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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-mrbin 1.9.4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/kleinomicslab/mrbin
Licenses: GPL 3
Build system: r
Synopsis: Metabolomics Data Analysis Functions
Description:

This package provides a collection of functions for processing and analyzing metabolite data. The namesake function mrbin() converts 1D or 2D Nuclear Magnetic Resonance data into a matrix of values suitable for further data analysis and performs basic processing steps in a reproducible way. Negative values, a common issue in such data, can be replaced by positive values (<doi:10.1021/acs.jproteome.0c00684>). All used parameters are stored in a readable text file and can be restored from that file to enable exact reproduction of the data at a later time. The function fia() ranks features according to their impact on classifier models, especially artificial neural network models.

r-malaytextr 0.1.3
Propagated dependencies: r-stringr@1.6.0 r-rlang@1.1.6 r-magrittr@2.0.4 r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/zahiernasrudin/malaytextr
Licenses: Expat
Build system: r
Synopsis: Text Mining for Bahasa Malaysia
Description:

It is designed to work with text written in Bahasa Malaysia. We provide functions and data sets that will make working with Bahasa Malaysia text much easier. For word stemming in particular, we will look up the Malay words in a dictionary and then proceed to remove "extra suffix" as explained in Khan, Rehman Ullah, Fitri Suraya Mohamad, Muh Inam UlHaq, Shahren Ahmad Zadi Adruce, Philip Nuli Anding, Sajjad Nawaz Khan, and Abdulrazak Yahya Saleh Al-Hababi (2017) <https://ijrest.net/vol-4-issue-12.html> . This package includes a dictionary of Malay words that may be used to perform word stemming, a dataset of Malay stop words, a dataset of sentiment words and a dataset of normalized words.

r-mobsim 0.3.2
Propagated dependencies: r-vegan@2.7-2 r-sads@0.6.5 r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/MoBiodiv/mobsim
Licenses: GPL 3+
Build system: r
Synopsis: Spatial Simulation and Scale-Dependent Analysis of Biodiversity Changes
Description:

Simulation, analysis and sampling of spatial biodiversity data (May, Gerstner, McGlinn, Xiao & Chase 2017) <doi:10.1111/2041-210x.12986>. In the simulation tools user define the numbers of species and individuals, the species abundance distribution and species aggregation. Functions for analysis include species rarefaction and accumulation curves, species-area relationships and the distance decay of similarity.

r-mafld 4.0.0
Propagated dependencies: r-dplyr@1.1.4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/jagadishramasamy/mafld
Licenses: GPL 3+
Build system: r
Synopsis: Diagnosis of Metabolic Dysfunction Associated Fatty Liver Disease
Description:

The latest guidelines proposed by International Expert Consensus are used for the clinical diagnosis of Metabolic Associated Fatty Liver Disease (MAFLD). The new definition takes hepatic steatosis (determined by elastography or histology or biomarker-based fatty liver index) as a major criterion. In addition, race, gender, body mass index (BMI), waist circumference (WC), fasting plasma glucose (FPG), systolic blood pressure (SBP), diastolic blood pressure (DBP), triglycerides (TG), high-density lipoprotein cholesterol (HDLC), homeostatic model assessment of insulin resistance (HOMAIR), high sensitive c-reactive protein (HsCRP) for the diagnosis of MAFLD. Each parameter has to be interpreted based on the proposed cut-offs, making the diagnosis slightly complex and error-prone. This package is developed by incorporating the latest international expert consensus guidelines, and it will aid in the easy and quick diagnosis of MAFLD based on FibroScan in busy healthcare settings and also for research purposes. The new definition for MAFLD as per the International Consensus Statement is described by Eslam M et al (2020). <doi:10.1016/j.jhep.2020.03.039>.

r-mggd 1.3.3
Propagated dependencies: r-rgl@1.3.31 r-mass@7.3-65 r-lifecycle@1.0.4 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/mggd
Licenses: GPL 3+
Build system: r
Synopsis: Multivariate Generalised Gaussian Distribution; Kullback-Leibler Divergence
Description:

