| 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. |