Package index
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adaptivelassoCoefficients() - Return Adaptive lasso coefficients after finding optimal t
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addCatCovariates() - Make dummy variable cols and updated covarsVec
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addorremoveCovariate() - Add covariate
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adjustedlassoCoefficients() - Return Adjusted adaptive lasso coefficients after finding optimal t
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backwardSearch() - Backward covariate search
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bootplot() - Produce delta objective function for boostrap
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bootstrapFit() - Bootstrap nlmixr2 fit
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buildcovInfo() - Build covInfo list from varsVec and covarsVec
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buildupatedUI() - Build updated from the covariate and variable vector list
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covarSearchAuto() - Stepwise Covariate Model-selection (SCM) method
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fixedControl() - Control options for fixed-value likelihood profiling
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foldgen() - Stratified cross-validation fold generator function, inspired from the caret
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forwardSearch() - Forward covariate search
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horseshoeSummardf() - Create Horseshoe summary posterior estimates
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knit_print(<nlmixr2FitCore>)knit_print(<rxUi>) - Extract the equations from an nlmixr2/rxode2 model to produce a 'LaTeX' equation.
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lassoCoefficients() - Return Final lasso coefficients after finding optimal t
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lassoSummardf() - Create Lasso summary posterior estimates
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llpControl() - Control options for log-likelihood profiling
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normalizedData() - Function to return data of normalized covariates
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optimUnisampling() - Sample from uniform distribution by optim
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preconditionFit() - Linearly re-parameterize the model to be less sensitive to rounding errors
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profile(<nlmixr2FitCore>) - Perform likelihood profiling on nlmixr2 focei fits
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profileFixed()profileFixedSingle() - Estimate the objective function values for a model while fixing defined parameter values
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profileLlp() - Profile confidence intervals with log-likelihood profiling
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profileNlmixr2FitCoreRet() - Give the output data.frame for a single model for profile.nlmixr2FitCore
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regularmodel() - Regular lasso model
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theoFitOde - Example single dose Theophylline ODE model