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      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
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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-minired 1.0.1
Propagated dependencies: r-cpp11@0.5.2
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=minired
Licenses: GPL 3+
Build system: r
Synopsis: R Interface to 'Redatam' Library
Description:

This package is deprecated. Please use redatamx instead. Provides an API to work with Redatam (see <https://redatam.org>) databases in both formats: RXDB (new format) and DICX (old format) and running Redatam programs written in SPC language. It's a wrapper around Redatam core and provides functions to open/close a database (redatam_open()/redatam_close()), list entities and variables from the database (redatam_entities(), redatam_variables()) and execute a SPC program and gets the results as data frames (redatam_query(), redatam_run()).

r-mcgibbsit 1.2.2
Propagated dependencies: r-coda@0.19-4.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/r-gregmisc/mcgibbsit
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Warnes and Raftery's 'MCGibbsit' MCMC Run Length and Convergence Diagnostic
Description:

Implementation of Warnes & Raftery's MCGibbsit run-length and convergence diagnostic for a set of (not-necessarily independent) Markov Chain Monte Carlo (MCMC) samplers. It combines the quantile estimate error-bounding approach of the Raftery and Lewis MCMC run length diagnostic `gibbsit` with the between verses within chain approach of the Gelman and Rubin MCMC convergence diagnostic.

r-morsedr 0.1.2
Propagated dependencies: r-rjags@4-17 r-ggplot2@4.0.1 r-coda@0.19-4.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=morseDR
Licenses: GPL 3+
Build system: r
Synopsis: Bayesian Inference of Binary, Count and Continuous Data in Toxicology
Description:

Advanced methods for a valuable quantitative environmental risk assessment using Bayesian inference of several type of toxicological data. binary (e.g., survival, mobility), count (e.g., reproduction) and continuous (e.g., growth as length, weight). Estimation procedures can be used without a deep knowledge of their underlying probabilistic model or inference methods. Rather, they were designed to behave as well as possible without requiring a user to provide values for some obscure parameters. That said, models can also be used as a first step to tailor new models for more specific situations.

r-mapstats 3.2
Propagated dependencies: r-ttutils@1.0-1.1 r-survey@4.4-8 r-sp@2.2-0 r-sf@1.0-23 r-reshape2@1.4.5 r-rcolorbrewer@1.1-3 r-lattice@0.22-7 r-hmisc@5.2-4 r-colorspace@2.1-2 r-classint@0.4-11
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mapStats
Licenses: GPL 2+
Build system: r
Synopsis: Geographic Display of Survey Data Statistics
Description:

Automated calculation and visualization of survey data statistics on a color-coded (choropleth) map.

r-mindonstats 0.11
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MindOnStats
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Data sets included in Utts and Heckard's Mind on Statistics
Description:

66 data sets that were imported using read.table() where appropriate but more commonly after converting to a csv file for importing via read.csv().

r-mirecsurv 1.0.2
Propagated dependencies: r-survival@3.8-3 r-stringi@1.8.7 r-matrixstats@1.5.0 r-compoissonreg@0.8.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=miRecSurv
Licenses: GPL 2+
Build system: r
Synopsis: Left-Censored Recurrent Events Survival Models
Description:

Fitting recurrent events survival models for left-censored data with multiple imputation of the number of previous episodes. See Hernández-Herrera G, Moriña D, Navarro A. (2020) <arXiv:2007.15031>.

r-metahd 0.1.4
Propagated dependencies: r-tidyr@1.3.1 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-nloptr@2.2.1 r-metapro@1.5.11 r-metap@1.12 r-metafor@4.8-0 r-matrixcalc@1.0-6 r-matrix@1.7-4 r-future-apply@1.20.0 r-dynamictreecut@1.63-1 r-dplyr@1.1.4 r-corpcor@1.6.10
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MetaHD
Licenses: GPL 3
Build system: r
Synopsis: Multivariate Meta-Analysis Model for High-Dimensional Data
Description:

This package performs multivariate meta-analysis for high-dimensional data to integrate and collectively analyse individual-level data from multiple studies, as well as to combine summary estimates. This approach accounts for correlation between outcomes, incorporates withinâ and betweenâ study variability, handles missing values, and uses shrinkage estimation to accommodate high dimensionality. The MetaHD R package provides access to our multivariate meta-analysis approach, along with a comprehensive suite of existing meta-analysis methods, including fixed-effects and random-effects models, Fisherâ s method, Stoufferâ s method, the weighted Z method, Lancasterâ s method, the weighted Fisherâ s method, and vote-counting approach. A detailed vignette with example datasets and code for data preparation and analysis is available at <https://alyshadelivera.github.io/MetaHD_vignette/>.

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-miic 2.0.3
Propagated dependencies: r-scales@1.4.0 r-rcpp@1.1.0 r-ppcor@1.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/miicTeam/miic_R_package
Licenses: GPL 2+
Build system: r
Synopsis: Learning Causal or Non-Causal Graphical Models Using Information Theory
Description:

Multivariate Information-based Inductive Causation, better known by its acronym MIIC, is a causal discovery method, based on information theory principles, which learns a large class of causal or non-causal graphical models from purely observational data, while including the effects of unobserved latent variables. Starting from a complete graph, the method iteratively removes dispensable edges, by uncovering significant information contributions from indirect paths, and assesses edge-specific confidences from randomization of available data. The remaining edges are then oriented based on the signature of causality in observational data. The recent more interpretable MIIC extension (iMIIC) further distinguishes genuine causes from putative and latent causal effects, while scaling to very large datasets (hundreds of thousands of samples). Since the version 2.0, MIIC also includes a temporal mode (tMIIC) to learn temporal causal graphs from stationary time series data. MIIC has been applied to a wide range of biological and biomedical data, such as single cell gene expression data, genomic alterations in tumors, live-cell time-lapse imaging data (CausalXtract), as well as medical records of patients. MIIC brings unique insights based on causal interpretation and could be used in a broad range of other data science domains (technology, climatology, economy, ...). For more information, you can refer to: Simon et al., eLife 2024, <doi:10.1101/2024.02.06.579177>, Ribeiro-Dantas et al., iScience 2024, <doi:10.1016/j.isci.2024.109736>, Cabeli et al., NeurIPS 2021, <https://why21.causalai.net/papers/WHY21_24.pdf>, Cabeli et al., Comput. Biol. 2020, <doi:10.1371/journal.pcbi.1007866>, Li et al., NeurIPS 2019, <https://papers.nips.cc/paper/9573-constraint-based-causal-structure-learning-with-consistent-separating-sets>, Verny et al., PLoS Comput. Biol. 2017, <doi:10.1371/journal.pcbi.1005662>, Affeldt et al., UAI 2015, <https://auai.org/uai2015/proceedings/papers/293.pdf>. Changes from the previous 1.5.3 release on CRAN are available at <https://github.com/miicTeam/miic_R_package/blob/master/NEWS.md>.

r-mdhglm 1.8
Propagated dependencies: r-mvtnorm@1.3-3 r-matrix@1.7-4 r-boot@1.3-32
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mdhglm
Licenses: FSDG-compatible
Build system: r
Synopsis: Multivariate Double Hierarchical Generalized Linear Models
Description:

Allows various models for multivariate response variables where each response is assumed to follow double hierarchical generalized linear models. In double hierarchical generalized linear models, the mean, dispersion parameters for variance of random effects, and residual variance can be further modeled as random-effect models.

r-mma 10.8-1
Propagated dependencies: r-survival@3.8-3 r-lattice@0.22-7 r-gplots@3.2.0 r-gbm@2.2.2 r-car@3.1-3
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://www.r-project.org
Licenses: GPL 2+
Build system: r
Synopsis: Multiple Mediation Analysis
Description:

