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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-matur 0.0.1.0
Propagated dependencies: r-tidyr@1.3.1 r-magrittr@2.0.4 r-lubridate@1.9.4 r-ggrepel@0.9.6 r-ggplot2@4.0.1 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/josedv82/matuR
Licenses: Expat
Build system: r
Synopsis: Athlete Maturation and Biobanding
Description:

Identifying maturation stages across young athletes is paramount for talent identification. Furthermore, the concept of biobanding, or grouping of athletes based on their biological development, instead of their chronological age, has been widely researched. The goal of this package is to help professionals working in the field of strength & conditioning and talent ID obtain common maturation metrics and as well as to quickly visualize this information via several plotting options. For the methods behind the computed maturation metrics implemented in this package refer to Khamis, H. J., & Roche, A. F. (1994) <https://pubmed.ncbi.nlm.nih.gov/7936860/>, Mirwald, R.L et al., (2002) <https://pubmed.ncbi.nlm.nih.gov/11932580/> and Cumming, Sean P. et al., (2017) <doi:10.1519/SSC.0000000000000281>.

r-mgwrhw 1.1.1.5
Propagated dependencies: r-tidyr@1.3.1 r-spgwr@0.6-37 r-sf@1.0-23 r-psych@2.5.6 r-ggplot2@4.0.1 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=mgwrhw
Licenses: GPL 3
Build system: r
Synopsis: Displays GWR (Geographically Weighted Regression) and Mixed GWR Output and Map
Description:

Display processing results using the GWR (Geographically Weighted Regression) method, display maps, and show the results of the Mixed GWR (Mixed Geographically Weighted Regression) model which automatically selects global variables based on variability between regions. This function refers to Yasin, & Purhadi. (2012). "Mixed Geographically Weighted Regression Model (Case Study the Percentage of Poor Households in Mojokerto 2008)". European Journal of Scientific Research, 188-196. <https://www.researchgate.net/profile/Hasbi-Yasin-2/publication/289689583_Mixed_geographically_weighted_regression_model_case_study_The_percentage_of_poor_households_in_Mojokerto_2008/links/58e46aa40f7e9bbe9c94d641/Mixed-geographically-weighted-regression-model-case-study-The-percentage-of-poor-households-in-Mojokerto-2008.pdf>.

r-maptpx 1.9-7
Propagated dependencies: r-slam@0.1-55
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: http://taddylab.com
Licenses: GPL 3
Build system: r
Synopsis: MAP Estimation of Topic Models
Description:

Maximum a posteriori (MAP) estimation for topic models (i.e., Latent Dirichlet Allocation) in text analysis, as described in Taddy (2012) On estimation and selection for topic models'. Previous versions of this code were included as part of the textir package. If you want to take advantage of openmp parallelization, uncomment the relevant flags in src/MAKEVARS before compiling.

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-moqa 2.0.0
Propagated dependencies: r-readr@2.1.6 r-psych@2.5.6 r-gplots@3.2.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MOQA
Licenses: AGPL 3
Build system: r
Synopsis: Basic Quality Data Assurance for Epidemiological Research
Description:

With the provision of several tools and templates the MOSAIC project (DFG-Grant Number HO 1937/2-1) supports the implementation of a central data management in epidemiological research projects. The MOQA package enables epidemiologists with none or low experience in R to generate basic data quality reports for a wide range of application scenarios. See <https://mosaic-greifswald.de/> for more information. Please read and cite the corresponding open access publication (using the former package-name) in METHODS OF INFORMATION IN MEDICINE by M. Bialke, H. Rau, T. Schwaneberg, R. Walk, T. Bahls and W. Hoffmann (2017) <doi:10.3414/ME16-01-0123>. <https://methods.schattauer.de/en/contents/most-recent-articles/issue/2483/issue/special/manuscript/27573/show.html>.

r-mstdif 0.1.8
Propagated dependencies: r-scdiftest@0.1.1 r-pp@0.6.3-11 r-mirt@1.45.1 r-matrix@1.7-4 r-expm@1.0-0 r-erm@1.0-10
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mstDIF
Licenses: GPL 2 GPL 3
Build system: r
Synopsis: Collection of DIF Tests for Multistage Tests
Description:

This package provides a collection of statistical tests for the detection of differential item functioning (DIF) in multistage tests. Methods entail logistic regression, an adaptation of the simultaneous item bias test (SIBTEST), and various score-based tests. The presented tests provide itemwise test for DIF along categorical, ordinal or metric covariates. Methods for uniform and non-uniform DIF effects are available depending on which method is used.

