Version 1.4.1 (Aug-23-2024)
- Some warnings were fixed in the documentation as required by
CRAN.
- Fixed bugs in functions ‘fitBLUP’, ‘SGP’, ‘getGenCov’: an error was
produced if ‘y’ has 2 dimensions but it is a ‘data.frame’. This was
fixed by using ‘as.matrix(y)’
- Fixed bug in ‘multitrait.plot’ function
- Fixed bug in checkpoint in function ‘fitBLUP’: an error was produced
whenever Z = NULL & K = NULL & ntraits > 1. This error is not
produced if an EVD is provided
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’