Used for general multiple mediation analysis. The analysis method is described in Yu and Li (2022) (ISBN: 9780367365479) "Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS", published by Chapman and Hall/CRC; and Yu et al.(2017) <DOI:10.1016/j.sste.2017.02.001> "Exploring racial disparity in obesity: a mediation analysis considering geo-coded environmental factors", published on Spatial and Spatio-temporal Epidemiology, 21, 13-23.

r-msgarch 2.51
Propagated dependencies: r-zoo@1.8-14 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-numderiv@2016.8-1.1 r-mass@7.3-65 r-fanplot@4.0.1 r-expm@1.0-0 r-coda@0.19-4.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/keblu/MSGARCH
Licenses: GPL 2+
Build system: r
Synopsis: Markov-Switching GARCH Models
Description:

Fit (by Maximum Likelihood or MCMC/Bayesian), simulate, and forecast various Markov-Switching GARCH models as described in Ardia et al. (2019) <doi:10.18637/jss.v091.i04>.

r-metamer 0.3.0
Propagated dependencies: r-progress@1.2.3 r-fnn@1.1.4.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://eliocamp.github.io/metamer/
Licenses: GPL 3
Build system: r
Synopsis: Create Data with Identical Statistics
Description:

This package creates data with identical statistics (metamers) using an iterative algorithm proposed by Matejka & Fitzmaurice (2017) <DOI:10.1145/3025453.3025912>.

r-metarange 1.1.4
Propagated dependencies: r-terra@1.8-86 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-r6@2.6.1 r-checkmate@2.3.3
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://metaRange.github.io/metaRange/
Licenses: GPL 3
Build system: r
Synopsis: Framework to Build Mechanistic and Metabolic Constrained Species Distribution Models
Description:

Build spatially and temporally explicit process-based species distribution models, that can include an arbitrary number of environmental factors, species and processes including metabolic constraints and species interactions. The focus of the package is simulating populations of one or multiple species in a grid-based landscape and studying the meta-population dynamics and emergent patterns that arise from the interaction of species under complex environmental conditions. It provides functions for common ecological processes such as negative exponential, kernel-based dispersal (see Nathan et al. (2012) <doi:10.1093/acprof:oso/9780199608898.003.0015>), calculation of the environmental suitability based on cardinal values ( Yin et al. (1995) <doi:10.1016/0168-1923(95)02236-Q>, simplified by Yan and Hunt (1999) <doi:10.1006/anbo.1999.0955> see eq: 4), reproduction in form of an Ricker model (see Ricker (1954) <doi:10.1139/f54-039> and Cabral and Schurr (2010) <doi:10.1111/j.1466-8238.2009.00492.x>), as well as metabolic scaling based on the metabolic theory of ecology (see Brown et al. (2004) <doi:10.1890/03-9000> and Brown, Sibly and Kodric-Brown (2012) <doi:10.1002/9781119968535.ch>).

r-mcl 1.0
Propagated dependencies: r-expm@1.0-0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MCL
Licenses: GPL 2+
Build system: r
Synopsis: Markov Cluster Algorithm
Description:

This package contains the Markov cluster algorithm (MCL) for identifying clusters in networks and graphs. The algorithm simulates random walks on a (n x n) matrix as the adjacency matrix of a graph. It alternates an expansion step and an inflation step until an equilibrium state is reached.

r-mva 1.0-9
Propagated dependencies: r-hsaur2@1.1-21
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: http://dx.doi.org/10.1007/978-1-4419-9650-3
Licenses: GPL 2
Build system: r
Synopsis: An Introduction to Applied Multivariate Analysis with R
Description:

Functions, data sets, analyses and examples from the book `An Introduction to Applied Multivariate Analysis with R (Brian S. Everitt and Torsten Hothorn, Springer, 2011).

r-muvicp 1.3.2
Propagated dependencies: r-sm@2.2-6.0 r-mass@7.3-65 r-gtools@3.9.5
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MuViCP
Licenses: GPL 3
Build system: r
Synopsis: MultiClass Visualizable Classification using Combination of Projections
Description:

