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bioc

spiky:Spike-in calibration for cell-free MeDIP

spiky implements methods and model generation for cfMeDIP (cell-free methylated DNA immunoprecipitation) with spike-in controls. CfMeDIP is an enrichment protocol which avoids destructive conversion of scarce template, making it ideal as a "liquid biopsy," but creating certain challenges in comparing results across specimens, subjects, and experiments. The use of synthetic spike-in standard oligos allows diagnostics performed with cfMeDIP to quantitatively compare samples across subjects, experiments, and time points in both relative and absolute terms.

Maintained by Tim Triche. Last updated 5 months ago.

differentialmethylationdnamethylationnormalizationpreprocessingqualitycontrolsequencing

2 stars 4.90 score 3 scripts

haraldschellander

mevr:Fitting the Metastatistical Extreme Value Distribution MEVD

Extreme value analysis with the metastatistical extreme value distribution MEVD (Marani and Ignaccolo, 2015, <doi:10.1016/j.advwatres.2015.03.001>) and some of its variants. In particular, analysis can be performed with the simplified metastatistical extreme value distribution SMEV (Marra et al., 2019, <doi:10.1016/j.advwatres.2019.04.002>) and the temporal metastatistical extreme value distribution TMEV (Falkensteiner et al., 2023, <doi:10.1016/j.wace.2023.100601>). Parameters can be estimated with probability weighted moments, maximum likelihood and least squares. The data can also be left-censored prior to a fit. Density, distribution function, quantile function and random generation for the MEVD, SMEV and TMEV are included. In addition, functions for the calculation of return levels including confidence intervals are provided. For a description of use cases please see the provided references.

Maintained by Harald Schellander. Last updated 3 days ago.

extreme-value-statistics

2 stars 4.11 score 1 scripts

alexvolkmann

MJMbamlss:Multivariate Joint Models with 'bamlss'

Multivariate joint models of longitudinal and time-to-event data based on functional principal components implemented with 'bamlss'. Implementation for Volkmann, Umlauf, Greven (2023) <arXiv:2311.06409>.

Maintained by Alexander Volkmann. Last updated 22 days ago.

2 stars 4.08 score 15 scripts

stan125

distreg.vis:Framework for the Visualization of Distributional Regression Models

Functions for visualizing distributional regression models fitted using the 'gamlss', 'bamlss' or 'betareg' R package. The core of the package consists of a 'shiny' application, where the model results can be interactively explored and visualized.

Maintained by Stanislaus Stadlmann. Last updated 1 years ago.

bamlssdistributional-regressiongamlssshinyvisualization

3 stars 3.35 score 15 scripts

alexvolkmann

gmfamm:Generalized Multivariate Functional Additive Models

Supply implementation to model generalized multivariate functional data using Bayesian additive mixed models of R package 'bamlss' via a latent Gaussian process (see Umlauf, Klein, Zeileis (2018) <doi:10.1080/10618600.2017.1407325>).

Maintained by Alexander Volkmann. Last updated 9 months ago.

1.70 score 1 scripts

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