We recommend installing the stable, peer-reviewed version of TruncExpFam, available on CRAN:

`install.packages("TruncExpFam")`

After successful installation, the package can be loaded with

```
library(TruncExpFam)
#> Welcome to TruncExpFam 1.1.1.
#> Please read the documentation on ?TruncExpFam to learn more about the package.
```

TruncExpFam comes equipped with functions to generate random samples
from no less than 12 different probability distributions from the
truncated exponential family. You can read more about them by running
`?rtrunc`

on your R console.

As an example, we will sample 100 values from a chi-square distribution with 14 degrees of freedom:

`<- rtrunc(100, family = "chisq", df = 14) x `

By default, however, `rtrunc()`

doesn’t generate a
truncated distribution. As a matter of fact, the code above will
generate the exact same sample as if were drawn from
`stats::rchisq()`

, watch:

```
set.seed(3067)
<- rtrunc(20, "chisq", df = 14)
x2
set.seed(3067)
<- rchisq(20, 14)
x3
identical(x2, x3)
#> [1] FALSE
```

Oh, wait… Those objects are supposed to be identical! What happened? Let’s investigate:

```
x2#> [1] 16.531982 10.021074 12.480308 16.165519 11.083118 32.684427 16.661472
#> [8] 18.085124 10.921481 11.150269 10.673091 12.012880 7.986689 7.500130
#> [15] 10.951995 6.725427 10.789780 5.616512 20.081876 8.138363
x3#> [1] 16.531982 10.021074 12.480308 16.165519 11.083118 32.684427 16.661472
#> [8] 18.085124 10.921481 11.150269 10.673091 12.012880 7.986689 7.500130
#> [15] 10.951995 6.725427 10.789780 5.616512 20.081876 8.138363
str(x2)
#> 'trunc_chisq' num [1:20] 16.5 10 12.5 16.2 11.1 ...
#> - attr(*, "parameters")=List of 1
#> ..$ df: num 14
#> - attr(*, "truncation_limits")=List of 2
#> ..$ a: num 0
#> ..$ b: num Inf
#> - attr(*, "continuous")= logi TRUE
str(x3)
#> num [1:20] 16.5 10 12.5 16.2 11.1 ...
class(x2)
#> [1] "trunc_chisq"
class(x3)
#> [1] "numeric"
```

OK, so you can tell that the generated numbers are the same, but
`x2`

and `x3`

are not literally the same objects
because the former has a different class. These `trunc_*`

classes are actually very special, because they contain some extra
information about the distribution that a simple vector does not. One
can access such information using
`print(x2, details = TRUE)`

:

```
print(x2, details = TRUE)
#> [1] 16.531982 10.021074 12.480308 16.165519 11.083118 32.684427 16.661472
#> [8] 18.085124 10.921481 11.150269 10.673091 12.012880 7.986689 7.500130
#> [15] 10.951995 6.725427 10.789780 5.616512 20.081876 8.138363
#> attr(,"class")
#> [1] "trunc_chisq"
#> attr(,"parameters")
#> attr(,"parameters")$df
#> [1] 14
#>
#> attr(,"truncation_limits")
#> attr(,"truncation_limits")$a
#> [1] 0
#>
#> attr(,"truncation_limits")$b
#> [1] Inf
#>
#> attr(,"continuous")
#> [1] TRUE
```

Just to be sure that the sample itself matches:

```
identical(as.vector(x2), x3)
#> [1] TRUE
```

Speaking of alternative ways to generate the same sample, for the
sake of convenience and of users familiar with the sampling functions
from the stats package, the wrapper function `rtruncchisq()`

is also available. The results, as you can see below, are identical:

```
set.seed(2912)
<- rtrunc(1e4, "chisq", df = 14)
x4
set.seed(2912)
<- rtruncchisq(1e4, df = 14)
x5
identical(x4, x5)
#> [1] TRUE
```

So far, all samples generated are actually not truncated. This is
because, by default, the truncation limits `a`

and
`b`

are set to the limits of the distribution support, which
are 0 and Inf for the chi-squared distribution.

Let us use a simpler distribution for this second example by sampling from Poisson(10):

```
<- rtruncpois(1e4, 10)
y1 summary(y1)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 0.00 8.00 10.00 10.01 12.00 26.00
var(y1)
#> [1] 10.04213
```

As expected, the values are all larger than 0 and the mean and variance are 10. If we wanted to generate instead from a Poisson(10) truncated at, say, 8 and 20, we would run:

```
<- rtruncpois(1e4, 10, a = 9, b = 20)
y2 summary(y2)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 9.00 10.00 11.00 11.63 13.00 20.00
var(y2)
#> [1] 5.159541
```

Notice how, even with a large sample, the observed mean and variance are still quite far from 10.

One reliable method of estimating the original lambda used to
generate `y2`

is by running the
`mlEstimationTruncDist()`

function:

```
<- mlEstimationTruncDist(y2, print.iter = TRUE)
lambda #> Estimating parameters for the poisson distribution
#> it delta.L2 parameter(s)
#> 1 0.0007325 11.626
#> 2 0.0005043 11.315
#> 3 0.0003465 11.064
#> 4 0.0002378 10.86
#> 5 0.0001631 10.694
#> 6 0.0001119 10.558
#> 7 7.674e-05 10.447
#> 8 5.264e-05 10.356
#> 9 3.611e-05 10.281
#> 10 2.478e-05 10.219
#> 11 1.7e-05 10.169
#> 12 1.167e-05 10.127
#> 13 8.007e-06 10.092
lambda#> lambda
#> 10.06366
```

More information about that function and how you can tweak it is
available on `?mlEstimationTruncDist`

.