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Version 1.4 (Jun-19-2024)
- Changes in function names: SSI -> SGP, SSI.CV -> SGP.CV.
Results from both functions are of the class “SGP” standing for “sparse
genomic prediction”
- Training and testing sets in function ‘SGP’ can now be defined using
integer vectors as ‘SGP(…,trn, tst)’. In the former version this was
defined as ‘SSI(…, trn_tst)’, where ‘trn_tst’ was be a vector with 0’s
(for tst) and 1’s (for trn)
- In cross-validation, training set is defined as ‘SGP.CV(…,trn)’. In
the former version this was ‘SSI.CV(…,trn_tst)’
- Method ‘fitted’ is replaced by method ‘predict’
- A multi-trait analysis can be performed using the function ‘SGP’
with argument ‘y’ being either a matrix or a vector. In the later case,
different genotypes and traits are specified by arguments ‘ID_geno’ and
‘ID_trait’, respectively
- Likewise, for the ‘getGenCov’, arguments ‘ID_geno’ and ‘ID_trait’
can be also used if argument ‘y’ is a vector
- Likewise, for the within-trait analysis of the function ‘fitBLUP’,
arguments ‘ID_geno’ and ‘ID_trait’ can be also used if argument ‘y’ is a
vector
- Eigenvalues (d) and eigenvectors (U) in functions ‘fitBLUP’ and
‘getGenCov’ can be passed as argument ‘EVD’ being a list as per the
function ‘eigen’
Version 1.3.1 (Nov-17-2023)
- Old dependencies R-packages were removed
- Calls to functions ‘Kronecker’ and ‘Kronecker_cov’ from the
tensorEVD R-package were added
- Functions to work with triangular matrices were removed
Version 1.3.0 (Aug-15-2023)
New features
- Function ‘fitBLUP’ allows the solution of the mixed model for
multiple traits when input ‘y’ has more than one column
- Function ‘getGenCov’ allows the calculation of all pairs of columns
of input ‘y’ so a genetic covariance matrix can be formed
- Function ‘SSI’ is extended to the multi-trait case if input ‘y’ has
more than one column. In this case within-trait genetic/residual
covariances varU and varE are calculated using function ‘getGenCov’ when
are not provided
- Arguments ‘trn’ and ‘tst’ in function ‘SSI(…,trn,tst)’ can be be now
passed as ‘SSI(…,trn_tst)’, where ‘trn_tst’ can be a vector with 0’s
(for tst) and 1’s (for trn)
- New function ‘prune’ added (see manual)
- New functions to work with triangular matrices added (see
manual)
Version 1.2.0 (Aug-16-2022)
New features
- Functions ‘solveEN’ and ‘LARS’ allow solving several regressions by
iterating over columns of argument ‘Gamma’
- Function ‘SSI’ allows either saving or returning the coefficients
through ‘save.beta’ and ‘return.beta’ arguments
- Function ‘SSI’ returns also genetic values ‘u’ of testing
subjects
- Methods ‘summary’, ‘fitted’, and ‘plot’ can be implemented for a
desired response variable ‘y’ different from the specified in object$y,
e.g., fitted(object, y)
Version 1.1.0 (Mar-10-2022)
New features
- Some problems were fixed in the documentation structure as required
by CRAN.
- Functions ‘lars2’, ‘SSI_CV’, ‘plotNet’, ‘plotPath’ changed their
names to ‘LARS’, ‘SSI.CV’, ‘net.plot’, and ‘path.plot’,
respectively.
- Some arguments’ functions changed their names to a more informative
name (e.g., ‘minLambda’ => ‘lambda.min’)
- More functionalities added to ‘net.plot’ function
Version 1.0.1 (Jan-26-2022)
New features
- Functions ‘SSI’ and ‘SSI_CV’ allow providing either ‘theta’
(residual/genetic variances ratio) or the ‘h2’ (heritability)
Bug fixes
- C-based routine associated to the ‘readBinary’ function now uses the
‘Rf_allocMatrix’ method to handle matrices whose length (number of rows
x number of columns) exceed 2^31-1 = 2147483647
Version 1.0.0 (Sep-30-2021)
New features
- Function ‘solveEN’ allows early stop when a user-provided number of
non-zero predictors (at a given value of lambda) is reached (argument
‘maxDF’)
- Functions ‘solveEN’ and ‘lars2’ return object ‘beta’ as matrix with
predictors in rows (rather than in columns)
- Function ‘cov2cor2’ allows multiplying the resulting correlation
matrix times a constant ‘a’ (default is ‘a=1’)
- Provided ‘wheatHTP’ dataset includes now an array of 4-folds
partitions (‘CV’ column in object ‘Y’) and calculations of genetic and
residual covariances between YLD and each of the wavelengths
(‘genCOV_xy’ and ‘resCOV_xy’ objects), and among YLD from each
environment (‘genCOV_yy’ object). Residuals covariances among YLD from
each environment (‘resCOV_yy’ object) is also included
Bug fixes
- Function ‘fitBLUP’ performs the new checking varU <= 2*var(y) to
declare a possible error if FALSE
- Function reshape2::melt is used instead of reshape::melt
Version 0.4.0 (May-12-2021)
New features
- More detailed functions’ documentation
- Function ‘fitBLUP’ performs a quality control for very small or
negatives eigenvalues
- Function ‘saveBinary’ does not save columns’ nor rows’ names
anymore
- Function ‘SSI’ uses now a C-based routine called ‘add2diag’ created
to add a numeric value to the diagonal of a symmetric matrix (single or
double precision). This routine is not at the user level
- Function ‘getGenCov’ has the argument ‘warn’ to whether show
warnings from ‘BLUP’ analyses
Bug fixes
- All C-based routines: a ‘long long’ variable type, instead of an
‘int’ type, was used for indexing arrays (matrices). This change allows
dealing with matrices whose length (number of rows x number of columns)
exceed 2^31-1 = 2147483647 (e.g., a matrix of 46341 x 46341)
Version 0.3.0 (Apr-29-2021)
Features
- First released version
- Function ‘solveMixed’ (from GitHub version) was renamed to
‘fitBLUP’
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.