_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
      \_\ \       /  \ \\ \ \   / / /     / /  \
      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
 / / /      / / /___/ / /     \ \ \ /_/\__/ / /
/_/ /      / / /____\/ /       \ \_\\ \/___/ /
\_\/       \/_________/         \/_/ \_____\/

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-mft 2.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MFT
Licenses: GPL 3
Build system: r
Synopsis: The Multiple Filter Test for Change Point Detection
Description:

This package provides statistical tests and algorithms for the detection of change points in time series and point processes - particularly for changes in the mean in time series and for changes in the rate and in the variance in point processes. References - Michael Messer, Marietta Kirchner, Julia Schiemann, Jochen Roeper, Ralph Neininger and Gaby Schneider (2014), A multiple filter test for the detection of rate changes in renewal processes with varying variance <doi:10.1214/14-AOAS782>. Stefan Albert, Michael Messer, Julia Schiemann, Jochen Roeper, Gaby Schneider (2017), Multi-scale detection of variance changes in renewal processes in the presence of rate change points <doi:10.1111/jtsa.12254>. Michael Messer, Kaue M. Costa, Jochen Roeper and Gaby Schneider (2017), Multi-scale detection of rate changes in spike trains with weak dependencies <doi:10.1007/s10827-016-0635-3>. Michael Messer, Stefan Albert and Gaby Schneider (2018), The multiple filter test for change point detection in time series <doi:10.1007/s00184-018-0672-1>. Michael Messer, Hendrik Backhaus, Albrecht Stroh and Gaby Schneider (2019+) Peak detection in time series.

r-micronutr 0.1.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://nutriverse.io/micronutr/
Licenses: GPL 3+
Build system: r
Synopsis: Determining Vitamin and Mineral Status of Populations
Description:

Vitamin and mineral deficiencies continue to be a significant public health problem. This is particularly critical in developing countries where deficiencies to vitamin A, iron, iodine, and other micronutrients lead to adverse health consequences. Cross-sectional surveys are helpful in answering questions related to the magnitude and distribution of deficiencies of selected vitamins and minerals. This package provides tools for calculating and determining select vitamin and mineral deficiencies based on World Health Organization (WHO) guidelines found at <https://www.who.int/teams/nutrition-and-food-safety/databases/vitamin-and-mineral-nutrition-information-system>.

r-moder 0.2.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/lhdjung/moder
Licenses: Expat
Build system: r
Synopsis: Mode Estimation
Description:

Determines single or multiple modes (most frequent values). Checks if missing values make this impossible, and returns NA in this case. Dependency-free source code. See Franzese and Iuliano (2019) <doi:10.1016/B978-0-12-809633-8.20354-3>.

r-mmdcopula 0.2.1
Propagated dependencies: r-wdm@0.2.6 r-vinecopula@2.6.1 r-randtoolbox@2.0.5 r-pbapply@1.7-4 r-cubature@2.1.4-1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MMDCopula
Licenses: GPL 3
Build system: r
Synopsis: Robust Estimation of Copulas by Maximum Mean Discrepancy
Description:

This package provides functions for the robust estimation of parametric families of copulas using minimization of the Maximum Mean Discrepancy, following the article Alquier, Chérief-Abdellatif, Derumigny and Fermanian (2022) <doi:10.1080/01621459.2021.2024836>.

r-mcmapper 0.0.11
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mcmapper
Licenses: Expat
Build system: r
Synopsis: Mapping First Moment and C-Statistic to the Parameters of Distributions for Risk
Description:

This package provides a series of numerical methods for extracting parameters of distributions for risks based on knowing the expected value and c-statistics (e.g., from a published report on the performance of a risk prediction model). This package implements the methodology described in Sadatsafavi et al (2024) <doi:10.48550/arXiv.2409.09178>. The core of the package is mcmap(), which takes a pair of (mean, c-statistic) and the distribution type requested. This function provides a generic interface to more customized functions (mcmap_beta(), mcmap_logitnorm(), mcmap_probitnorm()) for specific distributions.

r-muttest 0.1.0
Propagated dependencies: r-withr@3.0.2 r-treesitter-r@1.2.0 r-treesitter@0.3.1 r-tibble@3.3.0 r-testthat@3.3.0 r-rlang@1.1.6 r-r6@2.6.1 r-purrr@1.2.0 r-fs@1.6.6 r-dplyr@1.1.4 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://jakubsob.github.io/muttest/
Licenses: Expat
Build system: r
Synopsis: Mutation Testing
Description:

Measure quality of your tests. muttest introduces small changes (mutations) to your code and runs your tests to check if they catch the changes. If they do, your tests are good. If not, your assertions are not specific enough. muttest gives you percent score of how often your tests catch the changes.

