R-universe search: exports:snm

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bioc

snm:Supervised Normalization of Microarrays

SNM is a modeling strategy especially designed for normalizing high-throughput genomic data. The underlying premise of our approach is that your data is a function of what we refer to as study-specific variables. These variables are either biological variables that represent the target of the statistical analysis, or adjustment variables that represent factors arising from the experimental or biological setting the data is drawn from. The SNM approach aims to simultaneously model all study-specific variables in order to more accurately characterize the biological or clinical variables of interest.

Maintained by John D. Storey. Last updated 5 months ago.

microarray onechannel twochannel multichannel differentialexpression exonarray geneexpression transcription multiplecomparison preprocessing qualitycontrol

4.41 score 64 scripts

cran

iCAMP:Infer Community Assembly Mechanisms by Phylogenetic-Bin-Based Null Model Analysis

To implement a general framework to quantitatively infer Community Assembly Mechanisms by Phylogenetic-bin-based null model analysis, abbreviated as 'iCAMP' (Ning et al 2020) <doi:10.1038/s41467-020-18560-z>. It can quantitatively assess the relative importance of different community assembly processes, such as selection, dispersal, and drift, for both communities and each phylogenetic group ('bin'). Each bin usually consists of different taxa from a family or an order. The package also provides functions to implement some other published methods, including neutral taxa percentage (Burns et al 2016) <doi:10.1038/ismej.2015.142> based on neutral theory model and quantifying assembly processes based on entire-community null models ('QPEN', Stegen et al 2013) <doi:10.1038/ismej.2013.93>. It also includes some handy functions, particularly for big datasets, such as phylogenetic and taxonomic null model analysis at both community and bin levels, between-taxa niche difference and phylogenetic distance calculation, phylogenetic signal test within phylogenetic groups, midpoint root of big trees, etc. Version 1.3.x mainly improved the function for 'QPEN' and added function 'icamp.cate()' to summarize 'iCAMP' results for different categories of taxa (e.g. core versus rare taxa).

Maintained by Daliang Ning. Last updated 3 years ago.

2 stars 2.26 score 1 dependents

cran

assist:A Suite of R Functions Implementing Spline Smoothing Techniques

Fit various smoothing spline models. Includes an ssr() function for smoothing spline regression, an nnr() function for nonparametric nonlinear regression, an snr() function for semiparametric nonlinear regression, an slm() function for semiparametric linear mixed-effects models, and an snm() function for semiparametric nonlinear mixed-effects models. See Wang (2011) <doi:10.1201/b10954> for an overview.

Maintained by Yuedong Wang. Last updated 2 years ago.

fortran openblas

1.00 score