Function reference • mets

All functions
`BinAugmentCifstrata()`	Augmentation for Binomial regression based on stratified NPMLE Cif (Aalen-Johansen)
`Bootphreg()`	Wild bootstrap for Cox PH regression
`ClaytonOakes()`	Clayton-Oakes model with piece-wise constant hazards
`Dbvn()`	Derivatives of the bivariate normal cumulative distribution function
`EVaddGam()`	Relative risk for additive gamma model
`Effbinreg()`	Efficient IPCW for binary data
`EventSplit()`	Event split with two time-scales, time and gaptime
`FG_AugmentCifstrata()`	Augmentation for Fine-Gray model based on stratified NPMLE Cif (Aalen-Johansen)
`Grandom.cif()`	Additive Random effects model for competing risks data for polygenetic modelling
`LinSpline()`	Simple linear spline
`TRACE` `sTRACE` `tTRACE`	The TRACE study group of myocardial infarction
`aalenMets()`	Fast additive hazards model with robust standard errors
`aalenfrailty()`	Aalen frailty model
`back2timereg()`	Convert to timereg object
`base1cumhaz`	rate of CRBSI for HPN patients of Copenhagen
`base44cumhaz`	rate of Occlusion/Thrombosis complication for catheter of HPN patients of Copenhagen
`base4cumhaz`	rate of Mechanical (hole/defect) complication for catheter of HPN patients of Copenhagen
`basehazplot.phreg()`	Plotting the baslines of stratified Cox
`bicomprisk()`	Estimation of concordance in bivariate competing risks data
`binomial.twostage()`	Fits Clayton-Oakes or bivariate Plackett (OR) models for binary data using marginals that are on logistic form. If clusters contain more than two times, the algoritm uses a compososite likelihood based on all pairwise bivariate models.
`binreg()`	Binomial Regression for censored competing risks data
`binregATE()`	Average Treatment effect for censored competing risks data using Binomial Regression
`binregCasewise()`	Estimates the casewise concordance based on Concordance and marginal estimate using binreg
`binregG()`	G-estimator for binomial regression model (Standardized estimates)
`binregTSR()`	2 Stage Randomization for Survival Data or competing Risks Data
`biprobit()`	Bivariate Probit model
`blocksample()`	Block sampling
`bmt`	The Bone Marrow Transplant Data
`bptwin()`	Liability model for twin data
`casewise()`	Estimates the casewise concordance based on Concordance and marginal estimate using prodlim but no testing
`casewise.test()`	Estimates the casewise concordance based on Concordance and marginal estimate using timereg and performs test for independence
`cif()`	Cumulative incidence with robust standard errors
`cifreg()`	CIF regression
`cluster.index()`	Finds subjects related to same cluster
`concordanceCor()`	Concordance Computes concordance and casewise concordance
`cor.cif()`	Cross-odds-ratio, OR or RR risk regression for competing risks
`count.history()`	Counts the number of previous events of two types for recurrent events processes
`covarianceRecurrent()`	Estimation of covariance for bivariate recurrent events with terminal event
`daggregate()`	aggregating for for data frames
`dby()`	Calculate summary statistics grouped by
`dcor()`	summary, tables, and correlations for data frames
`dcut()`	Cutting, sorting, rm (removing), rename for data frames
`dermalridges`	Dermal ridges data (families)
`dermalridgesMZ`	Dermal ridges data (monozygotic twins)
`diabetes`	The Diabetic Retinopathy Data
`divide.conquer()`	Split a data set and run function
`divide.conquer.timereg()`	Split a data set and run function from timereg and aggregate
`dlag()`	Lag operator
`doubleFGR()`	Double CIF Fine-Gray model with two causes
`dprint()`	list, head, print, tail
`drcumhaz`	Rate for leaving HPN program for patients of Copenhagen
`dreg()`	Regression for data frames with dutility call
`drelevel()`	relev levels for data frames
`dsort()`	Sort data frame
`dspline()`	Simple linear spline
`dtable()`	tables for data frames
`dtransform()`	Transform that allows condition
`easy.binomial.twostage()`	Fits two-stage binomial for describing depdendence in binomial data using marginals that are on logistic form using the binomial.twostage funcion, but call is different and easier and the data manipulation is build into the function. Useful in particular for family design data.
`eventpois()`	Extract survival estimates from lifetable analysis
`familycluster.index()`	Finds all pairs within a cluster (family)
`familyclusterWithProbands.index()`	Finds all pairs within a cluster (famly) with the proband (case/control)
`fast.approx()`	Fast approximation
`fast.pattern()`	Fast pattern
`fast.reshape()`	Fast reshape
`ghaplos`	ghaplos haplo-types for subjects of haploX data
`glm_IPTW()`	IPTW GLM, Inverse Probaibilty of Treatment Weighted GLM
`gof(<phreg>)`	GOF for Cox PH regression
`gofG.phreg()`	Stratified baseline graphical GOF test for Cox covariates in PH regression
`gofM.phreg()`	GOF for Cox covariates in PH regression
