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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-multikink 0.2.0
Propagated dependencies: r-quantreg@6.1 r-pracma@2.4.6 r-matrix@1.7-4 r-gam@1.22-6
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MultiKink
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Estimation and Inference for Multi-Kink Quantile Regression
Description:

Estimation and inference for multiple kink quantile regression for longitudinal data and the i.i.d data. A bootstrap restarting iterative segmented quantile algorithm is proposed to estimate the multiple kink quantile regression model conditional on a given number of change points. The number of kinks is also allowed to be unknown. In such case, the backward elimination algorithm and the bootstrap restarting iterative segmented quantile algorithm are combined to select the number of change points based on a quantile BIC. For longitudinal data, we also develop the GEE estimator to incorporate the within-subject correlations. A score-type based test statistic is also developed for testing the existence of kink effect. The package is based on the paper, ``Wei Zhong, Chuang Wan and Wenyang Zhang (2022). Estimation and inference for multikink quantile regression, JBES and ``Chuang Wan, Wei Zhong, Wenyang Zhang and Changliang Zou (2022). Multi-kink quantile regression for longitudinal data with application to progesterone data analysis, Biometrics".

r-minilnm 0.1.0
Propagated dependencies: r-tidyselect@1.2.1 r-stanheaders@2.32.10 r-rstantools@2.5.0 r-rstan@2.32.7 r-rcppparallel@5.1.11-1 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-posterior@1.6.1 r-glue@1.8.0 r-formula-tools@1.7.1 r-fansi@1.0.7 r-dplyr@1.1.4 r-cli@3.6.5 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/krisrs1128/miniLNM/
Licenses: CC0
Build system: r
Synopsis: Miniature Logistic-Normal Multinomial Models
Description:

Logistic-normal Multinomial (LNM) models are common in problems with multivariate count data. This package gives a simple implementation with a 30 line Stan script. This lightweight implementation makes it an easy starting point for other projects, in particular for downstream tasks that require analysis of "compositional" data. It can be applied whenever a multinomial probability parameter is thought to depend linearly on inputs in a transformed, log ratio space. Additional utilities make it easy to inspect, create predictions, and draw samples using the fitted models. More about the LNM can be found in Xia et al. (2013) "A Logistic Normal Multinomial Regression Model for Microbiome Compositional Data Analysis" <doi:10.1111/biom.12079> and Sankaran and Holmes (2023) "Generative Models: An Interdisciplinary Perspective" <doi:10.1146/annurev-statistics-033121-110134>.

r-micd 1.1.2
Propagated dependencies: r-rfast@2.1.5.2 r-rbgl@1.86.0 r-pcalg@2.7-12 r-mice@3.18.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/bips-hb/micd
Licenses: GPL 3+
Build system: r
Synopsis: Multiple Imputation in Causal Graph Discovery
Description:

Modified functions of the package pcalg and some additional functions to run the PC and the FCI (Fast Causal Inference) algorithm for constraint-based causal discovery in incomplete and multiply imputed datasets. Foraita R, Friemel J, Günther K, Behrens T, Bullerdiek J, Nimzyk R, Ahrens W, Didelez V (2020) <doi:10.1111/rssa.12565>; Andrews RM, Bang CW, Didelez V, Witte J, Foraita R (2021) <doi:10.1093/ije/dyae113>; Witte J, Foraita R, Didelez V (2022) <doi:10.1002/sim.9535>.

r-multidiscreterng 0.1.0
Propagated dependencies: r-mvtnorm@1.3-3 r-multiord@2.4.4 r-matrixcalc@1.0-6 r-matrix@1.7-4 r-genord@2.0.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/ckchengtommy/MultiDiscreteRNG
Licenses: GPL 3
Build system: r
Synopsis: Generate Multivariate Discrete Data
Description:

Generate multivariate discrete data with generalized Poisson, negative binomial and binomial marginal distributions using user-specified distribution parameters and a target correlation matrix. The method is described in Cheng and Demirtas (2026) <doi:10.48550/arXiv.2602.07707>.

