| Type: | Package |
| Title: | Rcpp-Based Truncated Normal Distribution RNG and Family |
| Version: | 0.2-2 |
| Date: | 2017-11-21 |
| Author: | Jonathan Olmsted [aut, cre] |
| Maintainer: | Jonathan Olmsted <jpolmsted@gmail.com> |
| Description: | R-level and C++-level functionality to generate random deviates from and calculate moments of a Truncated Normal distribution using the algorithm of Robert (1995) <doi:10.1007/BF00143942>. In addition to RNG, functions for calculating moments, densities, and entropies are provided at both levels. |
| URL: | http://github.com/olmjo/RcppTN |
| BugReports: | http://github.com/olmjo/RcppTN/issues |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| Suggests: | testthat |
| LinkingTo: | Rcpp |
| Imports: | Rcpp |
| RoxygenNote: | 6.0.1 |
| NeedsCompilation: | yes |
| Packaged: | 2017-11-21 10:36:54 UTC; olmjo |
| Repository: | CRAN |
| Date/Publication: | 2017-11-21 11:42:30 UTC |
Truncated Normal Distribution Density
Description
Calculate density of Truncated Normal distributions
Usage
dtn(.x = 0, .mean = rep(0, length(.x)), .sd = rep(1, length(.x)),
.low = rep(-Inf, length(.x)), .high = rep(Inf, length(.x)),
.checks = TRUE)
Arguments
.x |
Length K vector of the points at which to evaluate the density |
.mean |
Length K vector with the means of the K Normal distributions *prior* to truncation |
.sd |
Length K vector with the standard deviations of the K Normal distributions *prior* to truncation |
.low |
Length K vector with the lower truncation bound of the K Normal distributions *prior* to truncation |
.high |
Length K vector with the upper truncation bound of the K Normal distributions *prior* to truncation |
.checks |
Logical indicating whether inputs and outputs should be checked and either stop (for bad inputs) or warn (for likely bad outputs) |
Value
Length K vector with the entropies associated with each of the K Truncated Normal distributions
Author(s)
Jonathan Olmsted
Examples
lows <- c(-1, 5, -100, 4, 4, -100, 7)
highs <- c(1, 100, 10, 7, 4.1, 100, 100)
dtn(.x = rep(0, length(lows)),
.mean = rep(0, length(lows)),
.sd = rep(1, length(lows)),
.high = highs
)
Truncated Normal Distribution Entropy
Description
Calculate entropy of Truncated Normal distributions
Usage
enttn(.mean = rep(0, 1), .sd = rep(1, length(.mean)), .low = rep(-Inf,
length(.mean)), .high = rep(Inf, length(.mean)))
Arguments
.mean |
Length K vector with the means of the K Normal distributions prior to truncation |
.sd |
Length K vector with the standard deviations of the K Normal distributions prior to truncation |
.low |
Length K vector with the lower truncation bound of the K Normal distributions prior to truncation |
.high |
Length K vector with the upper truncation bound of the K Normal distributions prior to truncation |
Value
Length K vector with the entropies associated with each of the K Truncated Normal distributions
Author(s)
Jonathan Olmsted
Examples
lows <- c(-1, 5, -100, 4, 4, -100, 7)
highs <- c(1, 100, 10, 7, 4.1, 100, 100)
enttn(.mean = rep(0, length(lows)),
.sd = rep(1, length(lows)),
.low = lows,
.high = highs
)
Truncated Normal Distribution Expectation
Description
Calculate expectation of Truncated Normal distributions
Usage
etn(.mean = rep(0, 1), .sd = rep(1, length(.mean)), .low = rep(-Inf,
length(.mean)), .high = rep(Inf, length(.mean)), .checks = TRUE)
Arguments
.mean |
Length K vector with the means of the K Normal distributions prior to truncation |
.sd |
Length K vector with the standard deviations of the K Normal distributions prior to truncation |
.low |
Length K vector with the lower truncation bound of the K Normal distributions prior to truncation |
.high |
Length K vector with the upper truncation bound of the K Normal distributions prior to truncation |
.checks |
Length 1 logical vector indicating whether to perform checks (safer) or not (faster) on the input parameters |
Details
The special values of -Inf and Inf are valid values in the .low and .high arguments, respectively.
