Discrete-Time Multistate Models

Documentation for package ‘dtms’ version 0.4.2

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dtms	Create dtms object
dtms_absorbed	Calculate the distribution of the time until entering an absorbing state
dtms_aggregate	Aggregate data
dtms_backward	Carry states backward
dtms_boot	Bootstrap and block bootstrap
dtms_boot_summary	Summary function for bootstrap results
dtms_censoring	Left censoring, right censoring, and gaps in data
dtms_clean	Cleans data in transition format
dtms_data_summary	Summarize data in transition format
dtms_delta	Calculate delta
dtms_distr_summary	Summary for distributional results
dtms_duration	Generate variable with duration
dtms_expectancy	Calculate state expectancy
dtms_first	Time needed to reach a subset of states for the first time
dtms_fit	Estimate (un)constrained discrete-time multistate model
dtms_format	Reshape data to transition format
dtms_forward	Carry states forward
dtms_fullfit	Estimate unconstrained discrete-time multistate model
dtms_last	Calculate the distribution of the time until a subset of states is left for the last time.
dtms_matrix	Creates a transition matrix from transition probabilities
dtms_nonparametric	Nonparametric estimates of transition probabilities
dtms_occurrence	Generate variable with number of occurrence of state
dtms_plot	Plotting transition probabilities
dtms_probs_summary	Summarize transition probabilities
dtms_reward	Markov chain with rewards
dtms_rewardmatrix	Generate the reward matrix for a Markov chain with rewards
dtms_risk	Calculate the lifetime risk of ever reaching a state
dtms_simplify	Simplify state names
dtms_simulate	Simulation of Markov chains
dtms_source	Source code only from specific lines
dtms_start	Tabulate starting distribution
dtms_survivor	Calculate the survivorship function
dtms_transitions	Predict transition probabilities
dtms_visits	Calculate the distribution of the time spent in a subset of states
simpledata	simpledata: an artificial data set with abstract trajectories
workdata	workdata: simulated working trajectories