Package: scrime 1.3.5

scrime: Analysis of High-Dimensional Categorical Data Such as SNP Data

Tools for the analysis of high-dimensional data developed/implemented at the group "Statistical Complexity Reduction In Molecular Epidemiology" (SCRIME). Main focus is on SNP data. But most of the functions can also be applied to other types of categorical data.

Authors:Holger Schwender, with a contribution of Arno Fritsch

scrime_1.3.5.tar.gz
scrime_1.3.5.zip(r-4.5)scrime_1.3.5.zip(r-4.4)scrime_1.3.5.zip(r-4.3)
scrime_1.3.5.tgz(r-4.5-any)scrime_1.3.5.tgz(r-4.4-any)scrime_1.3.5.tgz(r-4.3-any)
scrime_1.3.5.tar.gz(r-4.5-noble)scrime_1.3.5.tar.gz(r-4.4-noble)
scrime_1.3.5.tgz(r-4.4-emscripten)scrime_1.3.5.tgz(r-4.3-emscripten)
scrime.pdf |scrime.html✨
scrime/json (API)

# Install 'scrime' in R:
install.packages('scrime', repos = c('https://holgerschw.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

5.10 score 35 packages 53 scripts 3.2k downloads 7 mentions 48 exports 0 dependencies

Last updated 6 years agofrom:cf0033dbfe. Checks:1 OK, 8 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 17 2025
R-4.5-winNOTEMar 17 2025
R-4.5-macNOTEMar 17 2025
R-4.5-linuxNOTEMar 17 2025
R-4.4-winNOTEMar 17 2025
R-4.4-macNOTEMar 17 2025
R-4.4-linuxNOTEMar 17 2025
R-4.3-winNOTEMar 17 2025
R-4.3-macNOTEMar 17 2025

Exports:abfanalyse.modelsbuildSNPannotationcanberra2Matscohencohen2MatscolEpistaticcomputeContCellscomputeContClasseuclidean2Matsfblrfblr.weightgetMatFuzzygknnidentifyMonomorphismknncatimputeknncatimputeLargemanhattan2Matsmaximum2Matsminkowski2MatspamCatpccpcc2MatspredictFBLRrecodeAffySNPrecodeSNPsrowCATTsrowChisq2ClassrowChisqMultiClassrowChisqStatsrowCorsrowEpistaticrowFreqsrowHWEsrowMAFsrowMsquaresrowScalesrowTablesrowTrendFuzzyrowTrendStatsshortenGeneDescriptionshowChangessimulateSNPcatResponsesimulateSNPglmsimulateSNPssmcsmc2Matssnp2bin

Dependencies:

Readme and manuals

Help Manual

Help pageTopics
Approximate Bayes Factorabf
Summarize MCMC sample of Bayesian logic regression modelsanalyse.models
Construct Annotation for Affymetrix SNP ChipsbuildSNPannotation
Cordell's Test for Epistatic InteractionscolEpistatic print.colEpi rowEpistatic
Pairwise Contingency TablescomputeContCells
Rowwise Contigency TablescomputeContClass
Full Bayesian Logic Regression for SNP Datafblr fblr.weight
Generalized k Nearest Neighborsgknn
Identification of Constant VariablesidentifyMonomorphism
Missing Value Imputation with kNNknncatimpute
Missing Value Imputation with kNN for High-Dimensional DataknncatimputeLarge
Prediction Analysis of Categorical DatapamCat print.pamCat
Pearson's Contingency Coefficientpcc
Predict Method for pamCat Objectspredict.pamCat
Predict Case Probabilities with Full Bayesian Logic RegressionpredictFBLR
Recoding of Affymetrix SNP ValuesrecodeAffySNP
Recoding of SNP ValuesrecodeSNPs
Rowwise Cochran-Armitage Trend Test Based on TablesrowCATTs
Rowwise Pearson's ChiSquare Test Based on TablesrowChisq2Class rowChisqMultiClass
Rowwise Pearson's ChiSquare StatisticrowChisqStats
Rowwise Correlation with a VectorrowCors
Rowwise FrequenciesrowFreqs
Rowwise Test for Hardy-Weinberg EquilibriumrowHWEs
Rowwise Minor Allele FrequencyrowMAFs
Rowwise Linear Trend Test Based on TablesrowMsquares
Rowwise ScalingrowScales
Rowwise TablesrowTables
Trend Test for Fuzzy Genotype CallsgetMatFuzzy rowTrendFuzzy
Rowwise Linear Trend TestsrowTrendStats
Shorten the Gene DescriptionshortenGeneDescription
Displaying ChangesshowChanges
Simulation of SNP Data with Categorical Responseprint.simSNPcatResponse simulateSNPcatResponse
Simulation of SNP datasimulateSNPglm
Simulation of SNP datasimulateSNPs
Simple Matching Coefficient and Cohen's Kappacohen smc
Transformation of SNPs to Binary Variablessnp2bin
Summarizing a simSNPglm objectsummary.simSNPglm