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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-mortcast 2.8-0
Propagated dependencies: r-wpp2017@1.2-3
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MortCast
Licenses: GPL 2+
Build system: r
Synopsis: Estimation and Projection of Age-Specific Mortality Rates
Description:

Age-specific mortality rates are estimated and projected using the Kannisto, Lee-Carter and related methods as described in Sevcikova et al. (2016) <doi:10.1007/978-3-319-26603-9_15>.

r-mbnmatime 0.2.6
Dependencies: jags@4.3.1
Propagated dependencies: r-zoo@1.8-14 r-scales@1.4.0 r-rjags@4-17 r-reshape2@1.4.5 r-rdpack@2.6.4 r-r2jags@0.8-9 r-png@0.1-8 r-magrittr@2.0.4 r-lspline@1.0-0 r-knitr@1.50 r-igraph@2.2.1 r-gridextra@2.3 r-ggplot2@4.0.1 r-ggdist@3.3.3 r-dplyr@1.1.4 r-crayon@1.5.3 r-checkmate@2.3.3
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://hugaped.github.io/MBNMAtime/
Licenses: GPL 3
Build system: r
Synopsis: Run Time-Course Model-Based Network Meta-Analysis (MBNMA) Models
Description:

Fits Bayesian time-course models for model-based network meta-analysis (MBNMA) that allows inclusion of multiple time-points from studies. Repeated measures over time are accounted for within studies by applying different time-course functions, following the method of Pedder et al. (2019) <doi:10.1002/jrsm.1351>. The method allows synthesis of studies with multiple follow-up measurements that can account for time-course for a single or multiple treatment comparisons. Several general time-course functions are provided; others may be added by the user. Various characteristics can be flexibly added to the models, such as correlation between time points and shared class effects. The consistency of direct and indirect evidence in the network can be assessed using unrelated mean effects models and/or by node-splitting.

r-mcboost 0.4.4
Propagated dependencies: r-rpart@4.1.24 r-rmarkdown@2.30 r-r6@2.6.1 r-mlr3pipelines@0.10.0 r-mlr3misc@0.19.0 r-mlr3@1.2.0 r-glmnet@4.1-10 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://github.com/mlr-org/mcboost
Licenses: LGPL 3+
Build system: r
Synopsis: Multi-Calibration Boosting
Description:

This package implements Multi-Calibration Boosting (2018) <https://proceedings.mlr.press/v80/hebert-johnson18a.html> and Multi-Accuracy Boosting (2019) <doi:10.48550/arXiv.1805.12317> for the multi-calibration of a machine learning model's prediction. MCBoost updates predictions for sub-groups in an iterative fashion in order to mitigate biases like poor calibration or large accuracy differences across subgroups. Multi-Calibration works best in scenarios where the underlying data & labels are unbiased, but resulting models are. This is often the case, e.g. when an algorithm fits a majority population while ignoring or under-fitting minority populations.

r-mdmr 0.5.2
Propagated dependencies: r-lme4@1.1-37 r-compquadform@1.4.4 r-car@3.1-3
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/dmcartor/mdmr
Licenses: GPL 2+
Build system: r
Synopsis: Multivariate Distance Matrix Regression
Description:

Allows users to conduct multivariate distance matrix regression using analytic p-values and compute measures of effect size. For details on the method, see McArtor, Lubke, & Bergeman (2017) <doi:10.1007/s11336-016-9527-8>.

r-mapscanner 0.1.1
Propagated dependencies: r-tibble@3.3.0 r-slippymath@0.3.1 r-sf@1.0-23 r-rniftyreg@2.8.4 r-reproj@0.7.0 r-rcpp@1.1.0 r-raster@3.6-32 r-purrr@1.2.0 r-png@0.1-8 r-pdftools@3.6.0 r-memoise@2.0.1 r-magrittr@2.0.4 r-magick@2.9.0 r-glue@1.8.0 r-fs@1.6.6 r-curl@7.0.0 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/ropensci/mapscanner
Licenses: GPL 3 FreeBSD
Build system: r
Synopsis: Print Maps, Draw on Them, Scan Them Back in
Description:

Enables preparation of maps to be printed and drawn on. Modified maps can then be scanned back in, and hand-drawn marks converted to spatial objects.

