Aux_info_data           Auxiliary data
Case_data               Case data
Covariants_data         Covariants data
Covid19Wastewater       Covid19Wastewater: A package for running
                        Covid19 wastewater concentration analysis
DF_date_vector          DF_date_vector
Data_Description        Data Description This package contains a lot of
                        data with many column names, here is a list of
                        them all:
                        [https://github.com/UW-Madison-DSI/Covid19Wastewater/blob/main/docs/data/data_columns_discription.md](https://github.com/UW-Madison-DSI/Covid19Wastewater/blob/main/docs/data/data_columns_discription.md)
Example_data            Example data
Flag_From_Trend         Flag values as outliers based on error from
                        estimated trend This function can be done
                        within group if the data fed into it was
                        grouped
HFGCase_data            High frequency case data
HFGWaste_data           High frequency Waste data
InterceptorCase_data    Madison interceptor case data
OOB_MSE_num_trees       get OOB MSE vs number of forest in trees
OffsetDFMaker           Returns a dataframe with the multiple ways to
                        analyze how offset the Wastewater is from cases
                        data
OffsetDF_Plot           Given output from OffsetDFMaker returns a 2x3
                        grid of all the plots with highlighted values
OffsetHeatmap           Outputs a heatmap of the offset for variant /
                        time windows and population size / region
Pop_data                Sewer shed population data
VariantPlot             Shows each variant in proportion to the others
                        in 2 week time periods
WasteWater_data         Wastewater data set
bagging                 Bootstrap aggregating of dataset gen a list of
                        dataframes using row resampling and column
                        downsizing
buildCaseAnalysisDF     Prep case data into right format
buildRegressionEstimateTable
                        Run DHS analysis at a top level
buildWasteAnalysisDF    Convert wastewater_data data to workset4 shape
classifyCaseRegression
                        Create Case Flags based on regression slope
classifyQuantileFlagRegression
                        Classify FlagRegression with rolling Quantile
                        info
classifyRegressionAnalysis
                        classifyRegressionAnalysis
computeJumps            compute first difference Jumps for N1 and N2
computeRankQuantiles    computeRankQuantiles
countFlags              Create counts of flag data
createCaseFlag          Create Case flags
date_distance_calc      date_distance_calc
date_distance_clamp     remove distances above threshold
date_distance_remove    remove distances above threshold
expSmoothMod            expSmoothMod Add a column of the smoothed
                        values using exponential smoothing
expand_formula          Expand formula for increased info takes a
                        formula with shape A ~ B | C and convert . to
                        its real representation
factorVecByVec          Get ordering for ploting based on factoring
                        vector
flagOutliers            Create column with Boolean based on a threshold
gen_INCMSE              get increased mean square error for each column
gen_OOB_pred            get OOB predictions of the training dataset
                        returns the predictions of each row of the
                        input data using only trees not trained on the
                        row
heatmapcorfunc          Outputs a heatmap where the color is the r
                        squared of wastewater data and center day + x
                        many future days and y many past days Helps
                        inform Offset Analysis
loessSmoothMod          loessSmoothMod Add a column of the smoothed
                        values using Loess
makeQuantileColumns     Add many combo of rolling quantile columns to
                        dataframe have info for each quant window combo
predict,random_linear_forest-method
                        predict new data from random_linear_forest
                        models
random_linear_forest    Fitting linear random forest
random_linear_forest-class
                        random_linear_forest model class using a random
                        forest of linear forest models
rankJumps               rankJumps
removeOutliers          Add column with NA values where the data was
                        flagged
sgolaySmoothMod         sgolaySmoothMod Add a column of the smoothed
                        values using sgolayfilt
show,random_linear_forest-method
                        display form for random_linear_forest class
summary,random_linear_forest-method
                        summary method for linear forest class
