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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-muir 0.1.0
Propagated dependencies: r-stringr@1.6.0 r-dplyr@1.1.4 r-diagrammer@1.0.11
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/alforj/muir
Licenses: GPL 2+
Build system: r
Synopsis: Exploring Data with Tree Data Structures
Description:

This package provides a simple tool allowing users to easily and dynamically explore or document a data set using a tree structure.

r-mlquantify 0.2.0
Propagated dependencies: r-randomforest@4.7-1.2 r-fnn@1.1.4.1 r-caret@7.0-1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/andregustavom/mlquantify
Licenses: GPL 2+
Build system: r
Synopsis: Algorithms for Class Distribution Estimation
Description:

Quantification is a prominent machine learning task that has received an increasing amount of attention in the last years. The objective is to predict the class distribution of a data sample. This package is a collection of machine learning algorithms for class distribution estimation. This package include algorithms from different paradigms of quantification. These methods are described in the paper: A. Maletzke, W. Hassan, D. dos Reis, and G. Batista. The importance of the test set size in quantification assessment. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI20, pages 2640â 2646, 2020. <doi:10.24963/ijcai.2020/366>.

r-mlr3superlearner 0.1.2
Propagated dependencies: r-purrr@1.2.0 r-mlr3learners@0.13.0 r-mlr3@1.2.0 r-lgr@0.5.0 r-glmnet@4.1-10 r-data-table@1.17.8 r-cli@3.6.5 r-checkmate@2.3.3
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mlr3superlearner
Licenses: GPL 3+
Build system: r
Synopsis: Super Learner Fitting and Prediction
Description:

An implementation of the Super Learner prediction algorithm from van der Laan, Polley, and Hubbard (2007) <doi:10.2202/1544-6115.1309 using the mlr3 framework.

r-micromodal 1.0.0
Propagated dependencies: r-htmltools@0.5.8.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/kennedymwavu/micromodal
Licenses: Expat
Build system: r
Synopsis: Create Simple and Elegant Modal Dialogs in 'shiny'
Description:

Enables you to create accessible modal dialogs, with confidence and with minimal configuration.

r-methodcompare 1.1.0
Propagated dependencies: r-rockchalk@1.8.157 r-mfp@1.5.5.1 r-matrix@1.7-4 r-lme4@1.1-37 r-estimatr@1.0.6
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/UBERLULU/MethodCompare
Licenses: GPL 3+
Build system: r
Synopsis: Evaluating Bias and Precision in Method Comparison Studies
Description:

Evaluate bias and precision in method comparison studies. One provides measurements for each method and it takes care of the estimates. Multiple plots to evaluate bias, precision and compare methods.

r-multigraphr 0.2.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/termehs/multigraphr
Licenses: Expat
Build system: r
Synopsis: Probability Models and Statistical Analysis of Random Multigraphs
Description:

This package provides methods and models for analysing multigraphs as introduced by Shafie (2015) <doi:10.21307/joss-2019-011>, including methods to study local and global properties <doi:10.1080/0022250X.2016.1219732> and goodness of fit tests.

r-monte-carlo-se 0.1.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=Monte.Carlo.se
Licenses: GPL 3
Build system: r
Synopsis: Monte Carlo Standard Errors
Description:

Computes Monte Carlo standard errors for summaries of Monte Carlo output. Summaries and their standard errors are based on columns of Monte Carlo simulation output. Dennis D. Boos and Jason A. Osborne (2015) <doi:10.1111/insr.12087>.

r-mgarchbekk 0.0.5
Propagated dependencies: r-tseries@0.10-58 r-mvtnorm@1.3-3
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/vst/mgarchBEKK/
Licenses: GPL 3
Build system: r
Synopsis: Simulating, Estimating and Diagnosing MGARCH (BEKK and mGJR) Processes
Description:

Procedures to simulate, estimate and diagnose MGARCH processes of BEKK and multivariate GJR (bivariate asymmetric GARCH model) specification.

r-mkssd 1.2
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mkssd
Licenses: GPL 2+
Build system: r
Synopsis: Efficient Multi-Level k-Circulant Supersaturated Designs
Description:

