The triangulr
package provides high-performance triangular
distribution functions which includes density function, distribution
function, quantile function, random variate generator, moment generating
function, characteristic function, and expected shortfall function for
the triangular distribution.
You can install the released version of triangulr
from
CRAN with:
install.packages("triangulr")
And the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("irkaal/triangulr")
These are basic examples of using the included functions:
library(triangulr)
Using the density function,
dtri()
.
x <- c(0.1, 0.5, 0.9)
dtri(x,
min = 0,
max = 1,
mode = 0.5)
#> [1] 0.4 2.0 0.4
dtri(x,
min = c(0, 0, 0),
max = 1,
mode = 0.5)
#> [1] 0.4 2.0 0.4
Using the distribution function,
ptri()
.
q <- c(0.1, 0.5, 0.9)
1 - ptri(q, lower_tail = FALSE)
#> [1] 0.02 0.50 0.98
ptri(q, lower_tail = TRUE)
#> [1] 0.02 0.50 0.98
ptri(q, log_p = TRUE)
#> [1] -3.91202301 -0.69314718 -0.02020271
log(ptri(q, log_p = FALSE))
#> [1] -3.91202301 -0.69314718 -0.02020271
Using the quantile function,
qtri()
.
p <- c(0.1, 0.5, 0.9)
qtri(1 - p, lower_tail = FALSE)
#> [1] 0.2236068 0.5000000 0.7763932
qtri(p, lower_tail = TRUE)
#> [1] 0.2236068 0.5000000 0.7763932
qtri(log(p), log_p = TRUE)
#> [1] 0.2236068 0.5000000 0.7763932
qtri(p, log_p = FALSE)
#> [1] 0.2236068 0.5000000 0.7763932
Using the random variate generator,
rtri()
.
n <- 3
set.seed(1)
rtri(n,
min = 0,
max = 1,
mode = 0.5)
#> [1] 0.3643547 0.4313490 0.5378601
set.seed(1)
rtri(n,
min = c(0, 0, 0),
max = 1,
mode = 0.5)
#> [1] 0.3643547 0.4313490 0.5378601
Using the moment generating function,
mgtri()
.
t <- c(1, 2, 3)
mgtri(t,
min = 0,
max = 1,
mode = 0.5)
#> [1] 1.683357 2.952492 5.387626
mgtri(t,
min = c(0, 0, 0),
max = 1,
mode = 0.5)
#> [1] 1.683357 2.952492 5.387626
Using the expected shortfall function,
estri()
.
p <- c(0.1, 0.5, 0.9)
estri(p,
min = 0,
max = 1,
mode = 0.5)
#> [1] 0.1490712 0.3333333 0.4610079
estri(p,
min = c(0, 0, 0),
max = 1,
mode = 0.5)
#> [1] 0.1490712 0.3333333 0.4610079
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