## All functions

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 geo-referenced 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 k-means clustering function in the 'stats' package.