Distributions for ALM

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1. Normal distribution,
2. F-distribution,
3. Weird one with the ratio of folded normal distributions,

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4. Non-central Chi-Squared distribution

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5. Log-normal distribution

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(1) and (5) are done.

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(2) is too difficult to estimate. So probably leave it for a while...

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(2) is not doable, because it's not possible to parametrise it using mean and sd. So tough luck...

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Logit and probit are now implemented in alm() as well

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Just for fun:
6. Log Laplace,
7. Log Logistic,
8. Log S,
9. Asymmetric Laplace,
10. Student,

And even more:
11. Poisson,
12. Negative binomial,

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(11) and (12) are done in 2e1a8d8

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(4) is done in db86e01

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(9) is done in 1d22422

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

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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 to construct two regressions: for a and for b - and then estimate the parameters via the maximisation of the likelihood.

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

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

[config-i1]

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

Also makes sense to think about:
15. Inverse Gaussian
16. Gamma distribution,
17. Erlang distribution,
18. Exponential distribution.

[config-i1]

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.

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

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(14) is done in 0.6.1.41008

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

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(19) is done in cedbd5b

[Steviey]

R 4.x, greybox 0.6.4
Hi!
is there already any gamma, weibull dist. available in greybox::alm(), or something similar?

[config-i1]

Not yet. The closest thing to that is Inverse Gaussian.
The full list of supported distributions is provided here: https://cran.r-project.org/web/packages/greybox/vignettes/alm.html

If you need them, I'll add them to the to do list.

[Steviey]

Oh, that would be wonderful Ivan, Thank you so much!

[config-i1]

Gamma distribution is now available in 16eb934 Please, note that the implemented model is similar to the one discussed for the Inverse Gaussian: https://cran.r-project.org/web/packages/greybox/vignettes/alm.html#invgauss - it might not be the classical Gamma you expect. The vignette has been updated to explain the details.
Weibull is more challenging, so it won't be implemented any time soon. Sorry.

[Steviey]

Thank you Ivan, very nice! Will be dgamma available in other models too (sometimes)?