In FielDHub: A Shiny App for Design of Experiments in Life Sciences

library(FielDHub)
knitr::opts_chunk$set(
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  fig.path = "man/figures/README-",
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FielDHub 1.3.1

Photo by Markus Spiske on Pexels

2023/04/18

FielDHub

Didier Murillo

I am thrilled to announce the release of FielDHub v1.3.1, which is the culmination of dedicated effort and hard work. This updated version includes improvements and new features, including sparse allocation, optimized multi-location p-rep, and more. We are excited to share these new capabilities with our users.

Changelog

New Features in the Shiny App

• Added a module to generate Sparse allocation.

• Added a module for generating Optimized Multi-Location Partially Replicated (p-rep).

• Added vignettes and help documentation for all the new modules; Sparse Allocations and Optimized Multi-Location Partially Replicated (p-rep) Designs in the app.

Enhancements:

• Renamed the Partially Replicated module to Single and Multi-Location p-rep

• Improved the usability of the field dimensions dropdown menu by reordering the options based on the absolute value of the difference between the number of rows and columns for each option. This affects unreplicated and partially replicated design modules.

Fix bugs:

• Fixed issue: Upload data in the CRD module.

New Features in the FielDHub Package:

Standalone Functions

• Created the do_optim() function. This function generates the sparse or p-rep allocation to multiple locations. It optimized the allocation by using incomplete blocks.

• Created the sparse_allocation() function. This new function uses the other function, do_optim(), to generate the sparse allocation, then uses the function diagonal_arrangement() to create unreplicated designs across multiple locations.

• Created the multi_location_prep() function. It uses within the optimization function do_optim() to generate the partially replicated (p-rep) allocation, then uses the function partially_replicated() to create the p-rep designs across multiple locations.

• Created the pairs_distance() function. This function calculates pairwise distances between all elements in a matrix that appears twice or more.

• Created the swap_pairs() function. It swaps pairs in a matrix of integers and optimizes the p-rep design. This function modifies the input matrix $X$ to ensure that the distance between any two occurrences of the same integer is at least a distance $d$, by swapping one of the occurrences with a random occurrence of a different integer that is at least $d$ away. The function starts with starting dist at $d = 3$ and increases it by $1$ until the algorithm no longer converges or the max number of iterations have been performed.

• Created the search_matrix_values() function. It looks for values that appear in the same row in a matrix and return the row number, value, and frequency.

• Added optimization process for the partially replicated (p-rep) designs. It uses the function swap_pairs().

• Added vignettes and help documentation for all the new functions.

Enhancements:

• partially_replicated() accepts custom field dimensions at each location. For example, nrows = c(23, 20, 20) and ncols = c(20, 23, 23) are the field rows and columns for the three environments.

• Code refactoring on the diagonal_arrangement() function.

• Code refactoring on the utility function pREP().

• Avoid cyclic reps in incomplete block designs when the number of treatments is square.

Acknowledgements

FielDHub v1.3.1 results from dedicated effort and contribution from a group of individuals who have made this release possible. We want to extend our sincere gratitude to Mr. Jean-Marc Montpetit for his contributions to developing the swap_pairs() and pairs_distance() functions. His help enhanced the optimization of the partially replicated (p-rep) design. Thank you, Dr. Salvador Gezan, for your contributions and fresh ideas. We also thank Matthew Seefeldt for helping write documentation and Johan Aparicio for his ideas and reporting bugs. Thanks, Ana María Heilman, for the support and leadership throughout the development process.

FielDHub documentation built on Oct. 20, 2023, 1:07 a.m.