Distance between multivariate generalised Gaussian distributions, as presented by N. Bouhlel and A. Dziri (2019) <doi:10.1109/LSP.2019.2915000>. Manipulation of multivariate generalised Gaussian distributions (methods presented by Gomez, Gomez-Villegas and Marin (1998) <doi:10.1080/03610929808832115> and Pascal, Bombrun, Tourneret and Berthoumieu (2013) <doi:10.1109/TSP.2013.2282909>).

r-mixedbiastest 1.0.2
Propagated dependencies: r-rlang@1.1.6 r-matrix@1.7-4 r-lme4@1.1-37 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=mixedbiastest
Licenses: GPL 3
Build system: r
Synopsis: Bias Diagnostic for Linear Mixed Models
Description:

This package provides a function to perform bias diagnostics on linear mixed models fitted with lmer() from the lme4 package. Implements permutation tests for assessing the bias of fixed effects, as described in Karl and Zimmerman (2021) <doi:10.1016/j.jspi.2020.06.004>. Karl and Zimmerman (2020) <doi:10.17632/tmynggddfm.1> provide R code for implementing the test using mvglmmRank output. Development of this package was assisted by GPT o1-preview for code structure and documentation.

r-mixedpoisson 2.0
Propagated dependencies: r-rmpfr@1.1-2 r-mass@7.3-65 r-gaussquad@1.0-3
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MixedPoisson
Licenses: GPL 2
Build system: r
Synopsis: Mixed Poisson Models
Description:

The estimation of the parameters in mixed Poisson models.

r-meta4diag 2.1.1
Propagated dependencies: r-sp@2.2-0 r-shinybs@0.61.1 r-shiny@1.11.1 r-catools@1.18.3
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=meta4diag
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Meta-Analysis for Diagnostic Test Studies
Description:

Bayesian inference analysis for bivariate meta-analysis of diagnostic test studies using integrated nested Laplace approximation with INLA. A purpose built graphic user interface is available. The installation of R package INLA is compulsory for successful usage. The INLA package can be obtained from <https://www.r-inla.org>. We recommend the testing version, which can be downloaded by running: install.packages("INLA", repos=c(getOption("repos"), INLA="https://inla.r-inla-download.org/R/testing"), dep=TRUE).

r-mlpugs 0.2.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/bearloga/MLPUGS
Licenses: Expat
Build system: r
Synopsis: Multi-Label Prediction Using Gibbs Sampling (and Classifier Chains)
Description:

An implementation of classifier chains (CC's) for multi-label prediction. Users can employ an external package (e.g. randomForest', C50'), or supply their own. The package can train a single set of CC's or train an ensemble of CC's -- in parallel if running in a multi-core environment. New observations are classified using a Gibbs sampler since each unobserved label is conditioned on the others. The package includes methods for evaluating the predictions for accuracy and aggregating across iterations and models to produce binary or probabilistic classifications.

r-metasvr 0.1.0
Propagated dependencies: r-hms@1.1.4 r-e1071@1.7-16
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/rechtianaputri/metaSVR
Licenses: GPL 3+
Build system: r
Synopsis: Support Vector Regression with Metaheuristic Algorithms Optimization
Description:

This package provides a hybrid modeling framework combining Support Vector Regression (SVR) with metaheuristic optimization algorithms, including the Archimedes Optimization Algorithm (AO) (Hashim et al. (2021) <doi:10.1007/s10489-020-01893-z>), Coot Bird Optimization (CBO) (Naruei & Keynia (2021) <doi:10.1016/j.eswa.2021.115352>), and their hybrid (AOCBO), as well as several others such as Harris Hawks Optimization (HHO) (Heidari et al. (2019) <doi:10.1016/j.future.2019.02.028>), Gray Wolf Optimizer (GWO) (Mirjalili et al. (2014) <doi:10.1016/j.advengsoft.2013.12.007>), Ant Lion Optimization (ALO) (Mirjalili (2015) <doi:10.1016/j.advengsoft.2015.01.010>), and Enhanced Harris Hawk Optimization with Coot Bird Optimization (EHHOCBO) (Cui et al. (2023) <doi:10.32604/cmes.2023.026019>). The package enables automatic tuning of SVR hyperparameters (cost, gamma, and epsilon) to enhance prediction performance. Suitable for regression tasks in domains such as renewable energy forecasting and hourly data prediction. For more details about implementation and parameter bounds see: Setiawan et al. (2021) <doi:10.1016/j.procs.2020.12.003> and Liu et al. (2018) <doi:10.1155/2018/6076475>.