r-movewindspeed 0.2.4
Propagated dependencies: r-rcpp@1.1.0 r-move@4.2.7
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://gitlab.com/bartk/moveWindSpeed
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Estimate Wind Speeds from Bird Trajectories
Description:

Estimating wind speed from trajectories of individually tracked birds using a maximum likelihood approach.

r-malan 1.0.4
Propagated dependencies: r-tidygraph@1.3.1 r-tibble@3.3.0 r-rcppprogress@0.4.2 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-magrittr@2.0.4 r-igraph@2.2.1 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://mikldk.github.io/malan/
Licenses: GPL 2 FSDG-compatible
Build system: r
Synopsis: MAle Lineage ANalysis
Description:

MAle Lineage ANalysis by simulating genealogies backwards and imposing short tandem repeats (STR) mutations forwards. Intended for forensic Y chromosomal STR (Y-STR) haplotype analyses. Numerous analyses are possible, e.g. number of matches and meiotic distance to matches. Refer to papers mentioned in citation("malan") (DOI's: <doi:10.1371/journal.pgen.1007028>, <doi:10.21105/joss.00684> and <doi:10.1016/j.fsigen.2018.10.004>).

r-memo 1.1.2
Propagated dependencies: r-digest@0.6.39
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=memo
Licenses: Expat
Build system: r
Synopsis: Hashmaps and Memoization (in-Memory Caching of Repeated Computations)
Description:

This package provides a simple in-memory, LRU cache that can be wrapped around any function to memoize it. The cache is keyed on a hash of the input data (using digest') or on pointer equivalence. Also includes a generic hashmap object that can key on any object type.

r-mde 0.3.3
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/Nelson-Gon/mde
Licenses: GPL 3
Build system: r
Synopsis: Missing Data Explorer
Description:

Correct identification and handling of missing data is one of the most important steps in any analysis. To aid this process, mde provides a very easy to use yet robust framework to quickly get an idea of where the missing data lies and therefore find the most appropriate action to take. Graham WJ (2009) <doi:10.1146/annurev.psych.58.110405.085530>.

r-maxlike 0.1-12
Propagated dependencies: r-raster@3.6-32
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=maxlike
Licenses: GPL 3+
Build system: r
Synopsis: Model Species Distributions by Estimating the Probability of Occurrence Using Presence-Only Data
Description:

This package provides a likelihood-based approach to modeling species distributions using presence-only data. In contrast to the popular software program MAXENT, this approach yields estimates of the probability of occurrence, which is a natural descriptor of a species distribution.

r-mvdfa 0.0.4
Propagated dependencies: r-robper@1.2.3 r-pracma@2.4.6 r-pbapply@1.7-4 r-mvtnorm@1.3-3 r-longmemo@1.1-4 r-desolve@1.40
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/jpirmer/mvDFA
Licenses: GPL 3
Build system: r
Synopsis: Multivariate Detrended Fluctuation Analysis
Description:

This R package provides an implementation of multivariate extensions of a well-known fractal analysis technique, Detrended Fluctuations Analysis (DFA; Peng et al., 1995<doi:10.1063/1.166141>), for multivariate time series: multivariate DFA (mvDFA). Several coefficients are implemented that take into account the correlation structure of the multivariate time series to varying degrees. These coefficients may be used to analyze long memory and changes in the dynamic structure that would by univariate DFA. Therefore, this R package aims to extend and complement the original univariate DFA (Peng et al., 1995) for estimating the scaling properties of nonstationary time series.

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-mlim 0.3.0
Propagated dependencies: r-missranger@2.6.1 r-mice@3.18.0 r-memuse@4.2-3 r-md-log@0.2.0 r-h2o@3.44.0.3 r-curl@7.0.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/haghish/mlim
Licenses: Expat
Build system: r
Synopsis: Single and Multiple Imputation with Automated Machine Learning
Description:

Machine learning algorithms have been used for performing single missing data imputation and most recently, multiple imputations. However, this is the first attempt for using automated machine learning algorithms for performing both single and multiple imputation. Automated machine learning is a procedure for fine-tuning the model automatic, performing a random search for a model that results in less error, without overfitting the data. The main idea is to allow the model to set its own parameters for imputing each variable separately instead of setting fixed predefined parameters to impute all variables of the dataset. Using automated machine learning, the package fine-tunes an Elastic Net (default) or Gradient Boosting, Random Forest, Deep Learning, Extreme Gradient Boosting, or Stacked Ensemble machine learning model (from one or a combination of other supported algorithms) for imputing the missing observations. This procedure has been implemented for the first time by this package and is expected to outperform other packages for imputing missing data that do not fine-tune their models. The multiple imputation is implemented via bootstrapping without letting the duplicated observations to harm the cross-validation procedure, which is the way imputed variables are evaluated. Most notably, the package implements automated procedure for handling imputing imbalanced data (class rarity problem), which happens when a factor variable has a level that is far more prevalent than the other(s). This is known to result in biased predictions, hence, biased imputation of missing data. However, the autobalancing procedure ensures that instead of focusing on maximizing accuracy (classification error) in imputing factor variables, a fairer procedure and imputation method is practiced.

