comparisons are provided for the multivariate analyses. Friedrich, S., Konietschke, F. and Pauly, M. (2019) <doi:10.32614/RJ-2019-051>.

r-metabup 0.1.3
Propagated dependencies: r-partitions@1.10-9 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=metabup
Licenses: GPL 3+
Build system: r
Synopsis: Bayesian Meta-Analysis Using Basic Uncertain Pooling
Description:

This package contains functions that allow Bayesian meta-analysis (1) with binomial data, counts(y) and total counts (n) or, (2) with user-supplied point estimates and associated variances. Case (1) provides an analysis based on the logit transformation of the sample proportion. This methodology is also appropriate for combining data from sample surveys and related sources. The functions can calculate the corresponding similarity matrix. More details can be found in Cahoy and Sedransk (2023), Cahoy and Sedransk (2022) <doi:10.1007/s42519-018-0027-2>, Evans and Sedransk (2001) <doi:10.1093/biomet/88.3.643>, and Malec and Sedransk (1992) <doi:10.1093/biomet/79.3.593>.

r-minimaxapprox 0.5.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/aadler/MiniMaxApprox
Licenses: FSDG-compatible
Build system: r
Synopsis: Implementation of Remez Algorithm for Polynomial and Rational Function Approximation
Description:

This package implements the algorithm of Remez (1962) for polynomial minimax approximation and of Cody et al. (1968) <doi:10.1007/BF02162506> for rational minimax approximation.

r-mssearchr 0.2.0
Propagated dependencies: r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://mass-spec.ru/projects/gcmsdata/mssearchr/eng/
Licenses: Expat
Build system: r
Synopsis: Library Search Against Electron Ionization Mass Spectral Databases
Description:

Perform library searches against electron ionization mass spectral databases using either the API provided by MS Search software (<https://chemdata.nist.gov/dokuwiki/doku.php?id=chemdata:nistlibs>) or custom implementations of the Identity and Similarity algorithms.

r-metaggr 0.3.0
Propagated dependencies: 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=metaggR
Licenses: GPL 2
Build system: r
Synopsis: Calculate the Knowledge-Weighted Estimate
Description:

According to a phenomenon known as "the wisdom of the crowds," combining point estimates from multiple judges often provides a more accurate aggregate estimate than using a point estimate from a single judge. However, if the judges use shared information in their estimates, the simple average will over-emphasize this common component at the expense of the judgesâ private information. Asa Palley & Ville Satopää (2021) "Boosting the Wisdom of Crowds Within a Single Judgment Problem: Selective Averaging Based on Peer Predictions" <https://papers.ssrn.com/sol3/Papers.cfm?abstract_id=3504286> proposes a procedure for calculating a weighted average of the judgesâ individual estimates such that resulting aggregate estimate appropriately combines the judges collective information within a single estimation problem. The authors use both simulation and data from six experimental studies to illustrate that the weighting procedure outperforms existing averaging-like methods, such as the equally weighted average, trimmed average, and median. This aggregate estimate -- know as "the knowledge-weighted estimate" -- inputs a) judges estimates of a continuous outcome (E) and b) predictions of others average estimate of this outcome (P). In this R-package, the function knowledge_weighted_estimate(E,P) implements the knowledge-weighted estimate. Its use is illustrated with a simple stylized example and on real-world experimental data.

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-mesonet 0.0.2
Propagated dependencies: r-units@1.0-0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mesonet
Licenses: GPL 2
Build system: r
Synopsis: Download and Process Oklahoma Mesonet Data
Description:

This package provides a collection of functions to download and process weather data from the Oklahoma Mesonet <https://mesonet.org>. Functions are available for downloading station metadata, downloading Mesonet time series (MTS) files, importing MTS files into R, and converting soil temperature change measurements into soil matric potential and volumetric soil moisture.

r-mpwr 0.1.5.1
Propagated dependencies: r-upsetr@1.4.0 r-tidyr@1.3.1 r-tibble@3.3.0 r-stringr@1.6.0 r-purrr@1.2.0 r-plotly@4.11.0 r-magrittr@2.0.4 r-ggplot2@4.0.1 r-forcats@1.0.1 r-flowtracer@0.1.1 r-dplyr@1.1.4 r-data-table@1.17.8 r-comprehenr@0.6.10
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mpwR
Licenses: Expat
Build system: r
Synopsis: Standardized Comparison of Workflows in Mass Spectrometry-Based Bottom-Up Proteomics
Description:

Useful functions to analyze proteomic workflows including number of identifications, data completeness, missed cleavages, quantitative and retention time precision etc. Various software outputs are supported such as ProteomeDiscoverer', Spectronaut', DIA-NN and MaxQuant'.

r-marketmatching 1.2.1
Propagated dependencies: r-zoo@1.8-14 r-utf8@1.2.6 r-tidyr@1.3.1 r-scales@1.4.0 r-reshape2@1.4.5 r-iterators@1.0.14 r-ggplot2@4.0.1 r-foreach@1.5.2 r-dtw@1.23-1 r-dplyr@1.1.4 r-doparallel@1.0.17 r-causalimpact@1.4.1 r-bsts@0.9.11 r-boom@0.9.16
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MarketMatching
Licenses: GPL 3+
Build system: r
Synopsis: Market Matching and Causal Impact Inference
Description:

For a given test market find the best control markets using time series matching and analyze the impact of an intervention. The intervention could be a marketing event or some other local business tactic that is being tested. The workflow implemented in the Market Matching package utilizes dynamic time warping (the dtw package) to do the matching and the CausalImpact package to analyze the causal impact. In fact, this package can be considered a "workflow wrapper" for those two packages. In addition, if you don't have a chosen set of test markets to match, the Market Matching package can provide suggested test/control market pairs and pseudo prospective power analysis (measuring causal impact at fake interventions).

r-mvdalab 1.7
Propagated dependencies: r-sn@2.1.1 r-reshape2@1.4.5 r-plyr@1.8.9 r-penalized@0.9-53 r-moments@0.14.1 r-mass@7.3-65 r-ggplot2@4.0.1 r-car@3.1-3
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mvdalab
Licenses: GPL 3
Build system: r
Synopsis: Multivariate Data Analysis Laboratory
Description:

An open-source implementation of latent variable methods and multivariate modeling tools. The focus is on exploratory analyses using dimensionality reduction methods including low dimensional embedding, classical multivariate statistical tools, and tools for enhanced interpretation of machine learning methods (i.e. intelligible models to provide important information for end-users). Target domains include extension to dedicated applications e.g. for manufacturing process modeling, spectroscopic analyses, and data mining.

r-massextra 1.2.2
Propagated dependencies: r-mass@7.3-65 r-demokde@1.0.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MASSExtra
Licenses: GPL 2 GPL 3
Build system: r
Synopsis: Some 'MASS' Enhancements
Description:

Some enhancements, extensions and additions to the facilities of the recommended MASS package that are useful mainly for teaching purposes, with more convenient default settings and user interfaces. Key functions from MASS are imported and re-exported to avoid masking conflicts. In addition we provide some additional functions mainly used to illustrate coding paradigms and techniques, such as Gramm-Schmidt orthogonalisation and generalised eigenvalue problems.

r-maximininfer 2.0.0
Propagated dependencies: r-sihr@2.1.0 r-mass@7.3-65 r-intervals@0.15.5 r-glmnet@4.1-10 r-cvxr@1.0-15
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MaximinInfer
Licenses: GPL 3
Build system: r
Synopsis: Inference for Maximin Effects in High-Dimensional Settings
Description:

Implementation of the sampling and aggregation method for the covariate shift maximin effect, which was proposed in <arXiv:2011.07568>. It constructs the confidence interval for any linear combination of the high-dimensional maximin effect.

r-multirec 1.0.6
Propagated dependencies: r-survival@3.8-3 r-rfast@2.1.5.2 r-numderiv@2016.8-1.1 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=multiRec
Licenses: GPL 2
Build system: r
Synopsis: Analysis of Multi-Type Recurrent Events
Description:

This package implements likelihood-based estimation and diagnostics for multi-type recurrent event data with dynamic risk that depends on prior events and accommodates terminating events. Methods are described in Ghosh, Chan, Younes and Davis (2023) "A Dynamic Risk Model for Multitype Recurrent Events" <doi:10.1093/aje/kwac213>.

r-msclust 1.0.4
Propagated dependencies: r-psych@2.5.6 r-mvtnorm@1.3-3 r-mnormt@2.1.1 r-mclust@6.1.2 r-matrix@1.7-4 r-gtools@3.9.5 r-ggplot2@4.0.1 r-ggally@2.4.0 r-cluster@2.1.8.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MSclust
Licenses: GPL 2+
Build system: r
Synopsis: Multiple-Scaled Clustering
Description:

