Density distributions of lognormal distributions (lines) get closer to normal density shaded area) as multiplicative standard deviation \(\sigma^*\) decreases down to 1.2 for same \(\mu^* = 1\).

`getLognormMode(mu = 0.6,sigma = 0.5)`

`## [1] 1.419068`

`getLognormMedian(mu = 0.6,sigma = 0.5)`

`## [1] 1.822119`

`(theta <- getLognormMoments(mu = 0.6,sigma = 0.5))`

```
## mean var cv
## [1,] 2.064731 1.210833 0.5329404
```

Mode < Median < Mean for the right-skewed distribution.

The return type of `getLognormMoments`

is a matrix.

```
moments <- cbind(mean = c(1,1), var = c(0.2, 0.3)^2 )
(theta <- getParmsLognormForMoments( moments[,1], moments[,2]))
```

```
## mu sigma
## [1,] -0.01961036 0.1980422
## [2,] -0.04308885 0.2935604
```

The larger the spread, the more skewed is the distribution, here both with an expected value of one.