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Multiple, Unpacking, and Destructuring Assignment
Provides a %<-% operator to perform multiple, unpacking, and destructuring assignment in R. The operator unpacks the right-hand side of an assignment into multiple values and assigns these values to variables on the left-hand side of the assignment.
Sequential Multiple Assignment Randomized Trial Design
SMART trial design, as described by He, J., McClish, D., Sabo, R. (2021)
Sequential Multiple Assignment Randomized Trial and Adaptive Randomization
Primary data analysis for sequential multiple assignment randomization trial (SMART) and calibration tools for clinical trial planning purposes. \n
The methods used for this package include: \n
(1) Likelihood-based global test (hypothesis test, power calculation) by in Zhong X., Cheng, B., Qian M., Cheung Y.K. (2019)
Small N Sequential Multiple Assignment Randomized Trial Methods
Consolidated data simulation, sample size calculation and
analysis functions for several snSMART (small sample sequential,
multiple assignment, randomized trial) designs under one library. See
Wei, B., Braun, T.M., Tamura, R.N. and Kidwell, K.M. "A Bayesian
analysis of small n sequential multiple assignment randomized trials
(snSMARTs)." (2018) Statistics in medicine, 37(26), pp.3723-3732
Wrap R Tools for Debugging and Parametric Programming
Tools for writing and debugging R code. Provides: '%.>%' dot-pipe (an 'S3' configurable pipe), unpack/to (R style multiple assignment/return), 'build_frame()'/'draw_frame()' ('data.frame' example tools), 'qc()' (quoting concatenate), ':=' (named map builder), 'let()' (converts non-standard evaluation interfaces to parametric standard evaluation interfaces, inspired by 'gtools::strmacro()' and 'base::bquote()'), and more.
Implementation of the minMSE Treatment Assignment Method for One or Multiple Treatment Groups
Performs treatment assignment for (field) experiments considering available, possibly multivariate and continuous, information (covariates, observable characteristics), that is: forms balanced treatment groups, according to the minMSE-method as proposed by Schneider and Schlather (2017)
Power Analysis for a SMART Design
A set of tools for determining the necessary sample size
in order to identify the optimal dynamic treatment regime
in a sequential, multiple assignment, randomized trial (SMART).
Utilizes multiple comparisons with the best methodology
to adjust for multiple comparisons.
Designed for an arbitrary SMART design. Please see Artman (2018)
Regression Discontinuity Design Application
Estimation of both single- and multiple-assignment Regression Discontinuity Designs
(RDDs). Provides both parametric (global) and non-parametric (local) estimation choices for
both sharp and fuzzy designs, along with power analysis and assumption checks.
Introductions to the underlying logic and analysis of RDDs are in
Thistlethwaite, D. L., Campbell, D. T. (1960)
Sample Size for SMART Designs in Non-Surgical Periodontal Trials
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+)
Covariate Balancing Propensity Score
Implements the covariate balancing propensity score (CBPS) proposed
by Imai and Ratkovic (2014)