This collection of gene representation-independent functions 
   implements the population layer of extended evolutionary and genetic 
   algorithms and its support. The population layer consists of functions 
   for initializing, logging, observing, evaluating a population of genes, 
   as well as of computing the next population. For parallel evaluation of a 
   population of genes 4 execution models - named Sequential, MultiCore, 
   FutureApply, and Cluster - are provided. They are implemented by 
   configuring the lapply() function. The execution model FutureApply can be 
   externally configured as recommended by Bengtsson (2021) 
   <doi:10.32614/RJ-2021-048>. Configurable acceptance rules and cooling 
   schedules (see Kirkpatrick, S., Gelatt, C. D. J, and Vecchi, M. P. (1983) 
   <doi:10.1126/science.220.4598.671>, and Aarts, E., and Korst, J. 
   (1989, ISBN:0-471-92146-7) offer simulated annealing or greedy randomized 
   approximate search procedure elements. Adaptive crossover and mutation 
   rates depending on population statistics generalize the approach of 
   Stanhope, S. A. and Daida, J. M. (1996, ISBN:0-18-201-031-7). 
   For xega's architecture, 
   see Geyer-Schulz, A. (2025) <doi:10.5445/IR/1000187255>.

Datasets and definitions of generic functions used in dependencies of the 'xergm' package.

Read and write XES Files to create event log objects used by the 'bupaR' framework. XES (Extensible Event Stream) is the `IEEE` standard for storing and sharing event data (see <http://standards.ieee.org/findstds/standard/1849-2016.html> for more info).

Miscellaneous functions commonly used in other packages maintained by 'Yihui Xie'.

This tool enables in-database scoring of 'XGBoost' models built in R, by translating trained model objects into SQL query. 
  'XGBoost' <https://github.com/dmlc/xgboost> provides parallel tree boosting (also known as gradient boosting machine, or GBM) algorithms
  in a highly efficient, flexible and portable way. GBM algorithm is introduced by Friedman (2001) <doi:10.1214/aos/1013203451>, 
  and more details on 'XGBoost' can be found in Chen & Guestrin (2016) <doi:10.1145/2939672.2939785>.