Package: qape 2.1

Alicja Wolny-Dominiak

qape: Quantile of Absolute Prediction Errors

Estimates QAPE using bootstrap procedures. The residual, parametric and double bootstrap is used. The test of normality using Cholesky decomposition is added. Y pop is defined.

Authors:Alicja Wolny-Dominiak, Tomasz Zadlo

qape_2.1.tar.gz
qape_2.1.zip(r-4.7-any)qape_2.1.zip(r-4.6-any)
qape_2.1.tar.gz(r-4.7-any)qape_2.1.tar.gz(r-4.6-any)
qape_2.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
qape/json (API)

# Install 'qape' in R:
install.packages('qape', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • invData - Population data - investments in Poland at NUTS 4 level
  • realestData - Population data - real estate in Poland at NUTS 4 level

On CRAN:

Conda:

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

1.00 score 3 scripts 277 downloads 31 exports 41 dependencies

Last updated from:3fd1292c2d. Checks:4 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develNOTE184
source / vignettesOK175
linux-releaseNOTE149
windows-develNOTE131
windows-releaseNOTE139
wasm-releaseOK92

Exports:bootParbootParFuturebootParFutureCorbootParMisbootResbootResFuturebootResMiscorrectioncorrRancompcorrRanefdoubleBootdoubleBootFuturedoubleBootMisEBLUPebpLMMneEmpCMEstCMmcBootMismcLMMmismodifyDatasetnormCholTestplugInLMMprint.EBLUPprint.ebpLMMneprint.plugInLMMquantileNaNsrswrResummary.EBLUPsummary.ebpLMMnesummary.plugInLMMZfun

Dependencies:bootclicodetoolsdigestdplyrfuturefuture.applygenericsglobalsgluelatticelifecyclelistenvlme4magrittrMASSMatrixmatrixcalcminqamvtnormnlmenloptrparallellypillarpkgconfigplyrR6rbibutilsRcppRcppEigenRdpackreformulasreshape2rlangstringistringrtibbletidyselectutf8vctrswithr

Readme and manuals

Help Manual

Help pageTopics
Parametric bootstrap estimators of prediction accuracybootPar
Parametric bootstrap estimators of prediction accuracy - parallel computing.bootParFuture
Parametric bootstrap estimators of prediction accuracy - parallel computing using corrected covariance matricesbootParFutureCor
Parametric bootstrap estimators of prediction accuracy under the misspecified modelbootParMis
Residual bootstrap estimators of prediction accuracybootRes
Residual bootstrap estimators of prediction accuracy - parallel computingbootResFuture
Residual bootstrap estimators of prediction accuracy under the misspecified modelbootResMis
Correction term for predicted random effectscorrection
Correction of predicted random componentscorrRancomp
Correction of predicted random effectscorrRanef
Double bootstrap estimators of prediction accuracydoubleBoot
Double bootstrap estimators of prediction accuracy - parallel computingdoubleBootFuture
Double bootstrap estimators of prediction accuracy under the misspecified modeldoubleBootMis
Empirical Best Linear Unbiased PredictorEBLUP
Empirical Best Predictor based on the nested error linear mixed modelebpLMMne
Empirical covariance matrix of predicted random effectsEmpCM
Estimated covariance matrix of predicted random effectsEstCM
Population data - investments in Poland at NUTS 4 levelinvData
Monte Carlo simulation study of accuracy of estimators of accuracy measuresmcBootMis
Monte Carlo simuation study of accuracy of predictors under the misspecified modelmcLMMmis
Modification of the values of the variables in the datasetmodifyDataset
Test of normality of the dependent variablenormCholTest
PLUG-IN predictor based on the linear mixed modelplugInLMM
print the value of EBLUP predictorprint.EBLUP
print the value of ebpLMMne predictorprint.ebpLMMne
print the value of plugInLMM predictorprint.plugInLMM
quantile NaNquantileNaN
Population data - real estate in Poland at NUTS 4 levelrealestData
Bootstrap sample of predicted random effectssrswrRe
Summary of EBLUP predictionsummary.EBLUP
Summary of ebpLMMne predictionsummary.ebpLMMne
Summary of plugInLMM predictionsummary.plugInLMM
Matrix Z creatorZfun