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stpm (version 1.1.2)

Stochastic Model for Analysis of Longitudinal Data

Description

Utilities to estimate parameters of stochastic process and modeling survival trajectories and time-to-event outcomes observed from longitudinal studies. Miscellaneous function for data preparation is also provided. For more information, see: "Stochastic model for analysis of longitudinal data on aging and mortality" by Yashin A. et al, 2007, Mathematical Biosciences, 208(2), 538-551 .

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Version

Install

install.packages('stpm')

Monthly Downloads

202

Version

1.1.2

License

GPL

Maintainer

Ilya Y Zhbannikov

Last Published

March 10th, 2016

Functions in stpm (1.1.2)

Discrete multi-dimensional optimization

Sets upper boundaries.

Multi-dimensional simulation function for continuous trait.

prepare_data_discr

Prepares discrete-time dataset.

spm_time_dep : a function that estimates parameters from the model with time-dependent coefficients.

Data pre-processing for analysis with stochastic process model methodology.

Multi-dimensional simulation function for continuous trait. Similar to simdata_cont(...) but much faster.

A sub-finction that corresponds to the spm_time_dep(...)

Returns string w/o leading whitespace

A data projection with previously estimated or user-defined parameters. Projections are constructed for a cohort with fixed or normally distributed initial covariates.

Vital (mortality) statistics.

Returns string w/o leading or trailing whitespace

prepare_data_cont

Prepares continuouts-time dataset.

This is the longitudinal dataset.

simdata_time_dep

Simulation function for continuous trait with time-dependant coefficients.

Multi-dimension simulation function

Continuous multi-dimensional optimization

Sets lower boundaries.

Returns string w/o trailing whitespace

A central function that estimates Stochastic Process Model parameters a from given dataset.

Filling the last cell