Bayesian Set of Best Dynamic Treatment Regimes and Sample Size
in SMARTs
person("William", "Artman", email = "William_Artman@URMC.Rochester.edu", role = c("aut", "cre"))
Permits determination of a set of
optimal dynamic treatment regimes and sample size for SMART design
in the Bayesian setting. Please see Artman (2020) <arXiv:2008.02341>.

Permits determination of a set of
optimal dynamic treatment regimes and sample size for a SMART design
in the Bayesian setting. Please see Artman (2020) <arXiv:2008.02341>.

Bayesian Set of Best Dynamic Treatment Regimes and Sample Size
in SMARTs for Binary Outcomes
Permits determination of a set of
optimal dynamic treatment regimes and sample size for a SMART design
in the Bayesian setting with binary outcomes. Please see Artman (2020) <arXiv:2008.02341>.

Sample Size for SMART Designs in Non-Surgical Periodontal Trials
Jing Xu, Dipankar Bandyopadhyay, Douglas Azevedo, Bibhas Chakraborty
Sample size calculation to detect dynamic treatment regime (DTR) effects based on change in clinical attachment level (CAL) outcomes from a non-surgical chronic periodontitis treatments study. The experiment is performed under a Sequential Multiple Assignment Randomized Trial (SMART) design. The clustered tooth (sub-unit) level CAL outcomes are skewed, spatially-referenced, and non-randomly missing. The implemented algorithm is available in Xu et al. (2019+) <arXiv:1902.09386>.
covr, sn (>= 1.5), mvtnorm (>= 1.0), stats, methods

provides functions for particle filtering, auxiliary particle filtering and sequential Monte Carlo algorithms

particle filtering, auxiliary particle filtering and sequential Monte Carlo algorithms

The fit for marglinalized linear model with SMCP penalties.
A package containing functions that generate microarray
data, fit the marginalized linear model with MCP and QA
penalties and conduct cross-validation to select the tuning
parameter.

Smoothed minimax concave penalization (SMCP) method for
genome-wide association studies.
A package containing functions that fit the marginalized
linear model with SMCP.