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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-miivefa 0.1.2
Propagated dependencies: r-miivsem@0.5.8
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/lluo0/MIIVefa/
Licenses: Expat
Build system: r
Synopsis: Exploratory Factor Analysis Using Model Implied Instrumental Variables
Description:

Data-driven approach for Exploratory Factor Analysis (EFA) that uses Model Implied Instrumental Variables (MIIVs). The method starts with a one factor model and arrives at a suggested model with enhanced interpretability that allows cross-loadings and correlated errors.

r-mvdalab 1.7
Propagated dependencies: r-sn@2.1.1 r-reshape2@1.4.5 r-plyr@1.8.9 r-penalized@0.9-53 r-moments@0.14.1 r-mass@7.3-65 r-ggplot2@4.0.1 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=mvdalab
Licenses: GPL 3
Build system: r
Synopsis: Multivariate Data Analysis Laboratory
Description:

An open-source implementation of latent variable methods and multivariate modeling tools. The focus is on exploratory analyses using dimensionality reduction methods including low dimensional embedding, classical multivariate statistical tools, and tools for enhanced interpretation of machine learning methods (i.e. intelligible models to provide important information for end-users). Target domains include extension to dedicated applications e.g. for manufacturing process modeling, spectroscopic analyses, and data mining.

r-multimix 1.0-10
Propagated dependencies: r-mvtnorm@1.3-3
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/jmcurran/multimix
Licenses: GPL 2+
Build system: r
Synopsis: Fit Mixture Models Using the Expectation Maximisation (EM) Algorithm
Description:

This package provides a set of functions which use the Expectation Maximisation (EM) algorithm (Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977) <doi:10.1111/j.2517-6161.1977.tb01600.x> Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society, 39(1), 1--22) to take a finite mixture model approach to clustering. The package is designed to cluster multivariate data that have categorical and continuous variables and that possibly contain missing values. The method is described in Hunt, L. and Jorgensen, M. (1999) <doi:10.1111/1467-842X.00071> Australian & New Zealand Journal of Statistics 41(2), 153--171 and Hunt, L. and Jorgensen, M. (2003) <doi:10.1016/S0167-9473(02)00190-1> Mixture model clustering for mixed data with missing information, Computational Statistics & Data Analysis, 41(3-4), 429--440.

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-msip 1.3.7
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-ranger@0.17.0 r-prroc@1.4 r-proc@1.19.0.1 r-plyr@1.8.9 r-mice@3.18.0 r-magrittr@2.0.4 r-e1071@1.7-16 r-dplyr@1.1.4 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=MSiP
Licenses: GPL 3
Build system: r
Synopsis: 'MassSpectrometry' Interaction Prediction
Description:

The MSiP is a computational approach to predict protein-protein interactions from large-scale affinity purification mass spectrometry (AP-MS) data. This approach includes both spoke and matrix models for interpreting AP-MS data in a network context. The "spoke" model considers only bait-prey interactions, whereas the "matrix" model assumes that each of the identified proteins (baits and prey) in a given AP-MS experiment interacts with each of the others. The spoke model has a high false-negative rate, whereas the matrix model has a high false-positive rate. Although, both statistical models have merits, a combination of both models has shown to increase the performance of machine learning classifiers in terms of their capabilities in discrimination between true and false positive interactions.

r-massivegst 1.2.4
Propagated dependencies: r-writexls@6.8.0 r-visnetwork@2.1.4 r-igraph@2.2.1 r-formattable@0.2.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: <https://github.com/stefanoMP/massiveGST>
Licenses: GPL 3+
Build system: r
Synopsis: Competitive Gene Sets Test with the Mann-Whitney-Wilcoxon Test
Description:

Friendly implementation of the Mann-Whitney-Wilcoxon test for competitive gene set enrichment analysis.

r-metasubtract 1.60
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MetaSubtract
Licenses: GPL 3+
Build system: r
Synopsis: Subtracting Summary Statistics of One or more Cohorts from Meta-GWAS Results
Description:

If results from a meta-GWAS are used for validation in one of the cohorts that was included in the meta-analysis, this will yield biased (i.e. too optimistic) results. The validation cohort needs to be independent from the meta-Genome-Wide-Association-Study (meta-GWAS) results. MetaSubtract will subtract the results of the respective cohort from the meta-GWAS results analytically without having to redo the meta-GWAS analysis using the leave-one-out methodology. It can handle different meta-analyses methods and takes into account if single or double genomic control correction was applied to the original meta-analysis. It can also handle different meta-analysis methods. It can be used for whole GWAS, but also for a limited set of genetic markers. See for application: Nolte I.M. et al. (2017); <doi: 10.1038/ejhg.2017.50>.

r-mecfda 0.2.1
Propagated dependencies: r-refund@0.1-38 r-quantreg@6.1 r-pracma@2.4.6 r-nlme@3.1-168 r-mgcv@1.9-4 r-matrix@1.7-4 r-mass@7.3-65 r-magrittr@2.0.4 r-lme4@1.1-37 r-gss@2.2-10 r-glme@0.1.0 r-fda@6.3.0 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=MECfda
Licenses: GPL 3
Build system: r
Synopsis: Scalar-on-Function Regression with Measurement Error Correction
Description:

Solve scalar-on-function linear models, including generalized linear mixed effect model and quantile linear regression model, and bias correction estimation methods due to measurement error. Details about the measurement error bias correction methods, see Luan et al. (2023) <doi:10.48550/arXiv.2305.12624>, Tekwe et al. (2022) <doi:10.1093/biostatistics/kxac017>, Zhang et al. (2023) <doi:10.5705/ss.202021.0246>, Tekwe et al. (2019) <doi:10.1002/sim.8179>.

r-mixrf 1.0
Propagated dependencies: r-randomforest@4.7-1.2 r-lme4@1.1-37 r-foreach@1.5.2 r-doparallel@1.0.17
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/randel/MixRF
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Random-Forest-Based Approach for Imputing Clustered Incomplete Data
Description:

It offers random-forest-based functions to impute clustered incomplete data. The package is tailored for but not limited to imputing multitissue expression data, in which a gene's expression is measured on the collected tissues of an individual but missing on the uncollected tissues.

r-meanr 0.1-6
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/wrathematics/meanr
Licenses: FSDG-compatible
Build system: r
Synopsis: Sentiment Analysis Scorer
Description:

Sentiment analysis is a popular technique in text mining that attempts to determine the emotional state of some text. We provide a new implementation of a common method for computing sentiment, whereby words are scored as positive or negative according to a dictionary lookup. Then the sum of those scores is returned for the document. We use the Hu and Liu sentiment dictionary ('Hu and Liu', 2004) <doi:10.1145/1014052.1014073> for determining sentiment. The scoring function is vectorized by document, and scores for multiple documents are computed in parallel via OpenMP'.

r-metsizer 2.0.0
Propagated dependencies: r-vroom@1.6.6 r-shinythemes@1.2.0 r-shiny@1.11.1 r-rfast@2.1.5.2 r-metabolanalyze@1.3.1 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=MetSizeR
Licenses: GPL 3+
Build system: r
Synopsis: Shiny App for Sample Size Estimation in Metabolomic Experiments
Description:

This package provides a Shiny application to estimate the sample size required for a metabolomic experiment to achieve a desired statistical power. Estimation is possible with or without available data from a pilot study.

r-mvhist 1.2
Propagated dependencies: r-simplicialcubature@1.3 r-rgl@1.3.31 r-rcdd@1.6 r-mvmesh@1.6
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mvhist
Licenses: GPL 3+
Build system: r
Synopsis: Multivariate Histograms
Description:

Tabulate and plot directional and other multivariate histograms.

r-mfaces 0.1-4
Propagated dependencies: r-mgcv@1.9-4 r-matrixcalc@1.0-6 r-matrix@1.7-4 r-face@0.1-8
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mfaces
Licenses: GPL 3
Build system: r
Synopsis: Fast Covariance Estimation for Multivariate Sparse Functional Data
Description:

Multivariate functional principal component analysis via fast covariance estimation for multivariate sparse functional data or longitudinal data proposed by Li, Xiao, and Luo (2020) <doi: 10.1002/sta4.245>.

r-metabolanalyze 1.3.1
Propagated dependencies: r-mvtnorm@1.3-3 r-mclust@6.1.2 r-gtools@3.9.5 r-gplots@3.2.0 r-ellipse@0.5.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MetabolAnalyze
Licenses: GPL 2
Build system: r
Synopsis: Probabilistic Latent Variable Models for Metabolomic Data
Description:

Fits probabilistic principal components analysis, probabilistic principal components and covariates analysis and mixtures of probabilistic principal components models to metabolomic spectral data.

r-mixoofa 1.0
Propagated dependencies: r-rsolnp@2.0.1 r-mixexp@1.2.7.1 r-doofa@1.0 r-crossdes@1.1-2 r-combinat@0.0-8
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mixOofA
Licenses: GPL 2+
Build system: r
Synopsis: Design and Analysis of Order-of-Addition Mixture Experiments
Description:

This package provides a facility to generate various classes of fractional designs for order-of-addition experiments namely fractional order-of-additions orthogonal arrays, see Voelkel, Joseph G. (2019). "The design of order-of-addition experiments." Journal of Quality Technology 51:3, 230-241, <doi:10.1080/00224065.2019.1569958>. Provides facility to construct component orthogonal arrays, see Jian-Feng Yang, Fasheng Sun and Hongquan Xu (2020). "A Component Position Model, Analysis and Design for Order-of-Addition Experiments." Technometrics, <doi:10.1080/00401706.2020.1764394>. Supports generation of fractional designs for order-of-addition mixture experiments. Analysis of data from order-of-addition mixture experiments is also supported.

r-mousetrajectory 0.2.1
Propagated dependencies: r-signal@1.8-1 r-lifecycle@1.0.4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/mc-schaaf/mousetRajectory
Licenses: GPL 3+
Build system: r
Synopsis: Mouse Trajectory Analyses for Behavioural Scientists
Description:

Helping psychologists and other behavioural scientists to analyze mouse movement (and other 2-D trajectory) data. Bundles together several functions that compute spatial measures (e.g., maximum absolute deviation, area under the curve, sample entropy) or provide a shorthand for procedures that are frequently used (e.g., time normalization, linear interpolation, extracting initiation and movement times). For more information on these dependent measures, see Wirth et al. (2020) <doi:10.3758/s13428-020-01409-0>.

r-mvpbt 1.2-1
Propagated dependencies: r-mvmeta@1.0.3 r-metafor@4.8-0 r-mass@7.3-65 r-mada@0.5.12
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MVPBT
Licenses: GPL 3
Build system: r
Synopsis: Publication Bias Tests for Meta-Analysis of Diagnostic Accuracy Test
Description:

Generalized Egger tests for detecting publication bias in meta-analysis for diagnostic accuracy test (Noma (2020) <doi:10.1111/biom.13343>, Noma (2022) <doi:10.48550/arXiv.2209.07270>). These publication bias tests are generally more powerful compared with the conventional univariate publication bias tests and can incorporate correlation information between the outcome variables.

r-madrat 3.15.6
Propagated dependencies: r-yaml@2.3.10 r-withr@3.0.2 r-stringi@1.8.7 r-renv@1.1.5 r-pkgload@1.4.1 r-matrix@1.7-4 r-magclass@6.13.2 r-igraph@2.2.1 r-digest@0.6.39 r-callr@3.7.6
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/pik-piam/madrat
Licenses: FreeBSD
Build system: r
Synopsis: May All Data be Reproducible and Transparent (MADRaT) *
Description:

This package provides a framework which should improve reproducibility and transparency in data processing. It provides functionality such as automatic meta data creation and management, rudimentary quality management, data caching, work-flow management and data aggregation. * The title is a wish not a promise. By no means we expect this package to deliver everything what is needed to achieve full reproducibility and transparency, but we believe that it supports efforts in this direction.

r-mcavariants 2.6.1
Propagated dependencies: r-plotly@4.11.0 r-gridextra@2.3 r-ggrepel@0.9.6 r-ggplot2@4.0.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://www.R-project.org
Licenses: GPL 3+
Build system: r
Synopsis: Multiple Correspondence Analysis Variants
Description:

This package provides two variants of multiple correspondence analysis (ca): multiple ca and ordered multiple ca via orthogonal polynomials of Emerson.

r-miscmath 1.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MiscMath
Licenses: GPL 2+
Build system: r
Synopsis: Miscellaneous Mathematical Tools
Description:

Some basic math calculators for finding angles for triangles and for finding the greatest common divisor of two numbers and so on.

r-mfdfa 1.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://mlaib.github.io
Licenses: GPL 3
Build system: r
Synopsis: MultiFractal Detrended Fluctuation Analysis
Description:

This package contains the MultiFractal Detrended Fluctuation Analysis (MFDFA), MultiFractal Detrended Cross-Correlation Analysis (MFXDFA), and the Multiscale Multifractal Analysis (MMA). The MFDFA() function proposed in this package was used in Laib et al. (<doi:10.1016/j.chaos.2018.02.024> and <doi:10.1063/1.5022737>). See references for more information. Interested users can find a parallel version of the MFDFA() function on GitHub.

r-multiwayregression 1.2
Propagated dependencies: 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=MultiwayRegression
Licenses: GPL 3
Build system: r
Synopsis: Perform Tensor-on-Tensor Regression
Description:

This package provides functions to predict one multi-way array (i.e., a tensor) from another multi-way array, using a low-rank CANDECOMP/PARAFAC (CP) factorization and a ridge (L_2) penalty [Lock, EF (2018) <doi:10.1080/10618600.2017.1401544>]. Also includes functions to sample from the Bayesian posterior of a tensor-on-tensor model.

r-multirec 1.0.6
Propagated dependencies: r-survival@3.8-3 r-rfast@2.1.5.2 r-numderiv@2016.8-1.1 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=multiRec
Licenses: GPL 2
Build system: r
Synopsis: Analysis of Multi-Type Recurrent Events
Description:

This package implements likelihood-based estimation and diagnostics for multi-type recurrent event data with dynamic risk that depends on prior events and accommodates terminating events. Methods are described in Ghosh, Chan, Younes and Davis (2023) "A Dynamic Risk Model for Multitype Recurrent Events" <doi:10.1093/aje/kwac213>.

r-mpathr 1.0.4
Propagated dependencies: r-tidyr@1.3.1 r-rlang@1.1.6 r-readr@2.1.6 r-lubridate@1.9.4 r-lifecycle@1.0.4 r-jsonlite@2.0.0 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-cli@3.6.5
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://m-path.io
Licenses: GPL 3+
Build system: r
Synopsis: Easily Handling Data from the ‘m-Path’ Platform
Description:

This package provides tools for importing and cleaning Experience Sampling Method (ESM) data collected via the m-Path platform. The goal is to provide with a few utility functions to be able to read and perform some common operations in ESM data collected through the m-Path platform (<https://m-path.io/landing/>). Functions include raw data handling, format standardization, and basic data checks, as well as to calculate the response rate in data from ESM studies.

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