
<!-- NEWS.md is generated from NEWS.Rmd. Please edit that file -->

# Changelog

# FielDHub 1.3.1

### 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.

# FielDHub 1.2.0

### Shiny App

- Added a `help` menu option in the app that connect you directly to the
  documentation available in our GitHub repository.

- Added **vignettes and help documentation** for all standard functions
  and modules available for all designs in the app.

- Added capability for making multiple randomizations across different
  **locations** to the unreplicated, partially replicated, lattice,
  RCBD, factorial, split-plot, split-split-plot, strip-plot, IBD, and
  RCD designs.

- Added capability to produce **heatmap visualizations** of simulated
  data for all experimental designs.

- Added action buttons to **copy and save field maps and field book
  outputs to Excel.**

- Added **factorization options that aid users in the creation of
  randomizations and mapping layouts** for the unreplicated and
  partially replicated designs. Previous version required users to do
  the \* mathematical calculation a priori.

- Added **filters and search** boxes for the field book tables.

- Updated UI/UX design for home page.

- Grouped single diagonal arrangement, multiple diagonal arrangement,
  optimized arrangement and augmented RCB designs under one single
  module.

- Added action run button to all experimental designs to prevent
  reactivity issues with the application.

- Improved and standardized user experience features and readability
  access.

- Improved error logging messages. Added features to inform end-users on
  the utilization of correct input data file formats and associated
  metadata/columns, checking for duplicate values in input files, as
  well as data type verification.

- Added the plot() method to the FielDHub package to display the field
  layout of the field book for all designs.

- Added **additional field layout visualization/map options** to all
  experimental designs. Previous version only had mapping options for
  unreplicated and p-rep designs.

- Added a drop-down menu to display a **multiple layout mapping option
  shown by entry number and by plot** for all experimental designs. This
  means, that now you can visualize each randomization layout option for
  each of the locations you input.

- Added an option for repeating whole entries/experiments in the
  unreplicated diagonal arrangement design with multiple experiments
  (previously called decision blocks).

- Added a check box feature in the Augmented RCB design to allow for the
  creation of nurseries with the option of randomizing experimental
  entries or not. If user decides to leave this option unchecked, only
  the checks will be randomized, and the experimental entries will be
  shown in consecutive order.

- Added a check box option to the RCB design to allow for a continuous
  plot numbering independently of the rep or block number. Previous
  version coded the replication into the plot number (i.e., 101 =rep1,
  201=rep2, etc.).

- Fixed a restriction in the RCBD mapping layout to allow for the use of
  more than 25 entries. PS: There are better designs when the number of
  entries is higher than 25.

### Standalone Functions in `FielDHub` Package

- `partially_replicated()` now generates randomization across multiple
  locations/sites.

- `diagonal_arrangement()` now generates randomization across multiple
  locations/sites.

- `optimized_arrangement()` now generates randomization across multiple
  locations/sites.

- `partially_replicated()` now allows that all entries/treatments have
  replicates. Before, it required at least some unreplicated entries.

- Functions `optimized_arrangement()`, `diagonal_arrangement()` and
  `partially_replicated()` now return feedback if the input dimensions
  `nrows` and `ncols` are incorrect.

- `RCBD()` now includes an argument (`continuous`) to manage the way it
  sets up the plotting number.

- `RCBD_augmented()` now allows customization of the field dimensions by
  inputting the number of rows and columns through `nrows` and `ncols`
  arguments.

- `RCBD_augmented()` now returns feedback if the input dimensions
  `nrows` and `ncols` do not match the data entered.

- `RCBD_augmented()` when `random = FALSE` now allows only randomizing
  the checks/controls if the user wants.

- Fixed a bug in `full_factorial()` for the CRD factorial design that
  prevented the option of including all possible factorial combinations.

- Added a method `print()` of class `fieldLayout`. See
  [print()](https://didiermurillof.github.io/FielDHub/reference/print.fieldLayout.html).

- Added a method `plot()` of class `FieldHub` that returns an object of
  class `fieldLayout`. See
  [plot()](https://didiermurillof.github.io/FielDHub/reference/plot.FielDHub.html).
  The method `plot()` can plot a field layout for any of the designs
  output. It is possible to pass arguments such as the location, layout
  order and others. For more detail see [plot(), print() and summary()
  methods in
  FielDHub](https://didiermurillof.github.io/FielDHub/articles/methods.html).

- Fixed a bug in `diagonal_arrangement()` when `kindExpt = DBUDC`. The
  problem affected the random distribution of the checks for the case of
  unbalanced control plot numbers for each experiment.

- Fixed a bug in `diagonal_arrangement()` when `kindExpt = DBUDC`. The
  problem affected merging data between the user data and randomization
  data when users wanted replicated entries across experiments.

# FielDHub 0.1.0

CRAN release.