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-micvar 0.1.0
Propagated dependencies: r-rdpack@2.6.4 r-matrixcalc@1.0-6 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=micvar
Licenses: GPL 3+
Build system: r
Synopsis: Order Selection in Vector Autoregression by Mean Square Information Criteria
Description:

This package implements order selection for Vector Autoregressive (VAR) models using the Mean Square Information Criterion (MIC). Unlike standard methods such as AIC and BIC, MIC is likelihood-free. This method consistently estimates VAR order and has robust performance under model misspecification. For more details, see Hellstern and Shojaie (2025) <doi:10.48550/arXiv.2511.19761>.

r-ml-msbd 1.2.1
Propagated dependencies: r-foreach@1.5.2 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=ML.MSBD
Licenses: GPL 3
Build system: r
Synopsis: Maximum Likelihood Inference on Multi-State Trees
Description:

Inference of a multi-states birth-death model from a phylogeny, comprising a number of states N, birth and death rates for each state and on which edges each state appears. Inference is done using a hybrid approach: states are progressively added in a greedy approach. For a fixed number of states N the best model is selected via maximum likelihood. Reference: J. Barido-Sottani, T. G. Vaughan and T. Stadler (2018) <doi:10.1098/rsif.2018.0512>.

r-makicoint 1.0.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/merwanroudane/makicoint
Licenses: GPL 3
Build system: r
Synopsis: Maki Cointegration Test with Structural Breaks
Description:

This package implements the Maki (2012) <doi:10.1016/j.econmod.2012.05.006> cointegration test that allows for an unknown number of structural breaks. The test detects cointegration relationships in the presence of up to five structural breaks in the intercept and/or slope coefficients. Four different model specifications are supported: level shifts, level shifts with trend, regime shifts, and trend with regime shifts. The method is described in Maki (2012) "Tests for cointegration allowing for an unknown number of breaks" <doi:10.1016/j.econmod.2012.05.006>.

r-micsim 3.0.0
Propagated dependencies: r-snowfall@1.84-6.3 r-rlecuyer@0.3-8
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MicSim
Licenses: GPL 2+
Build system: r
Synopsis: Performing Continuous-Time Microsimulation
Description:

This toolkit allows performing continuous-time microsimulation for a wide range of life science (demography, social sciences, epidemiology) applications. Individual life-courses are specified by a continuous-time multi-state model as described in Zinn (2014) <doi:10.34196/IJM.00105>.

r-mmac 1.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MMAC
Licenses: GPL 2+
Build system: r
Synopsis: Data for Mathematical Modeling and Applied Calculus
Description:

This package contains the data sets for the first and second editions of the textbook "Mathematical Modeling and Applied Calculus" by Joel Kilty and Alex M. McAllister. The first edition of the book was published by Oxford University Press in 2018 with ISBN-13: 978-019882472. The second edition is expected to be published in January 2027.

r-marked 1.2.8
Propagated dependencies: r-truncnorm@1.0-9 r-tmb@1.9.18 r-rcpp@1.1.0 r-r2admb@0.7.16.3 r-optimx@2025-4.9 r-numderiv@2016.8-1.1 r-matrix@1.7-4 r-lme4@1.1-37 r-knitr@1.50 r-kableextra@1.4.0 r-expm@1.0-0 r-data-table@1.17.8 r-coda@0.19-4.1 r-bookdown@0.45
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=marked
Licenses: GPL 2+
Build system: r
Synopsis: Mark-Recapture Analysis for Survival and Abundance Estimation
Description:

This package provides functions for fitting various models to capture-recapture data including mixed-effects Cormack-Jolly-Seber(CJS) and multistate models and the multi-variate state model structure for survival estimation and POPAN structured Jolly-Seber models for abundance estimation. There are also Hidden Markov model (HMM) implementations of CJS and multistate models with and without state uncertainty and a simulation capability for HMM models.

r-marss 3.11.10
Propagated dependencies: r-nlme@3.1-168 r-mvtnorm@1.3-3 r-kfas@1.6.0 r-generics@0.1.4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://atsa-es.github.io/MARSS/
Licenses: GPL 2
Build system: r
Synopsis: Multivariate Autoregressive State-Space Modeling
Description:

The MARSS package provides maximum-likelihood parameter estimation for constrained and unconstrained linear multivariate autoregressive state-space (MARSS) models, including partially deterministic models. MARSS models are a class of dynamic linear model (DLM) and vector autoregressive model (VAR) model. Fitting available via Expectation-Maximization (EM), BFGS (using optim), and TMB (using the marssTMB companion package). Functions are provided for parametric and innovations bootstrapping, Kalman filtering and smoothing, model selection criteria including bootstrap AICb, confidences intervals via the Hessian approximation or bootstrapping, and all conditional residual types. See the user guide for examples of dynamic factor analysis, dynamic linear models, outlier and shock detection, and multivariate AR-p models. Online workshops (lectures, eBook, and computer labs) at <https://atsa-es.github.io/>.

