logspace
(Package: ramify) :
Logarithmically-spaced Elements
Construct a vector of n logarithmically-spaced elements from 10^a to 10^b .
● Data Source:
CranContrib
● Keywords:
● Alias: logspace
●
0 images
|
tr
(Package: ramify) :
Trace of a Matrix
Sum of diagonal elements of a matrix.
● Data Source:
CranContrib
● Keywords:
● Alias: tr
●
0 images
|
add_dots
(Package: ramify) :
Shorten a Vector
Shorten a vector using ... notation.
● Data Source:
CranContrib
● Keywords: internal
● Alias: add_dots
●
0 images
|
eye
(Package: ramify) :
Identity Matrix
Creates an nrow -by-ncol identity matrix.
● Data Source:
CranContrib
● Keywords:
● Alias: eye
●
0 images
|
hcat
(Package: ramify) :
Concatenate Matrices
Concatenate matrices along the first or second dimension.
● Data Source:
CranContrib
● Keywords:
● Alias: hcat, vcat
●
0 images
|
bmat
(Package: ramify) :
Block Matrices
Construct a block matrix using a character string initializer.
● Data Source:
CranContrib
● Keywords:
● Alias: bmat
●
0 images
|
randi
(Package: ramify) :
Matrix/Array of Uniform Random Integers
Construct a matrix or multi-way array of uniform random integers.
● Data Source:
CranContrib
● Keywords:
● Alias: randi
●
0 images
|
corRExp
(Package: ramps) :
Exponential Spatial Correlation Structure
This function is a constructor for the 'corRExp' class, representing an exponential spatial correlation structure. Letting r denote the range, the correlation between two observations a distance d apart is exp(-d/r).
● Data Source:
CranContrib
● Keywords: models
● Alias: corRExp
●
0 images
|
georamps
(Package: ramps) :
Bayesian Geostatistical Model Fitting with RAMPS
General function for fitting Bayesian geostatistical models using the reparameterized and marginalized posterior sampling (RAMPS) algorithm of Yan et al. (2007).
● Data Source:
CranContrib
● Keywords: models
● Alias: georamps, print.ramps
●
0 images
|
corRSpher
(Package: ramps) :
Spherical Spatial Correlation Structure
This function is a constructor for the 'corRSpher' class, representing a spherical spatial correlation structure. Letting r denote the range, the correlation between two observations a distance d < r apart is 1-1.5(d/r)+0.5(d/r)^3. If d >= r the correlation is zero.
● Data Source:
CranContrib
● Keywords: models
● Alias: corRSpher
●
0 images
|