Available for research (free with restriction: # genotyped animals = 25,000)

• BLUPF90+ - a combined program of blupf90, remlf90, and airemlf90
• GIBBSF90+ - a combined program of gibbs1f90, gibbs2f90, gibbs3f90, thrgibbs1f90, and thrgibbs3f90
• POSTGIBBSF90 - statistics and graphics for post-Gibbs analysis (S. Tsuruta)
• RENUMF90 - a renumbering program that also can check pedigrees and assign unknown parent groups; supports large data sets
• PREGSF90 – genomic preprocessor that combines genomic and pedigree relationships (I. Aguilar)
• POSTGSF90 – genomic postprocessor that extracts SNP solutions after genomic evaluations (single step, GBLUP) (I. Aguilar)
• PREDICTF90 - a program to calculate adjusted y, y_hat, and residuals (I. Aguilar)
• PREDF90 - a program to predict direct genomic value (DGV) for animals based on genotypes and SNP solution
• QCF90 - a quality-control tool on genotypes and pedigree information (Y. Masuda)
• INBUPGF90 - a program to calculate inbreeding coefficients with incomplete pedigree (I. Aguilar)
• SEEKPARENTF90 - a program to verify paternity and parent discovery using SNP markers (I. Aguilar)
• VALIDATIONF90 - a program to perform validation of predictions (coming soon!)
• RRMEBVF90 - postprocessor for random regression models

Available by request

• QXPAK - joint analysis of QTL and polygenic effects (M. Perez-Enciso) QxPak web page
• MRF90 - Method R program suitable for a large data set; contact T. Druet.
• COXF90 – Bayesian Cox model - contact J. P. Sanchez (JuanPablo.Sanchez@irta.cat)
• BLUPF90HYP – BLUPF90 with hypothesis testing (F and Chi2 tests) - contact J. P. Sanchez as above

Available only under research agreement

• BLUP90IOD2 - BLUP by iteration on data with PCG for very large models (S. Tsuruta)
• BLUP90IOD3 (new BLUP90IOD) - a combined program of BLUP90IOD2, BLUP90IOD2RR, BLUP90IOD2HR, and BLUP90MBE2 with new features
• CBLUP90IOD2 - BLUP by iteration on data for threshold-linear models (working for one threshold and several linear traits)
• CBLUP90MBE - BLUP by iteration on data for threshold-linear models (working for one threshold and several linear traits) with support for very large models for multi-breed evaluations
• ACCF90 - approximation of accuracies for breeding values
• ACCF90GS - approximation of accuracies for genomic breeding values based on diagonals of G
• ACCF90GS2 - new approximation of accuracies for genomic breeding values based on block sparse inversion
• BLUP90ADJ - BLUP data preadjustment tool
• DEPROOFSF90 - computation of deregressed proof (DRP)
• DYDF90 - computation of daughter yield deviation (DYD) known as progeny trait deviation

No longer updated and obsolete (as of May 2022)

• BLUPF90 - BLUP in memory
• REMLF90 - accelerated EM REML
• AIREMLF90 - Average Information REML with several options including EM-REML and heterogeneous residual variances (S. Tsuruta)
• GIBBSF90 - simple block implementation of Gibbs sampling - no genomic
• GIBBS1F90 - as above but faster for creating mixed model equations only once
• GIBBS2F90 - as above but with joint sampling of correlated effects
• GIBBS3F90 - as above with support for heterogeneous residual variances
• THRGIBBSF90 - Gibbs sampling for any combination of categorical and linear traits (D. Lee) - no genomic
• THRGIBBS1F90 - as above but simplified with several options (S. Tsuruta)
• THRGIBBS3F90 - as above with heterogeneous residual variances for linear traits
• CBLUP90 - solutions for bivariate linear-threshold models (works only for 2 trait model: threshold and linear)
• CBLUP90THR - as above but with thresholds computed and many linear traits (B. Auvray)
• CBLUP90REML - as above but with quasi REML (B. Auvray) - no genomic
• GIBBS2CEN - support censored data (J. Arango) - no genomic
• GIBBS2RECUR - support recursive models (J. P. Sanchez) - no genomic
• BLUP90IOD2RR & BLUP90IOD2HR - BLUP90IOD2 with heterogeneous residual variances (like in GIBBS3F90 & AIREMLF90)
• BLUP90MBE - BLUP by iteration on data with support for very large models for multi-breed evaluations (A. Legarra)

Other programming contributions were made by Miguel Perez-Enciso (user_file) and François Guillaume (Jenkins hashing functions).