r-minipch 0.4.0
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-checkmate@2.3.3
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://simnph.github.io/miniPCH/
Licenses: GPL 3+
Build system: r
Synopsis: Survival Distributions with Piece-Wise Constant Hazards
Description:

Density, distribution function, ... hazard function, cumulative hazard function, survival function for survival distributions with piece-wise constant hazards and multiple states and methods to plot and summarise those distributions. A derivation of the used algorithms can be found in my masters thesis <doi:10.25365/thesis.76098>.

r-mpn 0.4.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://pub-connect.foodsafetyrisk.org/microbial/mpncalc/
Licenses: FSDG-compatible
Build system: r
Synopsis: Most Probable Number and Other Microbial Enumeration Techniques
Description:

Calculates the Most Probable Number (MPN) to quantify the concentration (density) of microbes in serial dilutions of a laboratory sample (described in Jarvis, 2010 <doi:10.1111/j.1365-2672.2010.04792.x>). Also calculates the Aerobic Plate Count (APC) for similar microbial enumeration experiments.

r-metaboqc 1.1
Propagated dependencies: r-plyr@1.8.9
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MetaboQC
Licenses: GPL 2
Build system: r
Synopsis: Normalize Metabolomic Data using QC Signal
Description:

Takes QC signal for each day and normalize metabolomic data that has been acquired in a certain period of time. At least three QC per day are required.

r-multanova 1.0.1
Propagated dependencies: r-ggrepel@0.9.6 r-ggplot2@4.0.1 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=MultANOVA
Licenses: GPL 3+
Build system: r
Synopsis: Analysis of Designed High-Dimensional Data using the Comprehensive MultANOVA Framework
Description:

This package provides a comprehensive and computationally fast framework to analyze high dimensional data associated with an experimental design based on Multiple ANOVAs (MultANOVA). It includes testing the overall significance of terms in the model, post-hoc analyses of significant terms and variable selection. Details may be found in Mahieu, B., & Cariou, V. (2025). MultANOVA Followed by Post Hoc Analyses for Designed Highâ Dimensional Data: A Comprehensive Framework That Outperforms ASCA, rMANOVA, and VASCA. Journal of Chemometrics, 39(7). <doi:10.1002/cem.70039>.

r-mata 0.7.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MATA
Licenses: GPL 2
Build system: r
Synopsis: Model-Averaged Tail Area (MATA) Confidence Interval and Distribution
Description:

Calculates Model-Averaged Tail Area Wald (MATA-Wald) confidence intervals, and MATA-Wald confidence densities and distributions, which are constructed using single-model frequentist estimators and model weights. See Turek and Fletcher (2012) <doi:10.1016/j.csda.2012.03.002> and Fletcher et al (2019) <doi:10.1007/s10651-019-00432-5> for details.

r-mgms2 1.0.2
Propagated dependencies: r-maldiquantforeign@0.14.1 r-maldiquant@1.22.3
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MGMS2
Licenses: GPL 3
Build system: r
Synopsis: 'MGMS2' for Polymicrobial Samples
Description:

This package provides a glycolipid mass spectrometry technology has the potential to accurately identify individual bacterial species from polymicrobial samples. To develop bacterial identification algorithms (e.g. machine learning) using this glycolipid technology, it is necessary to generate a large number of various in-silico polymicrobial mass spectra that are similar to real mass spectra. MGMS2 (Membrane Glycolipid Mass Spectrum Simulator) generates such in-silico mass spectra, considering errors in m/z (mass-to-charge ratio) and variances of intensity values, occasions of missing signature ions, and noise peaks. It estimates summary statistics of monomicrobial mass spectra for each strain or species and simulates polymicrobial glycolipid mass spectra using the summary statistics of monomicrobial mass spectra. References: Ryu, S.Y., Wendt, G.A., Chandler, C.E., Ernst, R.K. and Goodlett, D.R. (2019) <doi:10.1021/acs.analchem.9b03340> "Model-based Spectral Library Approach for Bacterial Identification via Membrane Glycolipids." Gibb, S. and Strimmer, K. (2012) <doi:10.1093/bioinformatics/bts447> "MALDIquant: a versatile R package for the analysis of mass spectrometry data.".

r-mbend 1.3.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/nilforooshan/mbend
Licenses: GPL 3
Build system: r
Synopsis: Matrix Bending
Description:

Bending non-positive-definite (symmetric) matrices to positive-definite, using weighted and unweighted methods. Jorjani, H., et al. (2003) <doi:10.3168/jds.S0022-0302(03)73646-7>. Schaeffer, L. R. (2014) <http://animalbiosciences.uoguelph.ca/~lrs/ELARES/PDforce.pdf>.

r-meditations 1.0.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/jacobkap/meditations
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Prints a Random Quote from Marcus Aurelius' Book Meditations
Description:

Prints a random quote from Marcus Aurelius book Meditations.

r-mobps 1.13.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MoBPS
Licenses: GPL 3+
Build system: r
Synopsis: Modular Breeding Program Simulator
Description:

Framework for the simulation framework for the simulation of complex breeding programs and compare their economic and genetic impact. Associated publication: Pook et al. (2020) <doi:10.1534/g3.120.401193>.

r-mrfa 0.6
Propagated dependencies: r-randtoolbox@2.0.5 r-plyr@1.8.9 r-grplasso@0.4-7 r-glmnet@4.1-10 r-foreach@1.5.2 r-fields@17.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MRFA
Licenses: GPL 2 GPL 3
Build system: r
Synopsis: Fitting and Predicting Large-Scale Nonlinear Regression Problems using Multi-Resolution Functional ANOVA (MRFA) Approach
Description:

This package performs the MRFA approach proposed by Sung et al. (2020) <doi:10.1080/01621459.2019.1595630> to fit and predict nonlinear regression problems, particularly for large-scale and high-dimensional problems. The application includes deterministic or stochastic computer experiments, spatial datasets, and so on.

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-metaplus 1.0-6
Propagated dependencies: r-rfast@2.1.5.2 r-numderiv@2016.8-1.1 r-metafor@4.8-0 r-mass@7.3-65 r-lme4@1.1-37 r-foreach@1.5.2 r-fastghquad@1.0.1 r-doparallel@1.0.17 r-boot@1.3-32 r-bbmle@1.0.25.1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=metaplus
Licenses: GPL 2+
Build system: r
Synopsis: Robust Meta-Analysis and Meta-Regression
Description:

This package performs meta-analysis and meta-regression using standard and robust methods with confidence intervals based on the profile likelihood. Robust methods are based on alternative distributions for the random effect, either the t-distribution (Lee and Thompson, 2008 <doi:10.1002/sim.2897> or Baker and Jackson, 2008 <doi:10.1007/s10729-007-9041-8>) or mixtures of normals (Beath, 2014 <doi:10.1002/jrsm.1114>).

r-maditr 0.8.7
Propagated dependencies: r-magrittr@2.0.4 r-data-table@1.17.8
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/gdemin/maditr
Licenses: GPL 2
Build system: r
Synopsis: Fast Data Aggregation, Modification, and Filtering with Pipes and 'data.table'
Description:

This package provides pipe-style interface for data.table'. Package preserves all data.table features without significant impact on performance. let and take functions are simplified interfaces for most common data manipulation tasks. For example, you can write take(mtcars, mean(mpg), by = am) for aggregation or let(mtcars, hp_wt = hp/wt, hp_wt_mpg = hp_wt/mpg) for modification. Use take_if/let_if for conditional aggregation/modification. Additionally there are some conveniences such as automatic data.frame conversion to data.table'.

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.

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