Package index • mets

All functions

ACTG175: ACTG175, block randomized study from speff2trial package
CPH_HPN_CRBSI: Rates for HPN program for patients of Copenhagen Cohort
ClaytonOakes(): Clayton-Oakes model with piece-wise constant hazards
Dbvn(): Derivatives of the bivariate normal cumulative distribution function
Event(): Event history object
Grandom_cif(): Additive Random effects model for competing risks data for polygenetic modelling
IC(<phreg>): Influence Functions for phreg objects
LinSpline(): Simple linear spline
TRACE sTRACE tTRACE: The TRACE study group of myocardial infarction
WA_recurrent(): While-Alive Estimands for Recurrent Events
WA_reg(): While-Alive Regression for Recurrent Events
aalenMets(): Fast Additive Hazards Model with Robust Standard Errors
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(): Estimate Casewise Concordance Using Binomial Regression
binregG(): G-Estimator for Binomial Regression Model (Standardized Estimates)
binregRatio(): Percentage of Years Lost Due to a Cause Regression
binregTSR(): Two-Stage Randomization for Survival or Competing Risks Data
biprobit(): Bivariate Probit model
blocksample(): Block sampling
bmt: The Bone Marrow Transplant Data
bptwin(): Liability model for twin data
calgb8923: CALGB 8923, twostage randomization SMART design
casewise(): Estimate Casewise Concordance from prodlim Objects
casewise_bin(): Casewise Concordance from Concordant/Discordant Counts
rr_cif() or_cif() random.cif() Grandom.cif() predictPairPlack() npc() nonparcuminc() plotcr(): Non-parametric Cumulative Incidence Functions
cif(): Cumulative Incidence with Robust Standard Errors
cif_yearslost(): Restricted Mean Time Lost for Competing Risks
cifreg(): Cumulative Incidence Function (CIF) Regression
cifregFG(): Fine-Gray Cumulative Incidence Function Regression
cluster_index(): Finds subjects related to same cluster
coarse_clust(): Coarsen Cluster Identifiers
concordanceCor(): Concordance Computes concordance and casewise concordance
cor_cif(): Cross-odds-ratio, OR or RR risk regression for competing risks
count_history(): Compute cumulative event counts as time-dependent covariates
cumoddsreg(): Cumulative Odds Regression for Discrete Time Data
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
dlag(): Lag operator
dprint(): list, head, print, tail
dreg(): Regression for data frames with dutility call
drelevel(): relev levels for data frames
drop.specials(): Remove Special Terms from a Formula
dsort(): Sort data frame
dspline(): Simple linear spline
dtable(): tables for data frames
dtransform(): Transform that allows condition
event_split(): event_split (SurvSplit).
event_split2(): Event split with two time-scales, time and gaptime
eventpois(): Extract survival estimates from lifetable analysis
extendCums(): Extend Cumulative Hazard Functions to Common Time Range
familyclusterWithProbands_index(): Finds all pairs within a cluster (famly) with the proband (case/control)
familycluster_index(): Finds all pairs within a cluster (family)
fast.approx(): Fast approximation
fast.cluster(): Fast Cluster Index Conversion
fast.pattern(): Fast pattern
fast.reshape(): Fast reshape
faster.reshape(): Fast Reshape from Long to Wide Format
folds(): Generate Random Fold Indices for Cross-Validation
force.same.cens() force_same_cens(): Force Same Censoring Within Clusters
glm_IPTW(): IPTW GLM, Inverse Probabibilty of Treatment Weighted GLM
gof(<phreg>): Goodness-of-Fit for Cox PH Regression (Proportionality)
gofM_phreg(): Goodness-of-Fit for Cox Covariates (Model Matrix)
gofZ_phreg(): Goodness-of-Fit for Cox Covariates (Linearity)
grouptable(): Create Group Contingency Table from Clustered Data
haplo: haplo fun data
haplo_surv_discrete(): Discrete Time-to-Event Haplotype Analysis
hfactioncpx12: hfaction, subset of block randomized study HF-ACtion from WA package
iidBaseline(): Influence Functions or IID Decomposition of Baseline
ilap(): Inverse Laplace Transform Helper
interval_logitsurv_discrete(): Discrete Time-to-Event Analysis with Interval Censoring
ipw(): Inverse Probability of Censoring Weights
ipw2(): Inverse Probability of Censoring Weights
jumptimes(): Extract Event (Jump) Times
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.options(): Set global options for mets
