
Package index
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AICpls() - AIC function for plsR models
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CorMat - Correlation matrix for simulating plsR datasets
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Cornell - Cornell dataset
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PLS_glm_wvc() - Light version of PLS_glm for cross validation purposes
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PLS_lm_wvc() - Light version of PLS_lm for cross validation purposes
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XbordeauxNA - Incomplete dataset for the quality of wine dataset
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XpineNAX21 - Incomplete dataset from the pine caterpillars example
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aic.dof()bic.dof()gmdl.dof() - Akaike and Bayesian Information Criteria and Generalized minimum description length
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aze - Microsatellites Dataset
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aze_compl - As aze without missing values
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bootpls() - Non-parametric Bootstrap for PLS models
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bootplsglm() - Non-parametric Bootstrap for PLS generalized linear models
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bordeaux - Quality of wine dataset
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bordeauxNA - Quality of wine dataset
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boxplots.bootpls() - Boxplot bootstrap distributions
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coef(<plsRglmmodel>) - coef method for plsR models
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coef(<plsRmodel>) - coef method for plsR models
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coefs.plsR() - Coefficients for bootstrap computations of PLSR models
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coefs.plsR.raw() - Raw coefficients for bootstrap computations of PLSR models
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coefs.plsRglm() - Coefficients for bootstrap computations of PLSGLR models
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coefs.plsRglm.raw() - Raw coefficients for bootstrap computations of PLSGLR models
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coefs.plsRglmnp() - Coefficients for bootstrap computations of PLSGLR models
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coefs.plsRnp() - Coefficients for bootstrap computations of PLSR models
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confints.bootpls() - Bootstrap confidence intervals
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cv.plsR()cv.plsRmodel(<default>)cv.plsRmodel(<formula>)PLS_lm_kfoldcv()PLS_lm_kfoldcv_formula() - Partial least squares regression models with k-fold cross-validation
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cv.plsRglm()cv.plsRglmmodel(<default>)cv.plsRglmmodel(<formula>)PLS_glm_kfoldcv()PLS_glm_kfoldcv_formula() - Partial least squares regression glm models with k-fold cross validation
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cvtable() - Table method for summary of cross validated PLSR and PLSGLR models
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dicho() - Dichotomization
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fowlkes - Fowlkes dataset
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infcrit.dof() - Information criteria
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kfolds2CVinfos_glm() - Extracts and computes information criteria and fits statistics for k-fold cross validated partial least squares glm models
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kfolds2CVinfos_lm() - Extracts and computes information criteria and fits statistics for k-fold cross validated partial least squares models
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kfolds2Chisq() - Computes Predicted Chisquare for k-fold cross-validated partial least squares regression models.
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kfolds2Chisqind() - Computes individual Predicted Chisquare for k-fold cross validated partial least squares regression models.
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kfolds2Mclassed() - Number of missclassified individuals for k-fold cross validated partial least squares regression models.
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kfolds2Mclassedind() - Number of missclassified individuals per group for k-fold cross validated partial least squares regression models.
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kfolds2Press() - Computes PRESS for k-fold cross validated partial least squares regression models.
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kfolds2Pressind() - Computes individual PRESS for k-fold cross validated partial least squares regression models.
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kfolds2coeff() - Extracts coefficients from k-fold cross validated partial least squares regression models
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loglikpls() - loglikelihood function for plsR models
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permcoefs.plsR() - Coefficients for permutation bootstrap computations of PLSR models
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permcoefs.plsR.raw() - Raw coefficients for permutation bootstrap computations of PLSR models
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permcoefs.plsRglm() - Coefficients for permutation bootstrap computations of PLSGLR models
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permcoefs.plsRglm.raw() - Raw coefficients for permutation bootstrap computations of PLSGLR models
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permcoefs.plsRglmnp() - Coefficients for permutation bootstrap computations of PLSGLR models
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permcoefs.plsRnp() - Coefficients computation for permutation bootstrap
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pine - Pine dataset
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pineNAX21 - Incomplete dataset from the pine caterpillars example
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pine_full - Complete Pine dataset
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pine_sup - Complete Pine dataset
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plot(<table.summary.cv.plsRglmmodel>) - Plot method for table of summary of cross validated plsRglm models
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plot(<table.summary.cv.plsRmodel>) - Plot method for table of summary of cross validated plsR models
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plots.confints.bootpls() - Plot bootstrap confidence intervals
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plsR()plsRmodel(<default>)plsRmodel(<formula>)PLS_lm()PLS_lm_formula() - Partial least squares Regression models with leave one out cross validation
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plsR(<dof>) - Computation of the Degrees of Freedom
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plsRglm()plsRglmmodel(<default>)plsRglmmodel(<formula>)PLS_glm()PLS_glm_formula() - Partial least squares Regression generalized linear models
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predict(<plsRglmmodel>) - Print method for plsRglm models
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predict(<plsRmodel>) - Print method for plsR models
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print(<coef.plsRglmmodel>) - Print method for plsRglm models
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print(<coef.plsRmodel>) - Print method for plsR models
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print(<cv.plsRglmmodel>) - Print method for plsRglm models
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print(<cv.plsRmodel>) - Print method for plsR models
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print(<plsRglmmodel>) - Print method for plsRglm models
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print(<plsRmodel>) - Print method for plsR models
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print(<summary.plsRglmmodel>) - Print method for summaries of plsRglm models
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print(<summary.plsRmodel>) - Print method for summaries of plsR models
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signpred() - Graphical assessment of the stability of selected variables
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simul_data_UniYX() - Data generating function for univariate plsR models
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simul_data_UniYX_binom() - Data generating function for univariate binomial plsR models
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simul_data_YX() - Data generating function for multivariate plsR models
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simul_data_complete() - Data generating detailed process for multivariate plsR models
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summary(<cv.plsRglmmodel>) - Summary method for plsRglm models
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summary(<cv.plsRmodel>) - Summary method for plsR models
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summary(<plsRglmmodel>) - Summary method for plsRglm models
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summary(<plsRmodel>) - Summary method for plsR models
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tilt.bootpls() - Non-parametric tilted bootstrap for PLS regression models
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tilt.bootplsglm() - Non-parametric tilted bootstrap for PLS generalized linear regression models