r-motbfs 1.4.2
Propagated dependencies: r-quadprog@1.5-8 r-matrix@1.7-4 r-lpsolve@5.6.23 r-ggm@2.5.2 r-bnlearn@5.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MoTBFs
Licenses: LGPL 3
Build system: r
Synopsis: Learning Hybrid Bayesian Networks using Mixtures of Truncated Basis Functions
Description:

Learning, manipulation and evaluation of mixtures of truncated basis functions (MoTBFs), which include mixtures of polynomials (MOPs) and mixtures of truncated exponentials (MTEs). MoTBFs are a flexible framework for modelling hybrid Bayesian networks (I. Pérez-Bernabé, A. Salmerón, H. Langseth (2015) <doi:10.1007/978-3-319-20807-7_36>; H. Langseth, T.D. Nielsen, I. Pérez-Bernabé, A. Salmerón (2014) <doi:10.1016/j.ijar.2013.09.012>; I. Pérez-Bernabé, A. Fernández, R. Rumà , A. Salmerón (2016) <doi:10.1007/s10618-015-0429-7>). The package provides functionality for learning univariate, multivariate and conditional densities, with the possibility of incorporating prior knowledge. Structural learning of hybrid Bayesian networks is also provided. A set of useful tools is provided, including plotting, printing and likelihood evaluation. This package makes use of S3 objects, with two new classes called motbf and jointmotbf'.

r-maicplus 0.1.2
Propagated dependencies: r-survival@3.8-3 r-stringr@1.6.0 r-sandwich@3.1-1 r-matrixstats@1.5.0 r-mass@7.3-65 r-lubridate@1.9.4 r-lmtest@0.9-40 r-boot@1.3-32
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/hta-pharma/maicplus/
Licenses: ASL 2.0
Build system: r
Synopsis: Matching Adjusted Indirect Comparison
Description:

Facilitates performing matching adjusted indirect comparison (MAIC) analysis where the endpoint of interest is either time-to-event (e.g. overall survival) or binary (e.g. objective tumor response). The method is described by Signorovitch et al (2012) <doi:10.1016/j.jval.2012.05.004>.

r-managedcloudprovider 1.0.0
Propagated dependencies: r-jsonlite@2.0.0 r-dockerparallel@1.0.4 r-adagio@0.9.2
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/Jiefei-Wang/ManagedCloudProvider
Licenses: GPL 3
Build system: r
Synopsis: Providing the Kubernetes-Like Functions for the Non-Kubernetes Cloud Service
Description:

Providing the kubernetes-like class ManagedCloudProvider as a child class of the CloudProvider class in the DockerParallel package. The class is able to manage the cloud instance made by the non-kubernetes cloud service. For creating a provider for the non-kubernetes cloud service, the developer needs to define a reference class inherited from ManagedCloudProvider and define the method for the generics runDockerWorkerContainers(), getDockerWorkerStatus() and killDockerWorkerContainers(). For more information, please see the vignette in this package and <https://CRAN.R-project.org/package=DockerParallel>.

r-multirng 1.2.4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MultiRNG
Licenses: GPL 2 GPL 3
Build system: r
Synopsis: Multivariate Pseudo-Random Number Generation
Description:

Pseudo-random number generation for 11 multivariate distributions: Normal, t, Uniform, Bernoulli, Hypergeometric, Beta (Dirichlet), Multinomial, Dirichlet-Multinomial, Laplace, Wishart, and Inverted Wishart. The details of the method are explained in Demirtas (2004) <DOI:10.22237/jmasm/1099268340>.

r-mlrpro 0.1.3
Propagated dependencies: r-mass@7.3-65 r-dplyr@1.1.4 r-dgof@1.5.1 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=mlrpro
Licenses: GPL 3
Build system: r
Synopsis: Stepwise Regression with Assumptions Checking
Description:

The stepwise regression with assumptions checking and the possible Box-Cox transformation.

r-msml 1.0.0.1
Propagated dependencies: r-r2roc@1.0.1 r-r2redux@1.0.18
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/mommy003/MSML
Licenses: GPL 3+
Build system: r
Synopsis: Model Selection Based on Machine Learning (ML)
Description:

Model evaluation based on a modified version of the recursive feature elimination algorithm. This package is designed to determine the optimal model(s) by leveraging all available features.

