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) , includes multiple stages of randomization, where participants are randomized to an initial treatment in the first stage and then subsequently re-randomized between treatments in the following stage.

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) . \n (2) IPWE-based global test (hypotehsis test, power calculation) by Ogbagaber S.B., Karp J., Wahed A.S. (2016) . \n (3) G estimates (pairwise comparison, power calculation) by Lavori R., Dawson P.W. (2012) . \n (4) IPW estimates (pairwise comparison, power calculation) by Murphy S.A. (2005) . \n (5) SAMRT with adaptive randomization by Cheung Y.K. (2015) .

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) for more details.

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) and Lee, D. S., Lemieux, T. (2010) .

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+) .

Extra Methods for Sparse Matrices

Extends sparse matrix and vector classes from the 'Matrix' package by providing: (a) Methods and operators that work natively on CSR formats (compressed sparse row, a.k.a. 'RsparseMatrix') such as slicing/sub-setting, assignment, rbind(), mathematical operators for CSR and COO such as addition ("+") or sqrt(), and methods such as diag(); (b) Multi-threaded matrix multiplication and cross-product for many types, including the 'float32' type from 'float'; (c) Coercion methods between pairs of classes which are not present in 'Matrix', such as 'dgCMatrix' -> 'ngRMatrix', as well as convenience conversion functions; (d) Utility functions for sparse matrices such as sorting the indices or removing zero-valued entries; (e) Fast transposes that work by outputting in the opposite storage format; (f) Faster replacements for many 'Matrix' methods for all sparse types, such as slicing and elementwise multiplication. (g) Convenience functions for sparse objects, such as 'mapSparse' or a shorter 'show' method.