Generates efficient balanced non-aliased multi-level k-circulant supersaturated designs by interchanging the elements of the generator vector. Attempts to generate a supersaturated design that has chisquare efficiency more than user specified efficiency level (mef). Displays the progress of generation of an efficient multi-level k-circulant design through a progress bar. The progress of 100% means that one full round of interchange is completed. More than one full round (typically 4-5 rounds) of interchange may be required for larger designs.

r-maxmatching 0.1.0
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=maxmatching
Licenses: CC0
Build system: r
Synopsis: Maximum Matching for General Weighted Graph
Description:

Computes the maximum matching for unweighted graph and maximum matching for (un)weighted bipartite graph efficiently.

r-movehmm 1.12
Propagated dependencies: r-sp@2.2-0 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-numderiv@2016.8-1.1 r-mass@7.3-65 r-ggplot2@4.0.1 r-ggmap@4.0.2 r-geosphere@1.5-20 r-boot@1.3-32
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/TheoMichelot/moveHMM
Licenses: GPL 3
Build system: r
Synopsis: Animal Movement Modelling using Hidden Markov Models
Description:

This package provides tools for animal movement modelling using hidden Markov models. These include processing of tracking data, fitting hidden Markov models to movement data, visualization of data and fitted model, decoding of the state process, etc. <doi:10.1111/2041-210X.12578>.

r-matrans 0.2.0
Propagated dependencies: r-quadprog@1.5-8 r-mass@7.3-65 r-glmnet@4.1-10 r-formatr@1.14 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=matrans
Licenses: GPL 3+
Build system: r
Synopsis: Model Averaging-Assisted Optimal Transfer Learning
Description:

Transfer learning, as a prevailing technique in computer sciences, aims to improve the performance of a target model by leveraging auxiliary information from heterogeneous source data. We provide novel tools for multi-source transfer learning under statistical models based on model averaging strategies, including linear regression models, partially linear models. Unlike existing transfer learning approaches, this method integrates the auxiliary information through data-driven weight assignments to avoid negative transfer. This is the first package for transfer learning based on the optimal model averaging frameworks, providing efficient implementations for practitioners in multi-source data modeling. The details are described in Hu and Zhang (2023) <https://jmlr.org/papers/v24/23-0030.html>.

r-megb 0.2
Propagated dependencies: r-mass@7.3-65 r-gbm@2.2.2
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MEGB
Licenses: GPL 2
Build system: r
Synopsis: Gradient Boosting for Longitudinal Data
Description:

Gradient boosting is a powerful statistical learning method known for its ability to model complex relationships between predictors and outcomes while performing inherent variable selection. However, traditional gradient boosting methods lack flexibility in handling longitudinal data where within-subject correlations play a critical role. In this package, we propose a novel approach Mixed Effect Gradient Boosting ('MEGB'), designed specifically for high-dimensional longitudinal data. MEGB incorporates a flexible semi-parametric model that embeds random effects within the gradient boosting framework, allowing it to account for within-individual covariance over time. Additionally, the method efficiently handles scenarios where the number of predictors greatly exceeds the number of observations (p>>n) making it particularly suitable for genomics data and other large-scale biomedical studies.

r-magma-r 1.0.4
Propagated dependencies: r-tidyverse@2.0.0 r-tidyselect@1.2.1 r-tibble@3.3.0 r-stddiff@3.1 r-robumeta@2.1 r-rlang@1.1.6 r-purrr@1.2.0 r-psych@2.5.6 r-overlapping@2.4 r-metafor@4.8-0 r-janitor@2.2.1 r-ggplot2@4.0.1 r-foreach@1.5.2 r-flextable@0.9.10 r-dplyr@1.1.4 r-doparallel@1.0.17
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MAGMA.R
Licenses: GPL 3
Build system: r
Synopsis: MAny-Group MAtching
Description:

Balancing quasi-experimental field research for effects of covariates is fundamental for drawing causal inference. Propensity Score Matching deals with this issue but current techniques are restricted to binary treatment variables. Moreover, they provide several solutions without providing a comprehensive framework on choosing the best model. The MAGMA R-package addresses these restrictions by offering nearest neighbor matching for two to four groups. It also includes the option to match data of a 2x2 design. In addition, MAGMA includes a framework for evaluating the post-matching balance. The package includes functions for the matching process and matching reporting. We provide a tutorial on MAGMA as vignette. More information on MAGMA can be found in Feuchter, M. D., Urban, J., Scherrer V., Breit, M. L., and Preckel F. (2022) <https://osf.io/p47nc/>.

