Man pages for Compack
Regression with Compositional Covariates

cglassoFit a linearly constrained linear regression model with group...
classoFit a linearly constrained linear regression model with lasso...
coef.compCLextracts model estimated coefficients from a '"compCL"'...
coef.cv.compCLExtract estimated coefficients from a '"cv.compCL"' object.
coef.cv.FuncompCGLExtract estiamted coefficients from a '"cv.FuncompCGL"'...
coef.FuncompCGLExtract estimated coefficients from a '"FuncompCGL"' object.
coef.GIC.compCLExtracts estimated coefficients from a '"GIC.compCL"' object.
coef.GIC.FuncompCGLExtract model estimated coefficients from a...
compCLFit regularization path for log-contrast model of...
comp_ModelSimulation for log-contrast model.
cv.compCLCross-validation for compCL.
cv.FuncompCGLCross-validation for FuncompCGL.
Fcomp_ModelSimulation for functional composition data.
FuncompCGLFit regularization paths of sparse log-contrast regression...
GIC.compCLCompute information crieteria for the 'compCL' model.
GIC.FuncompCGLCompute information crieteria for the 'FuncompCGL' model.
plot.compCLPlot solution paths from a '"compCL"' object.
plot.cv.compCLPlot the cross-validation curve produced by '"cv.compCL"'...
plot.cv.FuncompCGLPlot the cross-validation curve produced by...
plot.FuncompCGLPlot solution paths from a '"FuncompCGL"' object.
plot.GIC.compCLPlot the GIC curve produced by '"GIC.compCL"' object.
plot.GIC.FuncompCGLPlot the GIC curve produced by '"GIC.FuncompCGL"' object.
predict.compCLMake predictions based on a '"compCL"' object.
predict.cv.compCLMake predictions based on a '"cv.compCL"' object.
predict.cv.FuncompCGLMake predictions based on a '"cv.FuncompCGL"' object.
predict.FuncompCGLMake prediction from a '"FuncompCGL"' object.
predict.GIC.compCLMake predictions based on a '"GIC.compCL"' object.
predict.GIC.FuncompCGLMake predictions based on a '"GIC.FuncompCGL"' object.
print.compCLPrint a '"compCL"' object.
print.FuncompCGLPrint a '"FuncompCGL"' object.
Compack documentation built on July 1, 2020, 10:26 p.m.