TSDFGS 3.0.0

R stays the interface; the computation moved to C++ (Rcpp and RcppEigen). All exported functions, their names, and their arguments are those of v2.4.2, so v2 scripts run unchanged.

New criteria and license

• 15 training set criteria were added (A-, D-, E-, G-optimality, PEVmax, MSPE, GVoverall, GVaverage, Avg_GRM, Avg_GRM_self, Avg_GRM_MinMax, CDmean, CDmean(v2), PAM, CDmax). Select one with optTrain(method = ); score a given set with its *_score() function; list them with criteria(). Each has a page on the website with the original reference, the authors’ repository, and a license review.
• optTrain() has a lambda argument (ridge penalty).
• The license is GPL (>= 3), as in v2.4.2. STPGA (GPL-3) and TSODBG (GPL >= 3) were used to check results; no code was copied.

Multithreading

• SSDFGS(), FGCM(), and nt2r() run independent searches in parallel with OpenMP (multi.threads = TRUE or threads = n) instead of parallel::mclapply. Each search has its own random generator, so values do not depend on the number of threads. They differ from the serial run with the same seed. Benchmark: bench/bench_openmp.R.
• The package no longer imports parallel; the default thread count comes from OpenMP (processors available to the process, limited by OMP_NUM_THREADS).
• optTrain(threads = ) sets the threads Eigen may use inside one search (useful for several hundred columns).

Published method unchanged

• r_score(), optTrain(), and SSDFGS() keep the method of Ou & Liao (2019, doi:10.1007/s00122-019-03387-0). The criterion values equal the v2.4.2 values to about 1e-7 (v2.4.2 returned single-precision numbers).
• optTrain() keeps the search moves, the order of the random numbers, the default number of iterations, and the stopping rule. With options(TSDFGS.legacy_float = TRUE) and the same seed it returns the training sets recorded from v2.4.2 for all twelve routes (with or without sub-populations, targeted or not, three criteria). This is tested.
• r_score() still uses the scalar 1 - 1/n0 where a centering matrix may be intended. It is kept on purpose, so that results do not change.

Faster

• The scores no longer form n0 x n0 matrices. They use matrices of size min(n, p) (primal or dual form), a Cholesky solve instead of an explicit inverse, and a precomputed test-set term when the test set is fixed. See the article “How the scores are computed”.
• With more columns than training rows (for example markers), the scores use the Gram matrix of all rows, built once per search. On the rice PCs this makes 404 PCs 13 to 106 times faster than v2.4.2 on one core.
• The search (hill climbing) runs in C++: no row copies in R, no growing vectors.
• The score vectors of the search are allocated once.
• Benchmark: bench/bench_speed.R; results in the article “Moving from v2 to v3”.

Fixes

• optTrain() no longer fails with “the condition has length > 1” when min.iter is smaller than 5 * sqrt(length(cand) * n.train) (negative look-back index in the stopping rule).
• Drawing from a group or pool of size 1 no longer samples from 1:x.
• With sub-populations and no test set, cand need not be 1:nrow(geno).
• Clear errors for missing values, n.train larger than cand, and row numbers outside geno.
• min.iter must be a single finite number of at least 1; NA or Inf used to run forever.
• A score that is not finite (for example a test set of one individual) stops the search with an error instead of returning NaN. A test set needs at least 2 individuals.
• n.train must be a whole number, and cand must not contain duplicates.
• With sub-populations and a test set, a new training individual is never one that is already in the training set (v2.4.2 could duplicate a member; TSDFGS.legacy_float keeps that draw). A sub-population with too few candidates gives a clear error.
• Parallel searches share one Gram matrix instead of copying it for each search.
• Scores are returned in double precision.
• Removed compiled files (*.o, *.so) from the repository.

New

• optTrain(seed = ); the result also has n.iter and converged.
• Plots of SSDFGS() use the Okabe-Ito colors; ggplot2 is optional (without it the figures are NULL and the numbers are still returned).
• Dependencies: dplyr, ggplot2, and latex2exp are no longer imported (dplyr was never used). Imports are now Rcpp and stats; RcppEigen is link-time only.
• Tests (tests/testthat) with regression tests against v2.4.2 output. Only the r-score, PEV, and CD (the criteria of v2.4.2) are tested carefully; the 15 added criteria are provided but not carefully tested.
• env/: Apptainer recipe (TSDFGS.def) and pinned conda list for building and testing.
• License: GPL (>= 3), unchanged from v2.4.2.
• environment.yml and env/Dockerfile: the tested software versions as a conda environment and as a Docker image (GitHub Container Registry, built by .github/workflows/docker.yaml).
• New website (pkgdown) with a getting-started guide and articles on criteria, methods, and migration.