r-multimediate 0.1.4
Propagated dependencies: r-timereg@2.0.7 r-rmutil@1.1.10 r-mvtnorm@1.3-3 r-mass@7.3-65
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://samarafk.github.io/multimediate/
Licenses: GPL 3
Build system: r
Synopsis: Causal Mediation Analysis in Presence of Multiple Mediators Uncausally Related
Description:

Estimates key quantities in causal mediation analysis - including average causal mediation effects (indirect effects), average direct effects, total effects, and proportions mediated - in the presence of multiple uncausally related mediators. Methods are described by Jérolon et al., (2021) <doi:10.1515/ijb-2019-0088> and extended to accommodate survival outcomes as described by Domingo-Relloso et al., (2024) <doi:10.1101/2024.02.16.24302923>.

r-mixkernel 0.9-2
Propagated dependencies: r-vegan@2.7-2 r-reticulate@1.44.1 r-quadprog@1.5-8 r-psych@2.5.6 r-phyloseq@1.54.0 r-mixomics@6.34.0 r-matrix@1.7-4 r-markdown@2.0 r-ldrtools@0.2-2 r-ggplot2@4.0.1 r-corrplot@0.95
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: http://mixkernel.clementine.wf
Licenses: GPL 2+
Build system: r
Synopsis: Omics Data Integration Using Kernel Methods
Description:

Kernel-based methods are powerful methods for integrating heterogeneous types of data. mixKernel aims at providing methods to combine kernel for unsupervised exploratory analysis. Different solutions are provided to compute a meta-kernel, in a consensus way or in a way that best preserves the original topology of the data. mixKernel also integrates kernel PCA to visualize similarities between samples in a non linear space and from the multiple source point of view <doi:10.1093/bioinformatics/btx682>. A method to select (as well as funtions to display) important variables is also provided <doi:10.1093/nargab/lqac014>.

r-maclogp 0.1.1
Propagated dependencies: r-rlist@0.4.6.2 r-plot-matrix@1.6.2 r-bma@3.18.20
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/YuanyuanLi96/maclogp
Licenses: GPL 3+
Build system: r
Synopsis: Measures of Uncertainty for Model Selection
Description:

Following the common types of measures of uncertainty for parameter estimation, two measures of uncertainty were proposed for model selection, see Liu, Li and Jiang (2020) <doi:10.1007/s11749-020-00737-9>. The first measure is a kind of model confidence set that relates to the variation of model selection, called Mac. The second measure focuses on error of model selection, called LogP. They are all computed via bootstrapping. This package provides functions to compute these two measures. Furthermore, a similar model confidence set adapted from Bayesian Model Averaging can also be computed using this package.

r-mitohear 0.1.0
Propagated dependencies: r-rsamtools@2.26.0 r-rlist@0.4.6.2 r-reshape2@1.4.5 r-rdist@0.0.5 r-mcclust@1.0.1 r-magrittr@2.0.4 r-iranges@2.44.0 r-gridextra@2.3 r-ggplot2@4.0.1 r-genomicranges@1.62.0 r-dynamictreecut@1.63-1 r-complexheatmap@2.26.0 r-circlize@0.4.16 r-biostrings@2.78.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MitoHEAR
Licenses: Artistic License 2.0
Build system: r
Synopsis: Quantification of Mitochondrial DNA Heteroplasmy
Description:

Allows the estimation and downstream statistical analysis of the mitochondrial DNA Heteroplasmy calculated from single-cell datasets <https://github.com/ScialdoneLab/MitoHEAR/tree/master>.

r-mifa 0.2.1
Propagated dependencies: r-mice@3.18.0 r-dplyr@1.1.4 r-checkmate@2.3.3
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/teebusch/mifa
Licenses: Expat
Build system: r
Synopsis: Multiple Imputation for Exploratory Factor Analysis
Description:

Impute the covariance matrix of incomplete data so that factor analysis can be performed. Imputations are made using multiple imputation by Multivariate Imputation with Chained Equations (MICE) and combined with Rubin's rules. Parametric Fieller confidence intervals and nonparametric bootstrap confidence intervals can be obtained for the variance explained by different numbers of principal components. The method is described in Nassiri et al. (2018) <doi:10.3758/s13428-017-1013-4>.

