v1.1.0 Release to CRAN 2024 November 4

NEW

• added c++ (and R) code to use the Takahashi et al. algorithm for obtaining the partial inverse of the coefficient matrix

  • This switch means almost a 20-fold speedup (for c++ routines) when using analytical first derivatives (i.e., need to calculate "trace" terms)
  • Speedup comes from only calculating elements of inverse matrix (C-inverse) following the non-zero pattern determined for the Cholesky decomposition of the C matrix.
  • follows the SuiteSparse Matlab_Tools sparseinv by Tim Davis, but sparseinv works on LDL' factorization of C whereas I changed this to work on LL' factorization of C.
    • Takahashi, Fagan, & Chin. 1973. Formation of a sparse bus impedance matrix and its application to short circuit study. 8th PICA Conference Proceedings, Minneapolis, MN.
  • gremlinR() now uses far less RAM per iteration of the model (previously was forming the entire C-inverse)

• add finite difference algorithm to obtain first derivatives of likelihood function

  • introduced a parameter (h) inside gremlinControl() to set the "difference" or amount to alter parameters to calculate change in log-likelihood.

• created a REML function inside the c++ code (reml) to calculate log-likelihood

  • moved log-likelihood calculation out of main program and reduced amount of repeated code.
  • also facilitated finite difference functions (which are just repeated log-likelihood evaluations)

Minor Changes

• Removed error() in c++

  • now issues with matrix singularities etc. do not stop code without returning model so far
  • should now be possible to use update() to get "through" trouble spots
  • also allows for user interruptions to c++ code from terminal

• Changed default parameterization so lambda transformation is no longer the default

• Changed default convergence check criteria (cctol)

  • Models using previous values tended to only improve precision of estimates well beyond what was meaningful.

CRAN submission - first with c++ code

1.0.1

NEW

• deltaSE() function to calculate approximate standard errors for functions of (co)variance parameters (e.g., h², standard deviations of variances, or correlations)

  • this can take a formula for the function or a character expression
  • also allows for a list of formulas or character expressions
    e.g., calculate all variance components as proportions of total variance

• Introduce Gcon and Rcon arguments to gremlin() for constraining parameters

  • enables parameters to be fixed or otherwise constrained
  • works in conjunction with the Gstart and Rstart arguments
  • For example in a simple sire model, we could restrain the sire variance =0.38.

grSf <- gremlin(WWG11 ~ sex,
	random= ~ sire,
	data = Mrode11,
	Gstart = list(matrix(0.38)),
	Gcon = list("F"),
	control = gremlinControl(lambda = FALSE))

• Similar to above change (Gcon/Rcon), introduced steps to deal with parameters outside of the boundaries of their parameter space (e.g., variance < 0).

  • restrain these parameters to near their boundaries (after trying step-reduction calculation)
  • re-calculate Average Information, conditional on restrained parameters
    • See Gilmour. 2019. J. Anim. Breed. Genet. for specifics

• change version numbering to just 3 numbers (instead of 4)

  • just dropping last number

Minor Changes

• create new c++ function to handle quasi Newton-Rhapson algorithm
  • allows secondary checks of appropriateness/naughtiness for proposed parameters based on a conditional AI algorithm (conditional on parameters restrained to boundary condition)

1.0.0.1

NEW

• update() function

  • can now continue a model where it left off or change the structure (e.g., drop a single variance component for likelihood ratio test)

• Implement "step-halving" algorithm for AI updates

  • restricts parameter updates if AI algorithm proposes a change of >80% of original parameter value
  • amount by which a parameter change is restricted can be set in gremlinControl() using the step argument

Minor Changes

• Implement more efficient algorithms in the c++ code, that were developed in the R code for version 1.0.0.0.
• Add gremlinControl() function for advanced changes to the way gremlin runs
• Begin major improvements to speed of gradient calculation function
  • changes to be incorporated in em, ai, and elsewhere (where relevant) in next version
  • implements calculations that take advantage of sparsity (i.e., don't calculate values where there are zeroes)

1.0.0.0

NEW

• Completely revised way models are built and called

  • made a "modular" series of functions for setting up the model and optimizing the REML likelihood
  • new grMod and gremlinR classes.
    • grMod is the model structure for which a log-likelihood can be calculated
    • gremlinR class distinguishes from gremlin class in that gremlinR objects will only use R code written by the package in order to run the model. Class gremlin will execute underlying c++ code written in the package.

• Average Information algorithm has been vastly improved

  • ai() efficiently calculates the AI matrix without directly computing several matrix inverses (as previously coded)

• lambda and alternative parameterizations now possible and executed by the same code

  • lambda parameterization is the REML likelihood of the variance ratios after factoring out a residual variance from the Mixed Model Equations.
  • the alternative does not have a special name, this is just a model of all (co)variance parameters as (co)variance parameters (as opposed to ratios, as in the lambda models).
  • instead of completely separate functions for these two parameterizations, there is an argument that runs alternative lines of code, wherever the calculations differ for these two different parameterizations

Minor Changes

• No long construct Mixed Model Array (M) matrix from which the Cholesky factorization (and logDetC and tyPy calculations are made)
  • Changed to directly construct coefficient matrix of mixed model equations (C) and obtain tyPy and logDetC using this
  • Previously had to store Cholesky factorizations of both M and C, now do a solve with Cholesky of C (sLc/Lc in R/c++ code) to calculate tyPy based off Boldman and Van Vleck

0.1.0.0

NEW

• methods for gremlin objects
  • notably, AIC, residuals, anova, and nobs
  • updated the summary, print, and logLik methods as well

0.0.2.0

Improved algorithm that reduces computational resources and time! Also implemented c++ code in gremlin(), while keeping gremlinR() purely the R implementation (at least from the package writing standpoint).

CRAN release v0.1.0.1

Quick fix for issue arising when R's code changed with the new version of R

First CRAN release

0.1.0.0

Initial release using purely R functionality.

NEW

• methods for gremlin objects
  • notably, AIC, residuals, anova, and nobs
  • updated the summary, print, and logLik methods as well

0.0.0.1 April 2017 gremlin is born!

Congratulations, its a gremlin!