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-mrfse 0.4.2
Propagated dependencies: r-rfast@2.1.5.2 r-rcpp@1.1.0 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=mrfse
Licenses: GPL 3+
Build system: r
Synopsis: Markov Random Field Structure Estimator
Description:

Three algorithms for estimating a Markov random field structure.Two of them are an exact version and a simulated annealing version of a penalized maximum conditional likelihood method similar to the Bayesian Information Criterion. These algorithm are described in Frondana (2016) <doi:10.11606/T.45.2018.tde-02022018-151123>.The third one is a greedy algorithm, described in Bresler (2015) <doi:10.1145/2746539.2746631).

r-mmtdiff 1.0.0
Propagated dependencies: r-mvtnorm@1.3-3
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mmtdiff
Licenses: Expat
Build system: r
Synopsis: Moment-Matching Approximation for t-Distribution Differences
Description:

This package implements the moment-matching approximation for differences of non-standardized t-distributed random variables in both univariate and multivariate settings. The package provides density, distribution function, quantile function, and random generation for the approximated distributions of t-differences. The methodology establishes the univariate approximated distributions through the systematic matching of the first, second, and fourth moments, and extends it to multivariate cases, considering both scenarios of independent components and the more general multivariate t-distributions with arbitrary dependence structures. Methods build on the classical moment-matching approximation method (e.g., Casella and Berger (2024) <doi:10.1201/9781003456285>).

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-mlt 1.7-4
Propagated dependencies: r-variables@1.1-2 r-survival@3.8-3 r-sandwich@3.1-1 r-quadprog@1.5-8 r-numderiv@2016.8-1.1 r-nloptr@2.2.1 r-mvtnorm@1.3-3 r-matrix@1.7-4 r-icenreg@2.0.16 r-coneproj@1.23 r-bb@2019.10-1 r-basefun@1.2-6 r-alabama@2023.1.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: http://ctm.R-forge.R-project.org
Licenses: GPL 2
Build system: r
Synopsis: Most Likely Transformations
Description:

Likelihood-based estimation of conditional transformation models via the most likely transformation approach described in Hothorn et al. (2018) <DOI:10.1111/sjos.12291> and Hothorn (2020) <DOI:10.18637/jss.v092.i01>. Shift-scale (Siegfried et al, 2023, <DOI:10.1080/00031305.2023.2203177>) and multivariate (Klein et al, 2022, <DOI:10.1111/sjos.12501>) transformation models are part of this package. A package vignette is available from <DOI:10.32614/CRAN.package.mlt.docreg> and more convenient user interfaces to many models from <DOI:10.32614/CRAN.package.tram>.

r-microinverterdata 0.4.0
Propagated dependencies: r-units@1.0-0 r-tidyr@1.3.1 r-rlang@1.1.6 r-purrr@1.2.0 r-httr2@1.2.1 r-glue@1.8.0 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://camembr.github.io/microinverterdata/
Licenses: Expat
Build system: r
Synopsis: Collect your Microinverter Data
Description:

Collect and normalize local microinverter energy and power production data through off-cloud API requests. Currently supports APSystems', Enphase', and Fronius microinverters.

r-mixedsde 5.0
Propagated dependencies: r-sde@2.0.21 r-plot3d@1.4.2 r-moments@0.14.1 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=mixedsde
Licenses: GPL 2+
Build system: r
Synopsis: Estimation Methods for Stochastic Differential Mixed Effects Models
Description:

Inference on stochastic differential models Ornstein-Uhlenbeck or Cox-Ingersoll-Ross, with one or two random effects in the drift function.

r-mhorseshoe 0.1.5
Propagated dependencies: r-rcpp@1.1.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=Mhorseshoe
Licenses: Expat
Build system: r
Synopsis: Approximate Algorithm for Horseshoe Prior
Description:

This package provides exact and approximate algorithms for the horseshoe prior in linear regression models, which were proposed by Johndrow et al. (2020) <https://www.jmlr.org/papers/v21/19-536.html>.

r-matchit 4.7.2
Propagated dependencies: r-rlang@1.1.6 r-rcppprogress@0.4.2 r-rcpp@1.1.0 r-chk@0.10.0 r-backports@1.5.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://kosukeimai.github.io/MatchIt/
Licenses: GPL 2+
Build system: r
Synopsis: Nonparametric Preprocessing for Parametric Causal Inference
Description:

Selects matched samples of the original treated and control groups with similar covariate distributions -- can be used to match exactly on covariates, to match on propensity scores, or perform a variety of other matching procedures. The package also implements a series of recommendations offered in Ho, Imai, King, and Stuart (2007) <DOI:10.1093/pan/mpl013>. (The gurobi package, which is not on CRAN, is optional and comes with an installation of the Gurobi Optimizer, available at <https://www.gurobi.com>.).

r-mountainplot 1.4
Propagated dependencies: r-lattice@0.22-7
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://kwstat.github.io/mountainplot/
Licenses: GPL 3
Build system: r
Synopsis: Mountain Plots, Folded Empirical Cumulative Distribution Plots
Description:

Lattice functions for drawing folded empirical cumulative distribution plots, or mountain plots. A mountain plot is similar to an empirical CDF plot, except that the curve increases from 0 to 0.5, then decreases from 0.5 to 1 using an inverted scale at the right side. See Monti (1995) <doi:10.1080/00031305.1995.10476179>.

r-maybe 1.1.0
Propagated dependencies: r-magrittr@2.0.4
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/armcn/maybe
Licenses: Expat
Build system: r
Synopsis: The Maybe Monad
Description:

The maybe type represents the possibility of some value or nothing. It is often used instead of throwing an error or returning `NULL`. The advantage of using a maybe type over `NULL` is that it is both composable and requires the developer to explicitly acknowledge the potential absence of a value, helping to avoid the existence of unexpected behaviour.

r-morsetktd 0.1.3
Propagated dependencies: r-zoo@1.8-14 r-testthat@3.3.0 r-stanheaders@2.32.10 r-rstantools@2.5.0 r-rstan@2.32.7 r-rcppparallel@5.1.11-1 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-gridextra@2.3 r-ggplot2@4.0.1 r-desolve@1.40 r-bh@1.87.0-1
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=morseTKTD
Licenses: AGPL 3+
Build system: r
Synopsis: Bayesian Inference of TKTD Models
Description:

Advanced methods for a valuable quantitative environmental risk assessment using Bayesian inference of survival Data with toxicokinetics toxicodynamics (TKTD) models. Among others, it facilitates Bayesian inference of the general unified threshold model of survival (GUTS). See models description in Jager et al. (2011) <doi:10.1021/es103092a> and implementation using Bayesian inference in Baudrot and Charles (2019) <doi:10.1038/s41598-019-47698-0>.

r-mcmcsae 0.8.0
Propagated dependencies: r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-matrix@1.7-4 r-loo@2.8.0 r-gigrvg@0.8 r-collapse@2.1.5
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mcmcsae
Licenses: GPL 3
Build system: r
Synopsis: Markov Chain Monte Carlo Small Area Estimation
Description:

Fit multi-level models with possibly correlated random effects using Markov Chain Monte Carlo simulation. Such models allow smoothing over space and time and are useful in, for example, small area estimation.

r-multinttestfunc 0.3.0
Propagated dependencies: r-pracma@2.4.6 r-mvtnorm@1.3-3
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/KlausHerrmann/multIntTestFunc
Licenses: Expat
Build system: r
Synopsis: Provides Test Functions for Multivariate Integration
Description:

This package provides implementations of functions that can be used to test multivariate integration routines. The package covers six different integration domains (unit hypercube, unit ball, unit sphere, standard simplex, non-negative real numbers and R^n). For each domain several functions with different properties (smooth, non-differentiable, ...) are available. The functions are available in all dimensions n >= 1. For each function the exact value of the integral is known and implemented to allow testing the accuracy of multivariate integration routines. Details on the available test functions can be found at on the development website.

r-metamicrobiomer 1.2
Propagated dependencies: r-zcompositions@1.5.0-5 r-tidyr@1.3.1 r-plyr@1.8.9 r-meta@8.2-1 r-matrixstats@1.5.0 r-lmertest@3.1-3 r-lme4@1.1-37 r-gridextra@2.3 r-ggplot2@4.0.1 r-gdata@3.0.1 r-gamlss@5.5-0 r-dplyr@1.1.4 r-compositions@2.0-9
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/nhanhocu/metamicrobiomeR
Licenses: GPL 2
Build system: r
Synopsis: Microbiome Data Analysis & Meta-Analysis with GAMLSS-BEZI & Random Effects
Description:

Generalized Additive Model for Location, Scale and Shape (GAMLSS) with zero inflated beta (BEZI) family for analysis of microbiome relative abundance data (with various options for data transformation/normalization to address compositional effects) and random effects meta-analysis models for meta-analysis pooling estimates across microbiome studies are implemented. Random Forest model to predict microbiome age based on relative abundances of shared bacterial genera with the Bangladesh data (Subramanian et al 2014), comparison of multiple diversity indexes using linear/linear mixed effect models and some data display/visualization are also implemented. The reference paper is published by Ho NT, Li F, Wang S, Kuhn L (2019) <doi:10.1186/s12859-019-2744-2> .

r-mcreplicate 0.1.2
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=mcreplicate
Licenses: AGPL 3+
Build system: r
Synopsis: Multi-Core Replicate
Description:

Multi-core replication function to make it easier to do fast Monte Carlo simulation. Based on the mcreplicate() function from the rethinking package. The rethinking package requires installing rstan', which is onerous to install, while also not adding capabilities to this function.

r-mhazard 0.2.3
Propagated dependencies: r-survival@3.8-3 r-rootsolve@1.8.2.4 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-plot3d@1.4.2 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=mhazard
Licenses: GPL 3
Build system: r
Synopsis: Nonparametric and Semiparametric Methods for Multivariate Failure Time Data
Description:

Nonparametric survival function estimates and semiparametric regression for the multivariate failure time data with right-censoring. For nonparametric survival function estimates, the Volterra, Dabrowska, and Prentice-Cai estimates for bivariate failure time data may be computed as well as the Dabrowska estimate for the trivariate failure time data. Bivariate marginal hazard rate regression can be fitted for the bivariate failure time data. Functions are also provided to compute (bootstrap) confidence intervals and plot the estimates of the bivariate survival function. For details, see "The Statistical Analysis of Multivariate Failure Time Data: A Marginal Modeling Approach", Prentice, R., Zhao, S. (2019, ISBN: 978-1-4822-5657-4), CRC Press.

r-micromacromultilevel 0.4.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MicroMacroMultilevel
Licenses: GPL 2+
Build system: r
Synopsis: Micro-Macro Multilevel Modeling
Description:

Most multilevel methodologies can only model macro-micro multilevel situations in an unbiased way, wherein group-level predictors (e.g., city temperature) are used to predict an individual-level outcome variable (e.g., citizen personality). In contrast, this R package enables researchers to model micro-macro situations, wherein individual-level (micro) predictors (and other group-level predictors) are used to predict a group-level (macro) outcome variable in an unbiased way.

r-mulset 1.0.0
Propagated dependencies: r-gtools@3.9.5 r-digest@0.6.39
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://github.com/LogIN-/mulset
Licenses: FSDG-compatible
Build system: r
Synopsis: Multiset Intersection Generator
Description:

Computes efficient data distributions from highly inconsistent datasets with many missing values using multi-set intersections. Based upon hash functions, mulset can quickly identify intersections from very large matrices of input vectors across columns and rows and thus provides scalable solution for dealing with missing values. Tomic et al. (2019) <doi:10.1101/545186>.

r-mm2sdata 1.0.3
Propagated dependencies: r-biobase@2.70.0
Channel: guix-cran
Location: guix-cran/packages/m.scm (guix-cran packages m)
Home page: https://cran.r-project.org/package=MM2Sdata
Licenses: GPL 3
Build system: r
Synopsis: Gene Expression Datasets for the 'MM2S' Package
Description:

Gene Expression datasets for the MM2S package. Contains normalized expression data for Human Medulloblastoma ('GSE37418') as well as Mouse Medulloblastoma models ('GSE36594'). Deena Gendoo et al. (2015) <doi:10.1016/j.ygeno.2015.05.002>.

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Total results: 69112