Provides systematic geometry-adaptive parameter optimization with 
    statistical validation for experimental biological data. Combines ANOVA-based 
    validation with systematic constraint configuration testing (log-scale, 
    positive domain, Euclidean) through T,P,E testing. Only proceeds with 
    parameter optimization when statistically significant biological effects 
    are detected, preventing over-fitting to noise. Uses 'GALAHAD' trust region methods with constraint projection from Conn et al. (2000) 
    <doi:10.1137/S1052623497325107>, ANOVA-based validation following Fisher 
    (1925) <doi:10.1007/978-1-4612-4380-9_6>, and effect size calculations 
    per Cohen (1988, ISBN:0805802835). Designed for structured experimental 
    data including kinetic curves, dose-response studies, and treatment 
    comparisons where appropriate parameter constraints and statistical 
    justification are important for meaningful biological interpretation. 
    Developed at the Minnesota Center for Prion Research and Outreach at 
    the University of Minnesota.

Finds causal connections in precision data, finds lags and embeddings in 
  time series, guides training of neural networks and other smooth models, evaluates 
  their performance, gives a mathematically grounded answer to the over-training 
  problem.  Smooth regression is based on the Gamma test, which measures smoothness
  in a multivariate relationship.  Causal relations are smooth, noise is not.  
  'sr' includes the Gamma test and search techniques that use it. 
  References: Evans & Jones (2002) <doi:10.1098/rspa.2002.1010>, 
  AJ Jones (2004) <doi:10.1007/s10287-003-0006-1>.

Artificial selection through selective breeding is an efficient way to induce changes in traits of interest in experimental populations. This package (sra) provides a set of tools to analyse artificial-selection response datasets. The data typically feature for several generations the average value of a trait in a population, the variance of the trait, the population size and the average value of the parents that were chosen to breed. Sra implements two families of models aiming at describing the dynamics of the genetic architecture of the trait during the selection response. The first family relies on purely descriptive (phenomenological) models, based on an autoregressive framework. The second family provides different mechanistic models, accounting e.g. for inbreeding, mutations, genetic and environmental canalization, or epistasis. The parameters underlying the dynamics of the time series are estimated by maximum likelihood. The sra package thus provides (i) a wrapper for the R functions mle() and optim() aiming at fitting in a convenient way a predetermined set of models, and (ii) some functions to plot and analyze the output of the models.