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          dh.anyData()
- Describe whether variables are completely missing for each cohort
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          dh.buildModels()
- Build Exposure-Outcome Models with Optional Covariates
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          dh.classDiscrepancy()
- Describes the class of one or more variables across cohorts and indicates differences
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          dh.createTableOne()
- Creates tables in useful formats for including in manuscripts
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          dh.findVarsIndex()
- Return return indices of column names in server-side dataframe
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          dh.getAnonPlotData()
- Extracts an anonymised version of serverside data which can be used to create bespoke plots
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          dh.getRmStats()
- Produces descriptive statistics based on repeated measures data which it would be useful to report in papers.
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          dh.getStats()
- Produces a range of descriptive statistics in a useful format
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          dh.lmTab()
- Extracts coefficients and confidence intervals from linear models
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          dh.lmeMultPoly()
- Fit multiple mixed effects models containing different combination of fractional polynomials
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          dh.localProxy()
- Generate a local proxy dataframe to enable local auto-completion in RStudio
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          dh.makeAgePolys()
- Produces multiple transformations of the age term for fractional polynomial analyses
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          dh.makeIQR()
- Transforms variables based on their interquartile range
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          dh.makeLmerForm()
- Make formulae for fitting multiple fractional polynomial models
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          dh.meanByGroup()
- Describes a numeric variable by strata of another numeric grouping variable.
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          dh.metaManual()
- Wrapper to manaully perform two-stage meta-analysis using metafor
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          dh.metaSepModels()
- Function in progress to meta-analyse separate models.
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          dh.multGLM()
- Loop multiple GLM models and handle errors & non-convergence
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          dh.pool()
- Perform Rubin's pooling on a list of imputed generalized linear models.
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          dh.predictLmer()
- Gets predicted values based on a new dataframe for lmer models
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          dh.quartileSplit()
- Splits a continuous variables into four quartiles
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          dh.stablisedWeights()
- Generate Stabilized Weights Using ds.glmSLMA output and outcome proportions.
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          dh.trimPredData()
- Trims predicted values based on min and max values provided
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          dh.zByGroup()
- Creates z-scores within specified bands