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.

Manage a collection/library of R source packages. Discover, document, load, test 
  source packages. Enable to use those packages as if they were actually installed. Quickly reload
  only what is needed on source code change. Run tests and checks in parallel.

Connecting to databases requires boilerplate code to specify
    connection parameters and to set up sessions properly with the DBMS.
    This package provides a simple tool to fill two purposes: abstracting
    connection details, including secret credentials, out of your source
    code and managing configuration for frequently-used database connections
    in a persistent and flexible way, while minimizing requirements on the
    runtime environment.

Implementation of the SRCS method for a color-based visualization of the
    results of multiple pairwise tests on a large number of problem configurations, proposed in: 
    I.G. del Amo, D.A. Pelta. SRCS: a technique for comparing multiple algorithms under several
    factors in dynamic optimization problems. In: E. Alba, A. Nakib, P. Siarry
    (Eds.), Metaheuristics for Dynamic Optimization. Series: Studies in
    Computational Intelligence 433, Springer, Berlin/Heidelberg, 2012.