r-measr 2.0.0
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-stanheaders@2.32.10 r-s7@0.2.1 r-rstantools@2.5.0 r-rstan@2.32.7 r-rlang@1.1.6 r-rdcmchecks@0.1.0 r-rcppparallel@5.1.11-1 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-psych@2.5.6 r-posterior@1.6.1 r-loo@2.8.0 r-lifecycle@1.0.4 r-glue@1.8.0 r-fs@1.6.6 r-dtplyr@1.3.2 r-dplyr@1.1.4 r-dcmstan@0.1.0 r-dcm2@1.0.2 r-cli@3.6.5 r-bridgesampling@1.2-1 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://measr.r-dcm.org
Licenses: GPL 3+
Build system: r
Synopsis: Bayesian Psychometric Measurement Using 'Stan'
Description:

Estimate diagnostic classification models (also called cognitive diagnostic models) with Stan'. Diagnostic classification models are confirmatory latent class models, as described by Rupp et al. (2010, ISBN: 978-1-60623-527-0). Automatically generate Stan code for the general loglinear cognitive diagnostic diagnostic model proposed by Henson et al. (2009) <doi:10.1007/s11336-008-9089-5> and other subtypes that introduce additional model constraints. Using the generated Stan code, estimate the model evaluate the model's performance using model fit indices, information criteria, and reliability metrics.

r-m2r 1.0.3
Propagated dependencies: r-usethis@3.2.1 r-stringr@1.6.0 r-rcpp@1.1.0 r-mpoly@1.1.2 r-memoise@2.0.1 r-gmp@0.7-5
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/coneill-math/m2r
Licenses: GPL 2
Build system: r
Synopsis: Interface to 'Macaulay2'
Description:

Persistent interface to Macaulay2 <https://www.macaulay2.com> and front-end tools facilitating its use in the R ecosystem. For details see Kahle et. al. (2020) <doi:10.18637/jss.v093.i09>.

r-mergingtools 1.0.1
Propagated dependencies: r-tidyr@1.3.1 r-stringr@1.6.0 r-rlang@1.1.6 r-purrr@1.2.0 r-mass@7.3-65 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=mergingTools
Licenses: GPL 3
Build system: r
Synopsis: Tools to Merge Hardware Event Monitors (HEMs) Coming from Separate Subexperiments into One Single Dataframe
Description:

Implementation of two tools to merge Hardware Event Monitors (HEMs) from different subexperiments. Hardware Reading and Merging (HRM), which uses order statistics to merge; and MUlti-Correlation HEM (MUCH) which merges using a multivariate normal distribution. The reference paper for HRM is: S. Vilardell, I. Serra, R. Santalla, E. Mezzetti, J. Abella and F. J. Cazorla, "HRM: Merging Hardware Event Monitors for Improved Timing Analysis of Complex MPSoCs," in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 39, no. 11, pp. 3662-3673, Nov. 2020, <doi:10.1109/TCAD.2020.3013051>. For MUCH: S. Vilardell, I. Serra, E. Mezzetti, J. Abella, and F. J. Cazorla. 2021. "MUCH: exploiting pairwise hardware event monitor correlations for improved timing analysis of complex MPSoCs". In Proceedings of the 36th Annual ACM Symposium on Applied Computing (SAC 21). Association for Computing Machinery. <doi:10.1145/3412841.3441931>. This work has been supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 772773).

r-mertools 0.6.4
Propagated dependencies: r-shiny@1.11.1 r-mvtnorm@1.3-3 r-matrix@1.7-4 r-lme4@1.1-37 r-ggplot2@4.0.1 r-foreach@1.5.2 r-dplyr@1.1.4 r-broom-mixed@0.2.9.6 r-blme@1.0-6 r-arm@1.14-4 r-abind@1.4-8
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=merTools
Licenses: GPL 2+
Build system: r
Synopsis: Tools for Analyzing Mixed Effect Regression Models
Description:

This package provides methods for extracting results from mixed-effect model objects fit with the lme4 package. Allows construction of prediction intervals efficiently from large scale linear and generalized linear mixed-effects models. This method draws from the simulation framework used in the Gelman and Hill (2007) textbook: Data Analysis Using Regression and Multilevel/Hierarchical Models.

r-mlmpower 1.0.10
Propagated dependencies: r-vartestnlme@1.3.5 r-lmertest@3.1-3 r-lme4@1.1-37 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/bkeller2/mlmpower
Licenses: GPL 3
Build system: r
Synopsis: Power Analysis and Data Simulation for Multilevel Models
Description:

This package provides a declarative language for specifying multilevel models, solving for population parameters based on specified variance-explained effect size measures, generating data, and conducting power analyses to determine sample size recommendations. The specification allows for any number of within-cluster effects, between-cluster effects, covariate effects at either level, and random coefficients. Moreover, the models do not assume orthogonal effects, and predictors can correlate at either level and accommodate models with multiple interaction effects.

