| 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 |
| swisserrors | 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 |
| swissstrata | 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. |