Package: skewlmm 1.1.5

skewlmm: Scale Mixture of Skew-Normal Linear Mixed Models

It fits scale mixture of skew-normal linear mixed models using either an expectation–maximization (EM) type algorithm or its accelerated version (Damped Anderson Acceleration with Epsilon Monotonicity, DAAREM), including some possibilities for modeling the within-subject dependence <doi:10.18637/jss.v115.i07>.

Authors:Fernanda L. Schumacher [aut, cre], Larissa A. Matos [aut], Victor H. Lachos [aut], Katherine A. L. Valeriano [aut], Nicholas Henderson [ctb], Ravi Varadhan [ctb]

skewlmm_1.1.5.tar.gz
skewlmm_1.1.5.zip(r-4.7)skewlmm_1.1.5.zip(r-4.6)skewlmm_1.1.5.zip(r-4.5)
skewlmm_1.1.5.tgz(r-4.6-any)skewlmm_1.1.5.tgz(r-4.5-any)
skewlmm_1.1.5.tar.gz(r-4.7-any)skewlmm_1.1.5.tar.gz(r-4.6-any)
skewlmm_1.1.5.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
skewlmm/json (API)

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

Bug tracker:https://github.com/fernandalschumacher/skewlmm/issues

Datasets:
  • miceweight - Data set for clinical trial measuring mice weight
  • UTIdata - Data set for Unstructured Treatment Interruption Study

On CRAN:

Conda:

3.62 score 6 stars 14 scripts 385 downloads 21 exports 75 dependencies

Last updated from:2f83170bb7. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK246
source / vignettesOK218
linux-release-x86_64OK232
macos-release-arm64OK117
macos-oldrel-arm64OK165
windows-develOK176
windows-releaseOK164
windows-oldrelOK167
wasm-releaseOK141

Exports:acfresidboot_ciboot_parcriteriaerrorVarfixefformulahealy.plotlmmControllr.testmahalDistmahalDistCensnobsranefrsmsn.clmmrsmsn.lmmsigmasmn.clmmsmn.lmmsmsn.lmmweight_plot

Dependencies:alabamaBHbitbit64clicliprcodetoolscontfraccpp11crayondeSolvedigestdplyrellipticfarverforcatsfurrrfutureFuzzyNumbersFuzzyNumbers.Ext.2genericsggplot2ggrepelglobalsgluegtablehavenhmshypergeoisobandlabelinglatticelifecyclelistenvmagrittrMASSmatrixcalcmomentsMomTruncmvtnormnleqslvnlmenumDerivoptimParallelparallellypillarpkgconfigprettyunitsprogresspurrrqrngR6rbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreadrrellipticalrlangRyacasS7scalesspacefillrtibbletidyselecttlrmvnmvtTruncatedNormaltzdbutf8vctrsviridisLitevroomwithr

Readme and manuals

Help Manual

Help pageTopics
Autocorrelation function for smn.lmm or smsn.lmm residualsacfresid
Extract confidence intervals from 'lmmBoot' objectboot_ci
Parametric bootstrap for SMSN/SMN objectsboot_par
Extract coefficients from smsn.lmm, smn.lmm and smn.clmm objectscoef coef.SMN coef.SMNclmm coef.SMSN
Computes confidence intervals from smn.lmm and smsn.lmm fitted modelsconfint confint.SMN confint.SMSN
Extracts criteria for model comparison of SMSN/SMN/SMNclmm objectscriteria
Error scale matrix associated with 'times'errorVar
Extract smn.lmm fitted valuesfitted.SMN
Extract smn.clmm fitted valuesfitted.SMNclmm
Extract smsn.lmm fitted valuesfitted.SMSN
Extract estimated fixed effects from smsn.lmm, smn.lmm and smn.clmm objectsfixef fixef.SMN fixef.SMSN
Formula from an smn.lmm and smsn.lmm modelsformula formula.SMN formula.SMSN
Healy-type plot from a smn.lmm or smsn.lmm objecthealy.plot
Control options for 'smsn.lmm()', 'smn.lmm()' and 'smn.clmm()'lmmControl
Log-likelihood of an smn.lmm and smsn.lmm modelslogLik.SMN logLik.SMSN
Likelihood-ratio test for SMSN/SMN objectslr.test
Mahalanobis distance from a smn.lmm or smsn.lmm objectmahalDist
Mahalanobis distance from a smn.clmm objectmahalDistCens
Data set for clinical trial measuring mice weightmiceweight
Extract the number of observations from smn.lmm and smsn.lmm fitted modelsnobs nobs.SMN nobs.SMSN
Plot a smn.lmm or smsn.lmm objectplot.SMN plot.SMSN
Plot ACF for smn.lmm or smsn.lmm residualsplot.acfresid
Plot Mahalanobis distance for a fitted smn.lmm or smsn.lmmplot.mahalDist
Plot Mahalanobis distance for a fitted smn.clmmplot.mahalDistCens
Plot a smn.clmm objectplot.SMNclmm
Prediction of future observations from an smn.lmm objectpredict.SMN
Prediction of future observations from an smn.clmm objectpredict.SMNclmm
Prediction of future observations from an smsn.lmm objectpredict.SMSN
Print a smn.lmm objectprint.SMN
Print a smn.clmm objectprint.SMNclmm
Print a smsn.lmm objectprint.SMSN
Extract random effects from smsn.lmm, smn.lmm and smn.clmm objectsranef ranef.SMN ranef.SMNclmm ranef.SMSN
Extract model residuals from smn.lmm and smsn.lmm objectsresiduals.SMN residuals.SMSN
Extract model residuals from smn.clmm objectsresiduals.SMNclmm
Generate data from SMSN-CLMM with censored responsesrsmsn.clmm
Generate data from SMSN-LMMrsmsn.lmm
Residual standard deviation from smn.lmm and smsn.lmm objectssigma sigma.SMN sigma.SMSN
ML estimation of scale mixture of normal linear mixed models with censored responsessmn.clmm
ML estimation of scale mixture of normal linear mixed modelssmn.lmm
ML estimation of scale mixture of skew-normal linear mixed modelssmsn.lmm
Summary of a smn.lmm objectsummary.SMN
Summary of a smn.clmm objectsummary.SMNclmm
Summary of a smsn.lmm objectsummary.SMSN
Update for SMSN/SMN/SMNclmm objectsupdate.SMN update.SMNclmm update.SMSN
Data set for Unstructured Treatment Interruption StudyUTIdata
Weight plot for smn.lmm or smsn.lmm objectweight_plot