CppLHS

matrix of data - must be numeric (factors are converted to numeric in R)

cost

strata

include

integer vector of rows from which sampling is allowed

factors

i_fact

nsample

cost_mode

iter

wCont

wFact

wCorr

eta matrix - either all 1, or user input

etaMat

temperature

temperature decrease every length_cycle iterations

tdecrease

number of iterations between temperature decrease

length_cycle

This is the internal Cpp function used to run the metropolis hasting algorithm if use.cpp = T. 
In general, it shouldn't be used as a stand alone function, because some preprocessing is done in R

Conditioned Latin hypercube sampling, as published by Minasny and McBratney (2006) <DOI:10.1016/j.cageo.2005.12.009>. This method proposes to stratify sampling in presence of ancillary data. An extension of this method, which propose to associate a cost to each individual and take it into account during the optimisation process, is also proposed (Roudier et al., 2012, <DOI:10.1201/b12728>).

Pierre Roudier

clhs

Conditioned Latin Hypercube Sampling

Colby Brugnard

Dylan Beaudette

Benjamin Louis

Kiri Daust

David Clifford

CppLHS function

This is the internal Cpp function used to run the metropolis hasting algorithm if use.cpp = T. 
In general, it shouldn't be used as a stand alone function, because some preprocessing is done in R — CppLHS

This is the internal Cpp function used to run the metropolis hasting algorithm if use.cpp = T. 
In general, it shouldn't be used as a stand alone function, because some preprocessing is done in R

CppLHS: This is the internal Cpp function used to run the metropolis hasting algorithm if use.cpp = T. In general, it shouldn't be used as a stand alone function, because some preprocessing is done in R

Description

Arguments

Value