Examples: visualization, C++, networks, data cleaning, html widgets, ropensci.

Found 1566 packages in 0.02 seconds

zeallot — by Nathan Teetor, 5 years ago

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.

smartDesign — by Jason Sinnwell, a year ago

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.

SMARTAR — by Tony Zhong, 2 years ago

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) .

snSMART — by Michael Kleinsasser, 20 days ago

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 .

wrapr — by John Mount, 10 months ago

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.

minMSE — by Sebastian O. Schneider, a year ago

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) .

smartsizer — by William Artman, 2 years ago

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.

rddapp — by Felix Thoemmes, 10 months ago

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) .

SMARTp — by Dipankar Bandyopadhyay, 4 years ago

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+) .

CBPS — by Christian Fong, a year ago

Covariate Balancing Propensity Score

Implements the covariate balancing propensity score (CBPS) proposed by Imai and Ratkovic (2014) . The propensity score is estimated such that it maximizes the resulting covariate balance as well as the prediction of treatment assignment. The method, therefore, avoids an iteration between model fitting and balance checking. The package also implements optimal CBPS from Fan et al. (in-press) , several extensions of the CBPS beyond the cross-sectional, binary treatment setting. They include the CBPS for longitudinal settings so that it can be used in conjunction with marginal structural models from Imai and Ratkovic (2015) , treatments with three- and four-valued treatment variables, continuous-valued treatments from Fong, Hazlett, and Imai (2018) , propensity score estimation with a large number of covariates from Ning, Peng, and Imai (2020) , and the situation with multiple distinct binary treatments administered simultaneously. In the future it will be extended to other settings including the generalization of experimental and instrumental variable estimates.