Model based clustering using the multivariate multiple Scaled t (MST) and multivariate multiple scaled contaminated normal (MSCN) distributions. The MST is an extension of the multivariate Student-t distribution to include flexible tail behaviors, Forbes, F. & Wraith, D. (2014) <doi:10.1007/s11222-013-9414-4>. The MSCN represents a heavy-tailed generalization of the multivariate normal (MN) distribution to model elliptical contoured scatters in the presence of mild outliers (also referred to as "bad" points) and automatically detect bad points, Punzo, A. & Tortora, C. (2021) <doi:10.1177/1471082X19890935>.

r-mediationsens 0.0.3
Propagated dependencies: r-mediation@4.5.1 r-distr@2.9.7
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mediationsens
Licenses: GPL 2
Build system: r
Synopsis: Simulation-Based Sensitivity Analysis for Causal Mediation Studies
Description:

Simulation-based sensitivity analysis for causal mediation studies. It numerically and graphically evaluates the sensitivity of causal mediation analysis results to the presence of unmeasured pretreatment confounding. The proposed method has primary advantages over existing methods. First, using an unmeasured pretreatment confounder conditional associations with the treatment, mediator, and outcome as sensitivity parameters, the method enables users to intuitively assess sensitivity in reference to prior knowledge about the strength of a potential unmeasured pretreatment confounder. Second, the method accurately reflects the influence of unmeasured pretreatment confounding on the efficiency of estimation of the causal effects. Third, the method can be implemented in different causal mediation analysis approaches, including regression-based, simulation-based, and propensity score-based methods. It is applicable to both randomized experiments and observational studies.

r-mmstat4 0.2.1
Propagated dependencies: r-stringdist@0.9.15 r-shiny@1.11.1 r-rstudioapi@0.17.1 r-rio@1.2.4 r-reticulate@1.44.1 r-rappdirs@0.3.3 r-knitr@1.50 r-httr@1.4.7 r-digest@0.6.39 r-crayon@1.5.3
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mmstat4
Licenses: GPL 3
Build system: r
Synopsis: Access to Teaching Materials from a ZIP File or GitHub
Description:

This package provides access to teaching materials for various statistics courses, including R and Python programs, Shiny apps, data, and PDF/HTML documents. These materials are stored on the Internet as a ZIP file (e.g., in a GitHub repository) and can be downloaded and displayed or run locally. The content of the ZIP file is temporarily or permanently stored. By default, the package uses the GitHub repository sigbertklinke/mmstat4.data. Additionally, the package includes association_measures.R from the archived package ryouready by Mark Heckman and some auxiliary functions.

r-multica 1.2.0
Propagated dependencies: r-multcomp@1.4-29 r-bitops@1.0-9
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/anikoszabo/multiCA
Licenses: GPL 2+
Build system: r
Synopsis: Multinomial Cochran-Armitage Trend Test
Description:

This package implements a generalization of the Cochran-Armitage trend test to multinomial data. In addition to an overall test, multiple testing adjusted p-values for trend in individual outcomes and power calculation is available.

r-mgl 1.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://sites.google.com/a/cs.washington.edu/mgl/
Licenses: GPL 2+
Build system: r
Synopsis: Module Graphical Lasso
Description:

An aggressive dimensionality reduction and network estimation technique for a high-dimensional Gaussian graphical model (GGM). Please refer to: Efficient Dimensionality Reduction for High-Dimensional Network Estimation, Safiye Celik, Benjamin A. Logsdon, Su-In Lee, Proceedings of The 31st International Conference on Machine Learning, 2014, p. 1953--1961.

r-mirnass 1.5
Propagated dependencies: r-rspectra@0.16-2 r-rcpp@1.1.0 r-matrix@1.7-4 r-corelearn@1.57.3.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=miRNAss
Licenses: ASL 2.0
Build system: r
Synopsis: Genome-Wide Discovery of Pre-miRNAs with few Labeled Examples
Description:

Machine learning method specifically designed for pre-miRNA prediction. It takes advantage of unlabeled sequences to improve the prediction rates even when there are just a few positive examples, when the negative examples are unreliable or are not good representatives of its class. Furthermore, the method can automatically search for negative examples if the user is unable to provide them. MiRNAss can find a good boundary to divide the pre-miRNAs from other groups of sequences; it automatically optimizes the threshold that defines the classes boundaries, and thus, it is robust to high class imbalance. Each step of the method is scalable and can handle large volumes of data.

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