An ensemble classifier for multiclass classification. This is a novel classifier that natively works as an ensemble. It projects data on a large number of matrices, and uses very simple classifiers on each of these projections. The results are then combined, ideally via Dempster-Shafer Calculus.

r-multigroupsequential 1.1.0
Propagated dependencies: r-openmx@2.22.10 r-hommel@1.8
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MultiGroupSequential
Licenses: GPL 2+
Build system: r
Synopsis: Group-Sequential Procedures with Multiple Hypotheses
Description:

It is often challenging to strongly control the family-wise type-1 error rate in the group-sequential trials with multiple endpoints (hypotheses). The inflation of type-1 error rate comes from two sources (S1) repeated testing individual hypothesis and (S2) simultaneous testing multiple hypotheses. The MultiGroupSequential package is intended to help researchers to tackle this challenge. The procedures provided include the sequential procedures described in Luo and Quan (2023) <doi:10.1080/19466315.2023.2191989> and the graphical procedure proposed by Maurer and Bretz (2013) <doi:10.1080/19466315.2013.807748>. Luo and Quan (2013) describes three procedures, and the functions to implement these procedures are (1) seqgspgx() implements a sequential graphical procedure based on the group-sequential p-values; (2) seqgsphh() implements a sequential Hochberg/Hommel procedure based on the group-sequential p-values; and (3) seqqvalhh() implements a sequential Hochberg/Hommel procedure based on the q-values. In addition, seqmbgx() implements the sequential graphical procedure described in Maurer and Bretz (2013).

r-mbrm 0.1.1
Propagated dependencies: r-tibble@3.3.0 r-rcpp@1.1.0 r-ggplot2@4.0.1 r-formula@1.2-5 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=MBRM
Licenses: GPL 3
Build system: r
Synopsis: Mixed Regression Models with Generalized Log-Gamma Random Effects
Description:

Multivariate distribution derived from a Bernoulli mixed model under a marginal approach, incorporating a non-normal random intercept whose distribution is assumed to follow a generalized log-gamma (GLG) specification under a particular parameter setting. Estimation is performed by maximizing the log-likelihood using numerical optimization techniques (Lizandra C. Fabio, Vanessa Barros, Cristian Lobos, Jalmar M. F. Carrasco, Marginal multivariate approach: A novel strategy for handling correlated binary outcomes, 2025, under submission).

r-mstudentd 1.1.2
Propagated dependencies: r-rgl@1.3.31 r-mass@7.3-65 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://forgemia.inra.fr/imhorphen/mstudentd
Licenses: GPL 3+
Build system: r
Synopsis: Multivariate t Distribution
Description:

Distance between multivariate t distributions, as presented by N. Bouhlel and D. Rousseau (2023) <doi:10.1109/LSP.2023.3324594>.

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-mhqol 0.14.0
Propagated dependencies: r-writexl@1.5.4 r-tidyr@1.3.1 r-shinyalert@3.1.0 r-shiny@1.11.1 r-fmsb@0.7.6 r-dt@0.34.0 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://cran.r-project.org/package=MHQoL
Licenses: Expat
Build system: r
Synopsis: Mental Health Quality of Life Toolkit
Description:

Transforms, calculates, and presents results from the Mental Health Quality of Life Questionnaire (MHQoL), a measure of health-related quality of life for individuals with mental health conditions. Provides scoring functions, summary statistics, and visualization tools to facilitate interpretation. For more details see van Krugten et al.(2022) <doi:10.1007/s11136-021-02935-w>.

r-mfx 1.2-4
Propagated dependencies: r-sandwich@3.1-1 r-mass@7.3-65 r-lmtest@0.9-40 r-betareg@3.2-4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mfx
Licenses: GPL 2 GPL 3
Build system: r
Synopsis: Marginal Effects, Odds Ratios and Incidence Rate Ratios for GLMs
Description:

Estimates probit, logit, Poisson, negative binomial, and beta regression models, returning their marginal effects, odds ratios, or incidence rate ratios as an output. Greene (2008, pp. 780-7) provides a textbook introduction to this topic.

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>.

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