Showing 3 of total 3 results (show query)
bedapub
designit:Blocking and Randomization for Experimental Design
Intelligently assign samples to batches in order to reduce batch effects. Batch effects can have a significant impact on data analysis, especially when the assignment of samples to batches coincides with the contrast groups being studied. By defining a batch container and a scoring function that reflects the contrasts, this package allows users to assign samples in a way that minimizes the potential impact of batch effects on the comparison of interest. Among other functionality, we provide an implementation for OSAT score by Yan et al. (2012, <doi:10.1186/1471-2164-13-689>).
Maintained by Iakov I. Davydov. Last updated 5 months ago.
design-of-experimentsrandomization
8 stars 7.28 score 24 scripts
tylermorganwall
skpr:Design of Experiments Suite: Generate and Evaluate Optimal Designs
Generates and evaluates D, I, A, Alias, E, T, and G optimal designs. Supports generation and evaluation of blocked and split/split-split/.../N-split plot designs. Includes parametric and Monte Carlo power evaluation functions, and supports calculating power for censored responses. Provides a framework to evaluate power using functions provided in other packages or written by the user. Includes a Shiny graphical user interface that displays the underlying code used to create and evaluate the design to improve ease-of-use and make analyses more reproducible. For details, see Morgan-Wall et al. (2021) <doi:10.18637/jss.v099.i01>.
Maintained by Tyler Morgan-Wall. Last updated 27 days ago.
design-of-experimentslinear-modelslinear-regressionmonte-carlooptimal-designspowersplit-plot-designssurvival-analysiscpp
118 stars 6.89 score 35 scripts
chikuang
SLSEdesign:Optimal Regression Design under the Second-Order Least Squares Estimator
With given inputs that include number of points, discrete design space, a measure of skewness, models and parameter value, this package calculates the objective value, optimal designs and plot the equivalence theory under A- and D-optimal criteria under the second-order Least squares estimator. This package is based on the paper "Properties of optimal regression designs under the second-order least squares estimator" by Chi-Kuang Yeh and Julie Zhou (2021) <doi:10.1007/s00362-018-01076-6>.
Maintained by Chi-Kuang Yeh. Last updated 6 months ago.
convex-optimizationcvxdesign-of-experimentsleast-squaresoptimal-designs
4.54 score 2 scripts