r-mgwrsar 1.3.2
Propagated dependencies: r-stringr@1.6.0 r-sp@2.2-0 r-smut@1.1 r-sf@1.0-23 r-rlang@1.1.6 r-rhpcblasctl@0.23-42 r-rcppeigen@0.3.4.0.2 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-plotly@4.11.0 r-nabor@0.5.0 r-mgcv@1.9-4 r-mboost@2.9-11 r-matrix@1.7-4 r-mapview@2.11.4 r-magrittr@2.0.4 r-lifecycle@1.0.4 r-knitr@1.50 r-gridextra@2.3 r-ggplot2@4.0.1 r-foreach@1.5.2 r-dplyr@1.1.4 r-doparallel@1.0.17 r-caret@7.0-1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mgwrsar
Licenses: GPL 2+
Build system: r
Synopsis: GWR, Mixed GWR with Spatial Autocorrelation and Multiscale GWR/GTWR (Top-Down Scale Approaches)
Description:

This package provides methods for Geographically Weighted Regression with spatial autocorrelation (Geniaux and Martinetti 2017) <doi:10.1016/j.regsciurbeco.2017.04.001>. Implements Multiscale Geographically Weighted Regression with Top-Down Scale approaches (Geniaux 2026) <doi:10.1007/s10109-025-00481-4>.

r-metricminer 1.0.1
Propagated dependencies: r-yaml@2.3.10 r-tidyr@1.3.1 r-stringr@1.6.0 r-rvest@1.0.5 r-rprojroot@2.1.1 r-purrr@1.2.0 r-openssl@2.3.4 r-magrittr@2.0.4 r-lubridate@1.9.4 r-jsonlite@2.0.0 r-janitor@2.2.1 r-httr@1.4.7 r-googlesheets4@1.1.2 r-googledrive@2.1.2 r-gh@1.5.0 r-getpass@0.2-4 r-dplyr@1.1.4 r-assertthat@0.2.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/ottrproject/metricminer
Licenses: GPL 3
Build system: r
Synopsis: Mine Metrics from Common Places on the Web
Description:

Mine metrics on common places on the web through the power of their APIs (application programming interfaces). It also helps make the data in a format that is easily used for a dashboard or other purposes. There is an associated dashboard template and tutorials that are underdevelopment that help you fully utilize metricminer'.

r-mlr3db 0.7.1
Propagated dependencies: r-r6@2.6.1 r-mlr3misc@0.19.0 r-mlr3@1.2.0 r-data-table@1.17.8 r-checkmate@2.3.3 r-backports@1.5.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://mlr3db.mlr-org.com
Licenses: LGPL 3
Build system: r
Synopsis: Data Base Backend for 'mlr3'
Description:

Extends the mlr3 package with a backend to transparently work with databases such as SQLite', DuckDB', MySQL', MariaDB', or PostgreSQL'. The package provides three additional backends: DataBackendDplyr relies on the abstraction of package dbplyr to interact with most DBMS. DataBackendDuckDB operates on DuckDB data bases and also on Apache Parquet files. DataBackendPolars operates on Polars data frames.

r-mlr3spatiotempcv 2.3.4
Propagated dependencies: r-r6@2.6.1 r-paradox@1.0.1 r-mlr3misc@0.19.0 r-mlr3@1.2.0 r-ggplot2@4.0.1 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://mlr3spatiotempcv.mlr-org.com/
Licenses: LGPL 3
Build system: r
Synopsis: Spatiotemporal Resampling Methods for 'mlr3'
Description:

Extends the mlr3 machine learning framework with spatio-temporal resampling methods to account for the presence of spatiotemporal autocorrelation (STAC) in predictor variables. STAC may cause highly biased performance estimates in cross-validation if ignored. A JSS article is available at <doi:10.18637/jss.v111.i07>.

r-metasurvival 0.1.0
Propagated dependencies: r-survival@3.8-3 r-mvtnorm@1.3-3
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/shubhrampandey/metaSurvival
Licenses: Expat
Build system: r
Synopsis: Meta-Analysis of a Single Survival Curve
Description:

To assess a summary survival curve from survival probabilities and number of at-risk patients collected at various points in time in various studies, and to test the between-strata heterogeneity.

r-multicoap 1.1
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-mass@7.3-65 r-irlba@2.3.5.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/feiyoung/MultiCOAP
Licenses: GPL 3
Build system: r
Synopsis: High-Dimensional Covariate-Augmented Overdispersed Multi-Study Poisson Factor Model
Description:

We introduce factor models designed to jointly analyze high-dimensional count data from multiple studies by extracting study-shared and specified factors. Our factor models account for heterogeneous noises and overdispersion among counts with augmented covariates. We propose an efficient and speedy variational estimation procedure for estimating model parameters, along with a novel criterion for selecting the optimal number of factors and the rank of regression coefficient matrix. More details can be referred to Liu et al. (2024) <doi:10.48550/arXiv.2402.15071>.

r-multilinguer 0.2.4
Propagated dependencies: r-usethis@3.2.1 r-sys@3.4.3 r-rappdirs@0.3.3 r-askpass@1.2.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/mrchypark/multilinguer
Licenses: Expat
Build system: r
Synopsis: Gentle Language Installer for R User
Description:

This package provides install functions of other languages such as java', python'.

r-matlabr 1.5.2
Propagated dependencies: r-stringr@1.6.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=matlabr
Licenses: GPL 2
Build system: r
Synopsis: An Interface for MATLAB using System Calls
Description:

This package provides users to call MATLAB from using the "system" command. Allows users to submit lines of code or MATLAB m files. This is in comparison to R.matlab', which creates a MATLAB server.

r-mixtwice 2.0
Propagated dependencies: r-iso@0.0-21 r-fdrtool@1.2.18 r-ashr@2.2-63 r-alabama@2023.1.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MixTwice
Licenses: GPL 2
Build system: r
Synopsis: Large-Scale Hypothesis Testing by Variance Mixing
Description:

This package implements large-scale hypothesis testing by variance mixing. It takes two statistics per testing unit -- an estimated effect and its associated squared standard error -- and fits a nonparametric, shape-constrained mixture separately on two latent parameters. It reports local false discovery rates (lfdr) and local false sign rates (lfsr). Manuscript describing algorithm of MixTwice: Zheng et al(2021) <doi: 10.1093/bioinformatics/btab162>.

r-mmtsne 0.1.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mmtsne
Licenses: FSDG-compatible FSDG-compatible
Build system: r
Synopsis: Multiple Maps t-SNE
Description:

An implementation of multiple maps t-distributed stochastic neighbor embedding (t-SNE). Multiple maps t-SNE is a method for projecting high-dimensional data into several low-dimensional maps such that non-metric space properties are better preserved than they would be by a single map. Multiple maps t-SNE with only one map is equivalent to standard t-SNE. When projecting onto more than one map, multiple maps t-SNE estimates a set of latent weights that allow each point to contribute to one or more maps depending on similarity relationships in the original data. This implementation is a port of the original Matlab library by Laurens van der Maaten. See Van der Maaten and Hinton (2012) <doi:10.1007/s10994-011-5273-4>. This material is based upon work supported by the United States Air Force and Defense Advanced Research Project Agency (DARPA) under Contract No. FA8750-17-C-0020. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the United States Air Force and Defense Advanced Research Projects Agency. Distribution Statement A: Approved for Public Release; Distribution Unlimited.

r-mave 1.3.12
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-mda@0.5-5
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MAVE
Licenses: GPL 2+
Build system: r
Synopsis: Methods for Dimension Reduction
Description:

This package provides functions for dimension reduction, using MAVE (Minimum Average Variance Estimation), OPG (Outer Product of Gradient) and KSIR (sliced inverse regression of kernel version). Methods for selecting the best dimension are also included. Xia (2002) <doi:10.1111/1467-9868.03411>; Xia (2007) <doi:10.1214/009053607000000352>; Wang (2008) <doi:10.1198/016214508000000418>.

r-mthapower 0.1.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/aurora-mareviv/mthapower
Licenses: GPL 3
Build system: r
Synopsis: Sample Size and Power for Association Studies Involving Mitochondrial DNA Haplogroups
Description:

Calculate Sample Size and Power for Association Studies Involving Mitochondrial DNA Haplogroups. Based on formulae by Samuels et al. AJHG, 2006. 78(4):713-720. <DOI:10.1086/502682>.

r-missr 1.0.1
Propagated dependencies: r-tibble@3.3.0 r-norm@1.0-11.1 r-lifecycle@1.0.4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/NoahHellen/missr
Licenses: Expat
Build system: r
Synopsis: Classify Missing Data as MCAR, MAR, or MNAR
Description:

Classify missing data as missing completely at random (MCAR), missing at random (MAR), or missing not at random (MNAR). This step is required before handling missing data (e.g. mean imputation) so that bias is not introduced. See Little (1988) <doi:10.1080/01621459.1988.10478722> for the statistical rationale for the methods used.