Value
A length K vector of expectations corresponding to the Truncated Normal distributions. NAs are returned (with a warning) for invalid parameter values.
Author(s)
Jonathan Olmsted
Examples
etn() ## 0
etn(0, 1, -Inf, Inf) ## 0
etn(0, 1, -9999, 9999) ## 0
etn(0, 1, 0, Inf) ## 0.798
etn(0, 1, Inf, -Inf) ## NA with warning
etn(c(0, 0),
c(1, 1),
c(-Inf, 5),
c(1, Inf)
) ## multiple expectations
Truncated Normal Distribution RNG
Description
Sample from Truncated Normal distributions
Usage
rtn(.mean = rep(0, 1), .sd = rep(1, length(.mean)), .low = rep(-Inf,
length(.mean)), .high = rep(Inf, length(.mean)), .checks = TRUE)
Arguments
.mean |
Length K vector with the means of the K Normal distributions prior to truncation |
.sd |
Length K vector with the standard deviations of the K Normal distributions prior to truncation |
.low |
Length K vector with the lower truncation bound of the K Normal distributions prior to truncation |
.high |
Length K vector with the upper truncation bound of the K Normal distributions prior to truncation |
.checks |
Length 1 logical vector indicating whether to perform checks (safer) or not (faster) on the input parameters |
Details
The special values of -Inf and Inf are valid values in the .low and .high arguments, respectively. The implementation is from Robert (1995). The computation is written in Rcpp-based C++ code, but respects R's RNG state. The draws from this function are reproducible because it respects R's RNG state. Draws using this algorithm (whether implemented in R code or C++) will be the same if seeded correctly. However, you should not expect these draws to match those from another algorithm.
Value
A length K vector of expectations corresponding to the Truncated Normal distributions. NAs are returned (with a warning) for invalid parameter values.
Author(s)
Jonathan Olmsted
References
Robert, Christian P. “Simulation of truncated normal variables”. Statistics and Computing 5.2 (1995): 121-125. http://dx.doi.org/10.1007/BF00143942
Examples
set.seed(1)
rtn(0, 1, -Inf, Inf) # single draw from a single distribution
## [1] -0.6264538
set.seed(1)
rtn(0, 1, -Inf, Inf) # again, because it respects the RNG state
## [1] -0.6264538
rtn(rep(0, 3),
rep(1, 3),
rep(-Inf, 3),
rep(Inf, 3)
) # multiple draws from a single distribution
## [1] 0.1836433 -0.8356286 1.5952808
rtn(c(0, 0),
c(1, 1),
c(-Inf, 5),
c(1, Inf)
) # multiple draws, each from a different distribution
## [1] 0.3295078 5.3917301
Truncated Normal Distribution Variance
Description
Calculate variance of Truncated Normal distributions
Usage
vtn(.mean = rep(0, 1), .sd = rep(1, length(.mean)), .low = rep(-Inf,
length(.mean)), .high = rep(Inf, length(.mean)), .checks = TRUE)
Arguments
.mean |
Length K vector with the means of the K Normal distributions prior to truncation |
.sd |
Length K vector with the standard deviations of the K Normal distributions prior to truncation |
.low |
Length K vector with the lower truncation bound of the K Normal distributions prior to truncation |
.high |
Length K vector with the upper truncation bound of the K Normal distributions prior to truncation |
.checks |
Length 1 logical vector indicating whether to perform checks (safer) or not (faster) on the input parameters |
Details
The special values of -Inf and Inf are valid values in the .low and .high arguments, respectively.
Value
A length K vector of expectations corresponding to the Truncated Normal distributions. NAs are returned (with a warning) for invalid. parameter values.
Author(s)
Jonathan Olmsted
Examples
vtn() ## 1
vtn(0, 1, -Inf, Inf) ## 1
vtn(0, 1, -9999, 9999) ## 1
vtn(0, 1, 0, Inf) ## 0.36338
vtn(0, 1, Inf, -Inf) ## NA with warning
vtn(c(0, 0),
c(1, 1),
c(-Inf, 5),
c(1, Inf)
) ## multiple variances