r-mirtest 2.2
Propagated dependencies: r-mass@7.3-65 r-limma@3.66.0 r-globaltest@5.64.0 r-globalancova@4.28.0 r-corpcor@1.6.10
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://pubmed.ncbi.nlm.nih.gov/22723856/
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Combined miRNA- And mRNA-Testing
Description:

Package for combined miRNA- and mRNA-testing.

r-mstest 0.1.8
Propagated dependencies: r-rlang@1.1.6 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-pso@1.0.4 r-pracma@2.4.6 r-numderiv@2016.8-1.1 r-nloptr@2.2.1 r-gensa@1.1.15 r-ga@3.2.4 r-foreach@1.5.2
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/roga11/MSTest
Licenses: GPL 2+
Build system: r
Synopsis: Hypothesis Testing for Markov Switching Models
Description:

Implementation of hypothesis testing procedures described in Hansen (1992) <doi:10.1002/jae.3950070506>, Carrasco, Hu, & Ploberger (2014) <doi:10.3982/ECTA8609>, Dufour & Luger (2017) <doi:10.1080/07474938.2017.1307548>, and Rodriguez Rondon & Dufour (2024) <https://grodriguezrondon.com/files/RodriguezRondon_Dufour_2025_MonteCarlo_LikelihoodRatioTest_MarkovSwitchingModels_20251014.pdf> that can be used to identify the number of regimes in Markov switching models.

r-moonboot 2.0.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=moonboot
Licenses: FSDG-compatible
Build system: r
Synopsis: m-Out-of-n Bootstrap Functions
Description:

This package provides functions and examples based on the m-out-of-n bootstrap suggested by Politis, D.N. and Romano, J.P. (1994) <doi:10.1214/aos/1176325770>. Additionally there are functions to estimate the scaling factor tau and the subsampling size m. For a detailed description and a full list of references, see Dalitz, C. and Lögler, F. (2025) <doi:10.32614/RJ-2025-031>.

r-morse 3.3.5
Dependencies: jags@4.3.1
Propagated dependencies: r-zoo@1.8-14 r-tidyr@1.3.1 r-tibble@3.3.0 r-rjags@4-17 r-reshape2@1.4.5 r-magrittr@2.0.4 r-gridextra@2.3 r-ggplot2@4.0.1 r-epitools@0.5-10.1 r-dplyr@1.1.4 r-desolve@1.40 r-coda@0.19-4.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://gitlab.in2p3.fr/mosaic-software/morse
Licenses: Expat
Build system: r
Synopsis: Modelling Reproduction and Survival Data in Ecotoxicology
Description:

Advanced methods for a valuable quantitative environmental risk assessment using Bayesian inference of survival and reproduction Data. Among others, it facilitates Bayesian inference of the general unified threshold model of survival (GUTS). See our companion paper Baudrot and Charles (2021) <doi:10.21105/joss.03200>, as well as complementary details in Baudrot et al. (2018) <doi:10.1021/acs.est.7b05464> and Delignette-Muller et al. (2017) <doi:10.1021/acs.est.6b05326>.

Page: 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197198199200201202203204205206207208209210211212213214215216217218219220221222223224225226227228229230231232233234235236237238239240241242243244245246247248249250251252253254255256257258259260261262263264265266267268269270271272273274275276277278279280281282283284285286287288289290291292293294295296297298299300301302303304305306307308309310311312313314315316317318319320321322323324325326327328329330331332333334335336337338339340341342343344345346347348349350351352353354355356357358359360361362363364365366367368369370371372373374375376377378379380381382383384385386387388389390391392393394395396397398399400401402403404405406407408409410411412413414415416417418419420421422423424425426427428429430431432433434435436437438439440441442443444445446447448449450451452453454455456457458459460461462463464465466467468469470471472473474475476477478479480481482483484485486487488489490491492493494495496497498499500501502503504505506507508509510511512513514515516517518519520521522523524525526527528529530531532533534535536537538539540541542543544545546547548549550551552553554555556557558559560561562563564565566567568569570571572573574575576577578579580581582583584585586587588589590591592593594595596597598599600601602603604605606607608609610611612613614615616617618619620621622623624625626627628629630631632633634635636637638639640641642643644645646647648649650651652653654655656657658659660661662663664665666667668669670671672673674675676677678679680681682683684685686687688689690691692693694695696697698699700701702703704705706707708709710711712713714715716717718719720721722723724725726727728729730731732733734735736737738739740741742743744745746747748749750751752753754755756757758759760761762763764765766767768769770771772773774775776777778779780781782783784785786787788789790791792793794795796797798799800801802803804805806807808809810811812813814815816817818819820821822823824825826827828829830831832833834835836837838839840841842843844845846847848849850851852853854855856857858859860861862863864865866867868869870871872873874875876877878879880881882883884885886887888889890891892893894895
Total results: 21457