# Stochastic Process Model for Analysis of Longitudinal and Time-to-Event Outcomes

## Documentation for package ‘stpm’ version 1.7.12

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

stpm-package | Stochastic Process Model for Analysis of Longitudinal and Time-to-Event Outcomes
assign_to_global | function loading results in global environment
ex_data | This is the longitudinal genetic dataset.
func1 | An internal function to compute m and gamma based on continuous-time model (Yashin et. al., 2007)
get.column.index | An internal function to obtain column index by its name
getNextY.cont | An internal function to compute next Y based on continous-time model (Yashin et. al., 2007)
getNextY.cont2 | An internal function to compute next value of physiological variable Y
getNextY.discr | An internal function to compute the next value of physiological variable Y based on discrete-time model (Akushevich et. al., 2005)
getNextY.discr.m | An internal function to compute next m based on dicrete-time model
getPrevY.discr | An internal function to compute previous value of physiological variable Y based on discrete-time model
getPrevY.discr.m | An internal function to compute previous m based on discrete-time model
longdat | This is the longitudinal dataset.
LRTest | Likelihood-ratio test
m | An internal function to compute m from
make.short.format | An internal function which construct short data format from a given long
mu | An internal function to compute mu
prepare_data | Data pre-processing for analysis with stochastic process model methodology.
prepare_data_cont | Prepares continuouts-time dataset.
prepare_data_discr | Prepares discrete-time dataset.
sigma_sq | An internal function to compute sigma square analytically
simdata_cont | Multi-dimensional simulation function for continuous-time SPM.
simdata_discr | Multi-dimension simulation function
simdata_gamma_frailty | This script simulates data using familial frailty model. We use the following variation: gamma(mu, ssq), where mu is the mean and ssq is sigma square. See: https://www.rocscience.com/help/swedge/webhelp/swedge/Gamma_Distribution.htm
simdata_time_dep | Simulation function for continuous trait with time-dependant coefficients.
sim_pobs | Multi-dimension simulation function for data with partially observed covariates (multidimensional GenSPM) with arbitrary intervals
spm | A central function that estimates Stochastic Process Model parameters a from given dataset.
spm.impute | Multiple Data Imputation with SPM
spm_continuous | Continuous multi-dimensional optimization
spm_cont_lin | Continuous multi-dimensional optimization with linear terms in mu only
spm_cont_quad_lin | Continuous multi-dimensional optimization with quadratic and linear terms
spm_con_1d | Fitting a 1-D SPM model with constant parameters
spm_con_1d_g | Fitting a 1-D genetic SPM model with constant parameters
spm_discrete | Discrete multi-dimensional optimization
spm_pobs | Continuous-time multi-dimensional optimization for SPM with partially observed covariates (multidimensional GenSPM)
spm_projection | A data projection with previously estimated or user-defined parameters. Projections are constructed for a cohort with fixed or normally distributed initial covariates.
spm_time_dep | A function for the model with time-dependent model parameters.
stpm | Stochastic Process Model for Analysis of Longitudinal and Time-to-Event Outcomes
trim | Returns string w/o leading or trailing whitespace
trim.leading | Returns string w/o leading whitespace
trim.trailing | Returns string w/o trailing whitespace
vitstat | Vital (mortality) statistics.