Ivan Svetunkov

13. Beta distribution with potential restrictions on a or b. This might be useful for the rmc() with Beta for pAIC weights.

> 13. Beta distribution with potential restrictions on a or b. This might be useful for the rmc() with Beta for pAIC weights. The more reasonable thing to do is...

Beta is done, but not yet sure how to use it in rmc...

14. Generalised normal distribution: https://en.wikipedia.org/wiki/Generalized_normal_distribution 15. Box-Cox / power normal distribution (as a more general than lognormal)

Summarising the progress so far, the following are not yet implemented, but could be potentially useful: 6. Log Laplace, 7. Log Logistic, 8. Log S, 14. Generalised normal distribution: https://en.wikipedia.org/wiki/Generalized_normal_distribution...

Stuff left since 27th October 2019: 7. Log Logistic, 14. Generalised normal distribution: https://en.wikipedia.org/wiki/Generalized_normal_distribution 16. Gamma distribution, 17. Erlang distribution, 18. Exponential distribution.

14. Do specifically Exponential power distribution, which is more general than Normal, Laplace and S. Estimate shape parameter using ML.

(14) is done in 0.6.1.41008

19. Logit-normal distribution: logit(x)~N(mu, sigma^2)

(19) is done in cedbd5b1f25ddf3942c74371da3caa8ad430967a