r-mhmmbayes 1.1.1
Propagated dependencies: r-rdpack@2.6.4 r-rcpp@1.1.0 r-mvtnorm@1.3-3 r-mcmcpack@1.7-1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://CRAN.R-project.org/package=mHMMbayes
Licenses: GPL 3
Build system: r
Synopsis: Multilevel Hidden Markov Models Using Bayesian Estimation
Description:

An implementation of the multilevel (also known as mixed or random effects) hidden Markov model using Bayesian estimation in R. The multilevel hidden Markov model (HMM) is a generalization of the well-known hidden Markov model, for the latter see Rabiner (1989) <doi:10.1109/5.18626>. The multilevel HMM is tailored to accommodate (intense) longitudinal data of multiple individuals simultaneously, see e.g., de Haan-Rietdijk et al. <doi:10.1080/00273171.2017.1370364>. Using a multilevel framework, we allow for heterogeneity in the model parameters (transition probability matrix and conditional distribution), while estimating one overall HMM. The model can be fitted on multivariate data with either a categorical, normal, or Poisson distribution, and include individual level covariates (allowing for e.g., group comparisons on model parameters). Parameters are estimated using Bayesian estimation utilizing the forward-backward recursion within a hybrid Metropolis within Gibbs sampler. Missing data (NA) in the dependent variables is accommodated assuming MAR. The package also includes various visualization options, a function to simulate data, and a function to obtain the most likely hidden state sequence for each individual using the Viterbi algorithm.

r-magnamwar 2.0.4
Propagated dependencies: r-survival@3.8-3 r-seqinr@4.2-36 r-qqman@0.1.9 r-plyr@1.8.9 r-multcomp@1.4-29 r-lme4@1.1-37 r-iterators@1.0.14 r-foreach@1.5.2 r-dplyr@1.1.4 r-doparallel@1.0.17 r-coxme@2.2-22 r-ape@5.8-1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MAGNAMWAR
Licenses: Expat
Build system: r
Synopsis: Pipeline for Meta-Genome Wide Association
Description:

Correlates variation within the meta-genome to target species phenotype variations in meta-genome with association studies. Follows the pipeline described in Chaston, J.M. et al. (2014) <doi:10.1128/mBio.01631-14>.

r-munsellinterpol 3.2-0
Propagated dependencies: r-spacesxyz@1.6-0 r-spacesrgb@1.7-0 r-rootsolve@1.8.2.4 r-logger@0.4.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=munsellinterpol
Licenses: GPL 3+
Build system: r
Synopsis: Interpolate Munsell Renotation Data from Hue Value/Chroma to CIE/RGB
Description:

This package provides methods for interpolating data in the Munsell color system following the ASTM D-1535 standard. Hues and chromas with decimal values can be interpolated and converted to/from the Munsell color system and CIE xyY, CIE XYZ, CIE Lab, CIE Luv, or RGB. Includes ISCC-NBS color block lookup. Based on the work by Paul Centore, "The Munsell and Kubelka-Munk Toolbox".

r-maxentvariableselection 1.0-3
Propagated dependencies: r-raster@3.6-32 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=MaxentVariableSelection
Licenses: GPL 2+
Build system: r
Synopsis: Selecting the Best Set of Relevant Environmental Variables along with the Optimal Regularization Multiplier for Maxent Niche Modeling
Description:

Complex niche models show low performance in identifying the most important range-limiting environmental variables and in transferring habitat suitability to novel environmental conditions (Warren and Seifert, 2011 <DOI:10.1890/10-1171.1>; Warren et al., 2014 <DOI:10.1111/ddi.12160>). This package helps to identify the most important set of uncorrelated variables and to fine-tune Maxent's regularization multiplier. In combination, this allows to constrain complexity and increase performance of Maxent niche models (assessed by information criteria, such as AICc (Akaike, 1974 <DOI:10.1109/TAC.1974.1100705>), and by the area under the receiver operating characteristic (AUC) (Fielding and Bell, 1997 <DOI:10.1017/S0376892997000088>). Users of this package should be familiar with Maxent niche modelling.

r-madgrad 0.1.0
Propagated dependencies: r-torch@0.16.3 r-rlang@1.1.6
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=madgrad
Licenses: Expat
Build system: r
Synopsis: 'MADGRAD' Method for Stochastic Optimization
Description:

This package provides a Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic Optimization algorithm. MADGRAD is a best-of-both-worlds optimizer with the generalization performance of stochastic gradient descent and at least as fast convergence as that of Adam, often faster. A drop-in optim_madgrad() implementation is provided based on Defazio et al (2020) <arxiv:2101.11075>.

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