adjustSize()

Adjustment of the sample size in case it is externally given 
aggrStrata2()

Builds the "strata" dataframe containing information on target variables Y's
distributions in the different strata, starting from a frame 
aggrStrataSpatial()

Builds the "strata" dataframe containing information on target variables Y's
distributions in the different strata, starting from a frame where units are spatially correlated. 
assignStrataLabel()

Function to assign the optimized strata labels 
bethel()

Multivariate optimal allocation 
buildFrameDF()

Builds the "sampling frame" dataframe from a dataset containing information on
all the units in the population of reference 
buildFrameSpatial()

Builds the "sampling frame" dataframe from a dataset containing information all the units in the population of reference including spatial 
buildStrataDF()

Builds the "strata" dataframe containing information on target variables Y's
distributions in the different strata, starting from sample data or from a frame 
buildStrataDFSpatial()

Builds the "strata" dataframe containing information on target variables Y's
distributions in the different strata, starting from sample data or from a frame 
checkInput()

Checks the inputs to the package: dataframes "errors", "strata" and "sampling frame" 
computeGamma()

Function that allows to calculate a heteroscedasticity index,
together with associate prediction variance,
to be used by the optimization step
to correctly evaluate the standard deviation in the strata
due to prediction errors. 
errors

Precision constraints (maximum CVs) as input for Bethel allocation 
evalSolution()

Evaluation of the solution produced by the function 'optimizeStrata' by
selecting a number of samples from the frame with the optimal stratification, and calculating average CV's on the target variables Y's. 
expected_CV()

Expected coefficients of variation of target variables Y 
KmeansSolution()

Initial solution obtained by applying kmeans clustering of atomic strata 
KmeansSolution2()

Initial solution obtained by applying kmeans clustering of frame units 
KmeansSolutionSpatial()

Initial solution obtained by applying kmeans clustering of frame units 
nations

Dataset 'nations' 
optimizeStrata()

Best stratification of a sampling frame for multipurpose surveys 
optimizeStrata2()

Best stratification of a sampling frame for multipurpose surveys (only with continuous stratification variables) 
optimizeStrataSpatial()

Best stratification of a sampling frame for multipurpose surveys considering also spatial correlation 
optimStrata()

Optimization of the stratification of a sampling frame given a sample survey 
plotSamprate()

Plotting sampling rates in the different strata for each domain in the solution. 
plotStrata2d()

Plot bivariate distibutions in strata 
prepareSuggestion()

Prepare suggestions for optimization with method = "continuous" or "spatial" 
procBethel()

Procedure to apply Bethel algorithm and select a sample from given strata 
selectSample()

Selection of a stratified sample from the frame with srswor method 
selectSampleSpatial()

Selection of georeferenced points from the frame 
selectSampleSystematic()

Selection of a stratified sample from the frame with systematic method 
strata

Dataframe containing information on strata in the frame 
summaryStrata()

Information on strata structure 
errors

Precision constraints (maximum CVs) as input for Bethel allocation 
swissframe

Dataframe containing information on all units in the population of reference
that can be considered as the final sampling unit
(this example is related to Swiss municipalities) 
swissmunicipalities

The Swiss municipalities population 
swissframe

Dataframe containing information on strata in the swiss municipalities frame 
tuneParameters()

Execution and compared evaluation of optimization runs 
updateFrame()

Updates the initial frame on the basis of the optimized stratification 
updateStrata()

Assigns new labels to atomic strata on the basis of the optimized aggregated strata 
var.bin()

Allows to transform a continuous variable into a categorical ordinal one
by applying a modified version of the kmeans clustering function in the 'stats' package. 