Showing 5 of total 5 results (show query)
nerler
JointAI:Joint Analysis and Imputation of Incomplete Data
Joint analysis and imputation of incomplete data in the Bayesian framework, using (generalized) linear (mixed) models and extensions there of, survival models, or joint models for longitudinal and survival data, as described in Erler, Rizopoulos and Lesaffre (2021) <doi:10.18637/jss.v100.i20>. Incomplete covariates, if present, are automatically imputed. The package performs some preprocessing of the data and creates a 'JAGS' model, which will then automatically be passed to 'JAGS' <https://mcmc-jags.sourceforge.io/> with the help of the package 'rjags'.
Maintained by Nicole S. Erler. Last updated 12 months ago.
bayesiangeneralized-linear-modelsglmglmmimputationimputationsjagsjoint-analysislinear-mixed-modelslinear-regression-modelsmcmc-samplemcmc-samplingmissing-datamissing-valuessurvivalcpp
28 stars 7.30 score 59 scripts 1 dependents
bioc
DEP:Differential Enrichment analysis of Proteomics data
This package provides an integrated analysis workflow for robust and reproducible analysis of mass spectrometry proteomics data for differential protein expression or differential enrichment. It requires tabular input (e.g. txt files) as generated by quantitative analysis softwares of raw mass spectrometry data, such as MaxQuant or IsobarQuant. Functions are provided for data preparation, filtering, variance normalization and imputation of missing values, as well as statistical testing of differentially enriched / expressed proteins. It also includes tools to check intermediate steps in the workflow, such as normalization and missing values imputation. Finally, visualization tools are provided to explore the results, including heatmap, volcano plot and barplot representations. For scientists with limited experience in R, the package also contains wrapper functions that entail the complete analysis workflow and generate a report. Even easier to use are the interactive Shiny apps that are provided by the package.
Maintained by Arne Smits. Last updated 5 months ago.
immunooncologyproteomicsmassspectrometrydifferentialexpressiondatarepresentation
7.10 score 628 scripts
yeukyul
lindia:Automated Linear Regression Diagnostic
Provides a set of streamlined functions that allow easy generation of linear regression diagnostic plots necessarily for checking linear model assumptions. This package is meant for easy scheming of linear regression diagnostics, while preserving merits of "The Grammar of Graphics" as implemented in 'ggplot2'. See the 'ggplot2' website for more information regarding the specific capability of graphics.
Maintained by Yeuk Yu Lee. Last updated 2 years ago.
105 stars 6.16 score 139 scripts
marcjwilliams1
neutralitytestr:Test for a Neutral Evolutionary Model in Cancer Sequencing Data
Package takes frequencies of mutations as reported by high throughput sequencing data from cancer and fits a theoretical neutral model of tumour evolution. Package outputs summary statistics and contains code for plotting the data and model fits. See Williams et al 2016 <doi:10.1038/ng.3489> and Williams et al 2017 <doi:10.1101/096305> for further details of the method.
Maintained by Marc Williams. Last updated 4 years ago.
10 stars 5.10 score 25 scripts
krajnc
densitr:Analysing Density Profiles from Resistance Drilling of Trees
Provides various tools for analysing density profiles obtained by resistance drilling. It can load individual or multiple files and trim the starting and ending part of each density profile. Tools are also provided to trim profiles manually, to remove the trend from measurements using several methods, to plot the profiles and to detect tree rings automatically. Written with a focus on forestry use of resistance drilling in standing trees.
Maintained by Luka Krajnc. Last updated 3 years ago.
2 stars 3.90 score 9 scripts