We are currently working on the following:
- a SIMALTO simulator; we are in an early exploratory phase – we are trying to define what features this simulator should have. If you would like to contribute with your idea, please get in touch with us firstname.lastname@example.org
Recent development work:
- R-sw Discriminant now includes a Genetic Algorithm to identify the combination(s) of variables able to most accurately predict cluster membership. This Genetic algorithm is very powerful, fast and more efficient versus traditional approaches, such as the one implemented in the function DA.report of R-sw Discriminant. The new function (genetic.DA) is available from R-sw 2019r2.
What are Genetic Algorithms? Genetic Algorithms are commonly used to generate good-quality solutions to difficult optimization and search problems by relying on genetics-inspired operators such as selection, crossover and mutation. A Genetic Algorithm repeatedly modifies a population of individual solutions; at each step, the algorithm selects individuals from the current population to be parents and uses them to produce the offsprings, subject to potential mutations, for the next generation. Over successive generations, the population evolves toward better solutions.
- In 2018 we have decided to re-write most of the functions available for R-sw Conjoint, in particular those for Rank-Based Conjoint and Allocation-Based Conjoint. The new functions are much simpler to use than previous ones, and allow running any utility estimation in a few minutes (literally 2 minutes to prepare the script). The new functions are available from R-sw 2019r1.
[last update: February 2020]