r-multitraits 0.6.0
Propagated dependencies: r-vegan@2.7-2 r-scatterplot3d@0.3-44 r-rpart@4.1.24 r-rlang@1.1.6 r-magrittr@2.0.4 r-igraph@2.2.1 r-hmisc@5.2-4 r-ggsci@4.1.0 r-ggrepel@0.9.6 r-ggraph@2.2.2 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-corrplot@0.95 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=MultiTraits
Licenses: GPL 3
Build system: r
Synopsis: Analyzing and Visualizing Multidimensional Plant Traits
Description:

This package implements analytical methods for multidimensional plant traits, including Competitors-Stress tolerators-Ruderals strategy analysis using leaf traits, Leaf-Height-Seed strategy analysis, Niche Periodicity Table analysis, and Trait Network analysis. Provides functions for data analysis, visualization, and network metrics calculation. Methods are based on Grime (1974) <doi:10.1038/250026a0>, Pierce et al. (2017) <doi:10.1111/1365-2435.12882>, Westoby (1998) <doi:10.1023/A:1004327224729>, Winemiller et al. (2015) <doi:10.1111/ele.12462>, He et al. (2020) <doi:10.1016/j.tree.2020.06.003>.

r-meltt 0.4.3
Dependencies: python@3.11.14
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-shinyjs@2.1.0 r-shiny@1.11.1 r-reticulate@1.44.1 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-plyr@1.8.9 r-leaflet@2.2.3 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=meltt
Licenses: LGPL 3
Build system: r
Synopsis: Matching Event Data by Location, Time and Type
Description:

Framework for merging and disambiguating event data based on spatiotemporal co-occurrence and secondary event characteristics. It can account for intrinsic "fuzziness" in the coding of events, varying event taxonomies and different geo-precision codes.

r-mvr 1.33.0
Propagated dependencies: r-statmod@1.5.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/jedazard/MVR
Licenses: GPL 3+ FSDG-compatible
Build system: r
Synopsis: Mean-Variance Regularization
Description:

This is a non-parametric method for joint adaptive mean-variance regularization and variance stabilization of high-dimensional data. It is suited for handling difficult problems posed by high-dimensional multivariate datasets (p >> n paradigm). Among those are that the variance is often a function of the mean, variable-specific estimators of variances are not reliable, and tests statistics have low powers due to a lack of degrees of freedom. Key features include: (i) Normalization and/or variance stabilization of the data, (ii) Computation of mean-variance-regularized t-statistics (F-statistics to follow), (iii) Generation of diverse diagnostic plots, (iv) Computationally efficient implementation using C/C++ interfacing and an option for parallel computing to enjoy a faster and easier experience in the R environment.

r-mixedfact 0.1.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mixedfact
Licenses: GPL 3
Build system: r
Synopsis: Generate and Analyze Mixed-Level Blocked Factorial Designs
Description:

Generates blocked designs for mixed-level factorial experiments for a given block size. Internally, it uses finite-field based, collapsed, and heuristic methods to construct block structures that minimize confounding between block effects and factorial effects. The package creates the full treatment combination table, partitions runs into blocks, and computes detailed confounding diagnostics for main effects and two-factor interactions. It also checks orthogonal factorial structure (OFS) and computes efficiencies of factorial effects using the methods of Nair and Rao (1948) <doi:10.1111/j.2517-6161.1948.tb00005.x>. When OFS is not satisfied but the design has equal treatment replications and equal block sizes, a general method based on the C-matrix and custom contrast vectors is used to compute efficiencies. The output includes the generated design, finite-field metadata, confounding summaries, OFS diagnostics, and efficiency results.

r-multivator 1.1-11
Propagated dependencies: r-mvtnorm@1.3-3 r-mathjaxr@1.8-0 r-emulator@1.2-24
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/RobinHankin/multivator
Licenses: GPL 2
Build system: r
Synopsis: Multivariate Emulator
Description:

This package provides a multivariate generalization of the emulator package.

r-minecitrus 1.0.0
Propagated dependencies: 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=mineCitrus
Licenses: GPL 2
Build system: r
Synopsis: Extract and Analyze Median Molecule Intensity from 'citrus' Output
Description:

Citrus is a computational technique developed for the analysis of high dimensional cytometry data sets. This package extracts, statistically analyzes, and visualizes marker expression from citrus data. This code was used to generate data for Figures 3 and 4 in the forthcoming manuscript: Throm et al. â Identification of Enhanced Interferon-Gamma Signaling in Polyarticular Juvenile Idiopathic Arthritis with Mass Cytometryâ , JCI-Insight. For more information on Citrus, please see: Bruggner et al. (2014) <doi:10.1073/pnas.1408792111>. To download the citrus package, please see <https://github.com/nolanlab/citrus>.

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