r-maicchecks 0.2.0
Propagated dependencies: r-tidyr@1.3.1 r-quadprog@1.5-8 r-lpsolve@5.6.23 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://cran.r-project.org/package=maicChecks
Licenses: GPL 3+
Build system: r
Synopsis: Exact Matching and Matching-Adjusted Indirect Comparison (MAIC)
Description:

The second version (0.2.0) contains implementation for exact matching which is an alternative to propensity score matching (see Glimm & Yau (2025)). The initial version (0.1.2) contains a collection of easy-to-implement tools for checking whether a MAIC can be conducted, as well as an alternative way of calculating weights (see Glimm & Yau (2021) <doi:10.1002/pst.2210>.).

r-mixmeta 1.2.2
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/gasparrini/mixmeta
Licenses: GPL 3+
Build system: r
Synopsis: An Extended Mixed-Effects Framework for Meta-Analysis
Description:

This package provides a collection of functions to perform various meta-analytical models through a unified mixed-effects framework, including standard univariate fixed and random-effects meta-analysis and meta-regression, and non-standard extensions such as multivariate, multilevel, longitudinal, and dose-response models.

r-miceconces 1.0-2
Propagated dependencies: r-systemfit@1.1-30 r-misctools@0.6-28 r-minpack-lm@1.2-4 r-micecon@0.6-20 r-deoptim@2.2-8 r-car@3.1-3
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: http://www.micEcon.org
Licenses: GPL 2+
Build system: r
Synopsis: Analysis with the Constant Elasticity of Substitution (CES) Function
Description:

This package provides tools for econometric analysis and economic modelling with the traditional two-input Constant Elasticity of Substitution (CES) function and with nested CES functions with three and four inputs. The econometric estimation can be done by the Kmenta approximation, or non-linear least-squares using various gradient-based or global optimisation algorithms. Some of these algorithms can constrain the parameters to certain ranges, e.g. economically meaningful values. Furthermore, the non-linear least-squares estimation can be combined with a grid-search for the rho-parameter(s). The estimation methods are described in Henningsen et al. (2021) <doi:10.4337/9781788976480.00030>.

r-mle 1.6
Propagated dependencies: r-rfast2@0.1.5.6 r-rfast@2.1.5.2 r-mn@1.0 r-gp@1.1 r-geppe@1.0 r-directional@7.3 r-compositional@8.1 r-compoissonreg@0.8.1 r-ccd@1.1 r-bivpois@1.1 r-bgfd@0.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MLE
Licenses: GPL 2+
Build system: r
Synopsis: Maximum Likelihood Estimation of Various Univariate and Multivariate Distributions
Description:

Several functions for maximum likelihood estimation of various univariate and multivariate distributions. The list includes more than 100 functions for univariate continuous and discrete distributions, distributions that lie on the real line, the positive line, interval restricted, circular distributions. Further, multivariate continuous and discrete distributions, distributions for compositional and directional data, etc. Some references include Johnson N. L., Kotz S. and Balakrishnan N. (1994). "Continuous Univariate Distributions, Volume 1" <ISBN:978-0-471-58495-7>, Johnson, Norman L. Kemp, Adrianne W. Kotz, Samuel (2005). "Univariate Discrete Distributions". <ISBN:978-0-471-71580-1> and Mardia, K. V. and Jupp, P. E. (2000). "Directional Statistics". <ISBN:978-0-471-95333-3>.

r-mimer 1.0.5
Propagated dependencies: r-tidyr@1.3.1 r-testthat@3.3.0 r-stringr@1.6.0 r-rlang@1.1.6 r-reshape2@1.4.5 r-fuzzyjoin@0.1.6.1 r-dplyr@1.1.4 r-data-table@1.17.8 r-amr@3.0.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MIMER
Licenses: GPL 3+
Build system: r
Synopsis: Data Wrangling for Antimicrobial Resistance Studies
Description:

Designed for analyzing the Medical Information Mart for Intensive Care(MIMIC) dataset, a repository of freely accessible electronic health records. MIMER(MIMIC-enabled Research) package, offers a suite of data wrangling functions tailored specifically for preparing the dataset for research purposes, particularly in antimicrobial resistance(AMR) studies. It simplifies complex data manipulation tasks, allowing researchers to focus on their primary inquiries without being bogged down by wrangling complexities.

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