migr: Migraine data
mlogit(): Multinomial Regression Based on phreg
multcif: Multivariate Cumulative Incidence Function example data set
np: np data set
robust.basehaz.phreg() summarybase.phreg() conftype(): Robust Baseline Hazard Standard Errors
phreg(): Fast Cox Proportional Hazards Regression
phreg_IPTW(): IPTW Cox Regression (Inverse Probability of Treatment Weighted)
phreg_rct(): Lu-Tsiatis More Efficient Log-Rank for Randomized Studies with Baseline Covariates
phreg_weibull(): Weibull-Cox regression
plack_cif(): plack Computes concordance for or.cif based model, that is Plackett random effects model
plot(<phreg>): Plotting the baselines of stratified Cox
plot_twin(): Scatter plot function
pmvn(): Multivariate normal distribution function
predict(<mlogit>): Predictions from Multinomial Regression
predict(<phreg>): Predictions from Proportional Hazards Model
print(<casewise>): prints Concordance test
prob_exceed_recurrent(): Estimate the probability of exceeding k recurrent events by time t
prt: Prostate data set
random_cif(): Random effects model for competing risks data
ratioATE(): Ratio of Average Treatment Effects
rchaz(): Simulation of Piecewise Constant Hazard Model (Cox)
rchazl(): Multiple Cause Piecewise Constant Hazard Simulation
rcrisk(): Simulation of Piecewise constant hazard models with two causes (Cox).
recreg(): Recurrent Events Regression with Terminal Event
recregIPCW(): IPCW Estimator for Recurrent Events
recurrent_marginal() recurrentMarginal(): Marginal mean estimation for recurrent events with a terminal event
resmeanATE(): Average Treatment Effect for Restricted Mean Time
resmeanIPCW(): Restricted IPCW Mean for Censored Survival Data
resmean_phreg(): Restricted Mean for Stratified Kaplan-Meier or Cox Model
rweibullcox(): Simulate observations from a Weibull distribution
sim_ClaytonOakes(): Simulate from the Clayton-Oakes frailty model
sim_ClaytonOakesWei(): Simulate from the Clayton-Oakes frailty model
sim_GLcox(): Simulation of Two-Stage Recurrent Events Data
sim_cif(): Simulation of Output from Cumulative Incidence Regression Model
sim_multistate(): Simulation of Illness-Death Model
sim_multistateII(): Illness-Death Competing Risks with Two Causes of Death
sim_phreg(): Simulation of Output from Cox Model
sim_phregs(): Simulation of Cause-Specific Cox Models
sim_recurrent(): Simulate recurrent events with a single event type and a terminal event
sim_recurrentII(): Simulate recurrent events with two event types and a terminal event
sim_recurrentTS(): Simulate recurrent events from a two-stage model with structured gamma frailties
sim_recurrent_ts(): Simulate recurrent events from a two-stage Cox or Ghosh-Lin model
sumstrata() cumsumstrata() revcumsumstrata() revcumsum() matdoubleindex() mdi(): Stratified Cumulative and Summary Operations
summary(<cor>): Summary for dependence models for competing risks
summaryGLM(): Reporting OR (exp(coef)) from glm with binomial link and glm predictions
summaryTimeobject(): Summarize a Time-Varying Estimate with Confidence Bands
surv_boxarea(): Bivariate Survival Data on Rectangular Regions
survival.twostage() matplot.mets.twostage() alpha2spear() alpha2kendall() piecewise_twostage() piecewise_data(): Survival Twostage Helpers
survivalG(): G-Estimator for Cox and Fine-Gray Models
survival_twostage(): Twostage Survival Model for Multivariate Survival Data
test_casewise(): Test for Independence Using Casewise Concordance
test_conc(): Compare Two Concordance Estimates
test_logrankRecurrent(): Logrank-type test for comparing recurrent event marginal means between groups
test_marginalMean(): Pepe-Mori Test for Marginal Mean Comparison
tetrachoric(): Estimate parameters from odds-ratio
tie_breaker(): Break ties in event times for recurrent event data
ttpd: ttpd discrete survival data on interval form
p11_binomial_twostage_RV() concordanceTwostage() concordanceTwinACE() kendall_ClaytonOakes_twin_ace() kendall.ClaytonOakes.twin.ace() kendall_normal_twin_ace() ascertained_pairs() twin.polygen.design() ace_family_design() make_pairwise_design(): Concordance Probability from Twostage Model
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
twostageREC(): Fitting of Two-Stage Recurrent Events Random Effects Model