Package index
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BinaryBrierScore() - Binary Brier Score
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CIndexCRisks() - Concordance index for competing risks
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Landmarking-class - S4 class for performing a landmarking analysis
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Landmarking() - Creates an S4 class for a landmarking analysis
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compute_risk_sets(<Landmarking>) - Compute the list of individuals at risk at landmark times
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compute_risk_sets() - Compute the list of individuals at risk at landmark times
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epileptic - Dose calibration of anti-epileptic drugs data
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fit_lcmm_() - Fits an LCMM model
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fit_longitudinal(<Landmarking>) - Fits the specified longitudinal model for the latent processes underlying the relevant time-varying covariates, up until the landmarking times
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fit_longitudinal() - Fits the specified longitudinal model for the latent processes underlying the relevant time-varying covariates, up until the landmarking times
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fit_survival(<Landmarking>) - Fits the specified survival model at the landmark times and up to the horizon times specified by the user
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fit_survival() - Fits the specified survival model at the landmark times and up to the horizon times specified by the user
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performance_metrics(<Landmarking>) - Performance metrics
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performance_metrics() - Performance metrics
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plot(<Landmarking>) - Plots survival curves for the fitted landmarking models.
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predict_lcmm_() - Makes predictions from an LCMM model
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predict_longitudinal(<Landmarking>) - Make predictions for time-varying covariates at specified landmark times
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predict_longitudinal() - Make predictions for time-varying covariates at specified landmark times
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predict_survival(<Landmarking>) - Make predictions for time-to-event outcomes at specified horizon times
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predict_survival() - Make predictions for time-to-event outcomes at specified horizon times
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split_wide_df() - Split a wide dataframe containing static and dynamic covariates and splits in into a dataframe with the static covariates and a list of dataframes, each associated to a dynamic covariate.