`gofZ.phreg()`	GOF for Cox covariates in PH regression
`hapfreqs`	hapfreqs data set
`haplo.surv.discrete()`	Discrete time to event haplo type analysis
`haploX`	haploX covariates and response for haplo survival discrete survival
`npc` `plotcr` `nonparcuminc` `simnordic` `corsim.prostate` `alpha2kendall` `alpha2spear` `coefmat` `piecewise.twostage` `surv.boxarea` `faster.reshape` `piecewise.data` `simBinPlack` `simBinFam` `simBinFam2` `simSurvFam` `corsim.prostate.random` `simnordic.random` `simCox` `sim` `grouptable` `jumptimes` `folds` `ace.family.design` `ascertained.pairs` `CCbinomial.twostage` `coarse.clust` `concordanceTwinACE` `concordanceTwostage` `fast.cluster` `force.same.cens` `ilap` `kendall.ClaytonOakes.twin.ace` `kendall.normal.twin.ace` `make.pairwise.design` `make.pairwise.design.competing` `matplot.mets.twostage` `object.defined` `p11.binomial.twostage.RV` `predictPairPlack` `simbinClaytonOakes.family.ace` `simbinClaytonOakes.pairs` `simbinClaytonOakes.twin.ace` `simClaytonOakes.family.ace` `simClaytonOakes.twin.ace` `simFrailty.simple` `simCompete.simple` `simCompete.twin.ace` `twin.polygen.design` `procform` `procform3` `procformdata` `drop.specials`	For internal use
`interval.logitsurv.discrete()`	Discrete time to event interval censored data
`ipw()`	Inverse Probability of Censoring Weights
`ipw2()`	Inverse Probability of Censoring Weights
`km()`	Kaplan-Meier with robust standard errors
`lifecourse()`	Life-course plot
`lifetable(<matrix>)` `lifetable(<formula>)`	Life table
`logitSurv()`	Proportional odds survival model
`mediatorSurv()`	Mediation analysis in survival context
`medweight()`	Computes mediation weights
`melanoma`	The Melanoma Survival Data
`mena`	Menarche data set
`mets-package`	Analysis of Multivariate Events
`mets.options()`	Set global options for `mets`
`migr`	Migraine data
`mlogit()`	Multinomial regression based on phreg regression
`multcif`	Multivariate Cumulative Incidence Function example data set
`np`	np data set
`phreg()`	Fast Cox PH regression
`phregR()`	Fast Cox PH regression and calculations done in R to make play and adjustments easy
`phreg_IPTW()`	IPTW Cox, Inverse Probaibilty of Treatment Weighted Cox regression
`phreg_rct()`	Lu-Tsiatis More Efficient Log-Rank for Randomized studies with baseline covariates
`plack.cif()`	plack Computes concordance for or.cif based model, that is Plackett random effects model
`pmvn()`	Multivariate normal distribution function
`predict(<phreg>)`	Predictions from proportional hazards model
`print(<casewise>)`	prints Concordance test
`prob.exceed.recurrent()`	Estimation of probability of more that k events for recurrent events process
`prt`	Prostate data set
`random.cif()`	Random effects model for competing risks data
`rchaz()`	Simulation of Piecewise constant hazard model (Cox).
`rchazC()`	Piecewise constant hazard distribution
`rcrisk()`	Simulation of Piecewise constant hazard models with two causes (Cox).
`recreg()`	Recurrent events regression with terminal event
`recurrentMarginal()`	Fast recurrent marginal mean when death is possible
`resmean.phreg()`	Restricted mean for stratified Kaplan-Meier or Cox model with martingale standard errors
`resmeanATE()`	Average Treatment effect for Restricted Mean for censored competing risks data using IPCW
`resmeanIPCW()`	Restricted IPCW mean for censored survival data
`rpch()`	Piecewise constant hazard distribution
`sim.cause.cox()`	Simulation of cause specific from Cox models.
`sim.cif()`	Simulation of output from Cumulative incidence regression model
`sim.cox()`	Simulation of output from Cox model.
`simAalenFrailty()`	Simulate from the Aalen Frailty model
`simClaytonOakes()`	Simulate from the Clayton-Oakes frailty model
`simClaytonOakesWei()`	Simulate from the Clayton-Oakes frailty model
`simMultistate()`	Simulation of illness-death model
`simRecurrentII()`	Simulation of recurrent events data based on cumulative hazards II
`simRecurrentTS()`	Simulation of recurrent events data based on cumulative hazards: Two-stage model
`summary(<cor>)`	Summary for dependence models for competing risks
`summaryGLM()`	Reporting OR (exp(coef)) from glm with binomial link and glm predictions
`survival.twostage()`	Twostage survival model for multivariate survival data
`survivalG()`	G-estimator for Cox and Fine-Gray model
`test.conc()`	Concordance test Compares two concordance estimates
`tetrachoric()`	Estimate parameters from odds-ratio
`ttpd`	ttpd discrete survival data on interval form
`twin.clustertrunc()`	Estimation of twostage model with cluster truncation in bivariate situation
`twinbmi`	BMI data set
`twinlm()`	Classic twin model for quantitative traits
`twinsim()`	Simulate twin data
`twinstut`	Stutter data set
`twostageMLE()`	Twostage survival model fitted by pseudo MLE

Reference

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