Package: imp4p 1.3

Quentin Giai Gianetto

imp4p: Imputation for Proteomics

Functions to analyse missing value mechanisms and to impute data sets in the context of bottom-up MS-based proteomics.

Authors:Quentin Giai Gianetto [aut, cre], Samuel Wieczorek [ctb], Yohann Coute [ctb], Thomas Burger [aut]

imp4p_1.3.tar.gz
imp4p_1.3.zip(r-4.7-x86_64)imp4p_1.3.zip(r-4.7-arm64)
imp4p_1.3.tar.gz(r-4.7-arm64)imp4p_1.3.tar.gz(r-4.7-x86_64)imp4p_1.3.tar.gz(r-4.6-arm64)imp4p_1.3.tar.gz(r-4.6-x86_64)
imp4p_1.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
imp4p/json (API)

# Install 'imp4p' in R:
install.packages('imp4p', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

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

cpp

2.06 score 1 stars 1 packages 38 scripts 695 downloads 1 mentions 27 exports 152 dependencies

Last updated from:a0a7116b92. Checks:8 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK172
linux-devel-x86_64OK199
source / vignettesOK246
linux-release-arm64OK176
linux-release-x86_64OK199
windows-devel-arm64OK248
windows-devel-x86_64OK173
wasm-releaseOK174

Exports:estim.boundestim.mixfast_apply_nb_nafast_apply_nb_not_nafast_apply_sd_na_rm_Tfast_apply_sum_na_rm_Tfast_simgen.condimpute.igcdaimpute.miimpute.miximpute.mleimpute.paimpute.PCAimpute.randimpute.RFimpute.slsami.mixmiss.mcar.processmiss.total.processpi.mcar.karpievitchpi.mcar.logitpi.mcar.probitprob.mcarprob.mcar.tabsim.datatranslatedRandomBeta

Dependencies:abindbackportsbase64encbitbit64bootbroombslibcachemcarcarDataclicliprclustercodetoolscolorspacecommonmarkcowplotcpp11crayoncrosstalkcurlDerivdigestdoBydoParalleldplyrDTellipseemmeansestimabilityevaluateFactoMineRfarverfastmapflashClustFNNfontawesomeforcatsforeachforecastFormulafracdifffsgenericsggplot2ggrepelggtextglmnetgluegridtextgtablehavenhighrhmshtmltoolshtmlwidgetsirlbaIsoisobanditeratorsjomojpegjquerylibjsonliteknitrlabelinglaterlatticelazyevalleapslifecyclelitedownlme4lmtestmagrittrmarkdownMASSMatrixMatrixModelsmemoisemgcvmicemimeminqamissMDAmissRangermitmlmodelrmultcompViewmvtnormnlmenloptrnnetnormnumDerivordinalotelpanpbkrtestpillarpkgconfigpngprettyunitsprogresspromisespurrrquantregR6rangerrappdirsrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreadrreformulasrlangrmarkdownrpartS7sassscalesscatterplot3dshapeshowtextshowtextdbSparseMstringistringrsurvivalsysfontstibbletidyrtidyselecttimeDatetinytextruncnormtzdbucminfurcautf8vctrsviridisLitevroomwithrxfunxml2yamlzoo

Readme and manuals

Help Manual

Help pageTopics
Introduction to the IMP4P packageimp4p-package imp4p
Estimation of lower and upper bounds for missing values.estim.bound
Estimation of a mixture model of MCAR and MNAR values in each column of a data matrix.estim.mix
Function similar to the function 'apply(X,dim,function(x)sum(is.na(x)))'.fast_apply_nb_na
Function similar to the function 'apply(X,dim,function(x)sum(!is.na(x)))'.fast_apply_nb_not_na
Function similar to the function 'apply(X,dim,sd,na.rm=TRUE)'.fast_apply_sd_na_rm_T
Function similar to the function 'apply(X,dim,sum,na.rm=TRUE)'.fast_apply_sum_na_rm_T
Function to compute similarity measures between a vector and each row of a matrix.fast_sim
Function allowing to create a vector indicating the membership of each sample to a condition.gen.cond
Imputing missing values by assuming that the distribution of complete values is Gaussian in each column of an input matrix. This algorithm is named "Imputation under a Gaussian Complete Data Assumption" (IGCDA).impute.igcda
Imputation of data sets containing peptide intensities with a multiple imputation strategy.impute.mi
Imputation using a decision rule under an assumption of a mixture of MCAR and MNAR values.impute.mix
Imputing missing values using a maximum likelihood estimation (MLE).impute.mle
Imputation of peptides having no value in a biological condition (present in a condition / absent in another).impute.pa
Imputing missing values using Principal Components Analysis.impute.PCA
Imputation of peptides with a random value.impute.rand
Imputing missing values using Random Forest.impute.RF
Imputing missing values using an adaptation of the LSimpute algorithm (Bo et al. (2004)) to experimental designs. This algorithm is named "Structured Least Squares Algorithm" (SLSA).impute.slsa
Multiple imputation from a matrix of probabilities of being MCAR for each missing value.mi.mix
Estimating the MCAR mechanism in a sample.miss.mcar.process
Estimating the missing data mechanism in a sample.miss.total.process
Estimating the proportion of MCAR values in biological conditions using the method of Karpievitch (2009).pi.mcar.karpievitch
Estimating the proportion of MCAR values in a sample using a logit model.pi.mcar.logit
Estimating the proportion of MCAR values in a sample using a probit model.pi.mcar.probit
Estimation of a vector of probabilities that missing values are MCAR.prob.mcar
Estimation of a matrix of probabilities that missing values are MCAR.prob.mcar.tab
Simulation of data sets by controlling the proportion of MCAR values and the distribution of MNAR values.sim.data
Function to generated values following a translated Beta distributiontranslatedRandomBeta