Simulation | Estimation and inference | Censored observations | SessionInfo | Bibliography
Package: lava 1.9.3.9000
lava: Latent Variable Models
A general implementation of Structural Equation Models with latent variables (MLE, 2SLS, and composite likelihood estimators) with both continuous, censored, and ordinal outcomes (Holst and Budtz-Joergensen (2013) <doi:10.1007/s00180-012-0344-y>). Mixture latent variable models and non-linear latent variable models (Holst and Budtz-Joergensen (2020) <doi:10.1093/biostatistics/kxy082>). The package also provides methods for graph exploration (d-separation, back-door criterion), simulation of general non-linear latent variable models, and estimation of influence functions for a broad range of statistical models.
Authors:
lava_1.9.3.9000.tar.gz
lava_1.9.3.9000.zip(r-4.7-any)lava_1.9.3.9000.zip(r-4.6-any)lava_1.9.3.9000.zip(r-4.5-any)
lava_1.9.3.9000.tgz(r-4.6-any)lava_1.9.3.9000.tgz(r-4.5-any)
lava_1.9.3.9000.tar.gz(r-4.7-any)lava_1.9.3.9000.tar.gz(r-4.6-any)
lava_1.9.3.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
lava/json (API)
| # Install 'lava' in R: |
| install.packages('lava', repos = c('https://kkholst.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/kkholst/lava/issues
Pkgdown/docs site:https://kkholst.github.io
Datasets:
- bmd - Longitudinal Bone Mineral Density Data
- bmidata - Data
- brisa - Simulated data
- calcium - Longitudinal Bone Mineral Density Data
- deprdiag - 50 patients from Monash Medical Centre, Melbourne
- hubble - Hubble data
- hubble2 - Hubble data
- indoorenv - Data
- missingdata - Missing data example
- nldata - Example data
- nsem - Example SEM data
- semdata - Example SEM data
- serotonin - Serotonin data
- twindata - Twin menarche data
influence-functionlatent-variable-modelssimulationstatisticsstructural-equation-modelsquarto
Last updated from:1def4b4b0e. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 254 | ||
| source / vignettes | OK | 312 | ||
| linux-release | OK | 278 | ||
| macos-release | OK | 300 | ||
| macos-oldrel | OK | 262 | ||
| windows-devel | OK | 242 | ||
| windows-release | OK | 298 | ||
| windows-oldrel | OK | 237 | ||
| wasm-release | OK | 130 |
Exports:%++%%in.closed%%in.open%%ni%aalenExponential.lvmaddhookaddvaraddvar<-adjMatalpha_zmaxancestorsas.simbackdoorbaptizebeta.lvmbinary.lvmbinomial.lvmbinomial.rdbinomial.rrblockdiagbootstrapBycancelcancel<-categoricalcategorical<-childrenchisq.lvmclickclosed_testingCoefMatCoefMat.multigroupfitColcolorbarcolselCombinecommutationcomparecomplikconfbandconfpredconstant.lvmconstrainconstrain<-constraintscontrcorrelationcovariancecovariance<-coxExponential.lvmcoxGompertz.lvmcoxWeibull.lvmcsplitcurlydecomp.specialsdensity.simdescendantsdescribecoefdevcoordsdiagtestdist_aalen_exponentialdist_bernoullidist_betadist_chisqdist_constdist_cox_exponentialdist_cox_gompertzdist_cox_weibulldist_gammadist_gaussiandist_gaussian_mixture2dist_gaussian_mixture3dist_loggammadist_lognormaldist_multinomialdist_mvndist_nonedist_onesdist_paretodist_poissondist_seqdist_seqintdist_tdist_thresholddist_uniformdist_weibulldistributiondistribution<-dmvn0dsepedgelabelsedgelabels<-edgeListendogenousequivalenceestimateestimate.defaulteventTimeeventTime<-exogenousexogenous<-Expandexpectationexpitfinalizefixsomefoldrforestplotfplotfunctionalfunctional<-Gamma.lvmgaussian_logLik.lvmgaussian.lvmgethookgetoutcomegetSASgkgammaGM2.lvmGM3.lvmgofGraphGraph<-graph2lvmGrepheavytailheavytail<-ICid.lvmidplotigraph.lvmiidimagesindexindex<-informationinterceptintercept<-interventionintervention<-InverseIVkillkill<-ksmooth2labels<-latentlatent<-lavalava.optionsloggamma.lvmlogitlogit.lvmlognormal.lvmlvmmakemissingmanifestmat.lvmmeasurementmeasurement_errorMissingMissing<-mixtureModelModel<-modelParmodelsearchmodelVarmomentsmultigroupmultinomialmultinomial.lvmmvn.lvmmvnmixna.pass0NA2xnodecolor<-none.lvmnonlinearnonlinear<-normal.lvmnormcdfnorminvnormpdfNRoddsoffdiagoffdiag<-offdiagsones.lvmORordinalordinal<-ordregpairwise_diffpairwise.diffparameterparameter<-parentspareto.lvmparlabelsparposparsparsedesignpartialcorpathpcorPDpdfconvertplot_regionplot.simplotConfpoisson.lvmpredict_glmpredictlvmPrintprobit.lvmprocformularandomsloperandomslope<-Range.lvmregfixregressionregression<-revdiagrevdiag<-rmvarrmvar<-rmvn0rootsrot2Drot3Drotate2rsqscheffescoreSequence.lvmsimsinksspaghettiSpecialsstarter.multigroupstartvaluesstartvalues0startvalues1startvalues2student.lvmsummary.simsurfacetest_waldthreshold.lvmtigoltimedeptimedep<-toformulatotaleffectstrtransform<-trimtwostagetwostageCVuniform.lvmvar_icvariancevariance<-variancesvarsvecwaitweibull.lvmWeightswkmwrapvecx2NAzibreg
Dependencies:clicodetoolsdigestfuturefuture.applyglobalslatticelistenvMatrixnumDerivparallellyprogressrSQUAREMsurvival
Estimating partial correlations with lava
Last update: 2026-10-13
Started: 2026-08-14
Non-linear latent variable models and error-in-variable models
Estimation | Cross-validation | Specification of general functional forms | Mixture models | SessionInfo | Bibliography
Last update: 2026-10-13
Started: 2026-08-14
The Art of Influence
Influence functions | Working with influence functions with lava::estimate | Examples | Example: population mean | Example: generalized linear model | Example: right-censored outcomes | Example: random effects model / structural equation model | Example: quantile | Combining influence functions | Estimators computed on different subsets | Based on the above Lemma we can therefore combine estimators that are estimatedon different datasets. Let the observed data be$(Z_ | \begin | Renaming and subsetting parameters | Clustered data (non-iid case) | Computational aspects | IF building blocks: transformations and the delta theorem | Example: Pearson correlation | Linear contrasts and hypothesis testing | Standardization | Average Treatment Effects | SessionInfo | Bibliography
Last update: 2026-10-13
Started: 2026-08-14
