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  "Title": "Data processing & analysis tools for Metabolomics and other\nomics",
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    "extra/manual.pdf",
    "extra/NEWS.html",
    "extra/NEWS.txt",
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    "extra/readme.md",
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  "_homeurl": "https://github.com/computational-metabolomics/structtoolbox",
  "_realowner": "bioc",
  "_cranurl": false,
  "_exports": [
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    "as_data_frame",
    "AUC",
    "autoscale",
    "balanced_accuracy",
    "balanced_error",
    "blank_filter",
    "blank_filter_hist",
    "bootstrap",
    "calculate",
    "chart_plot",
    "classical_lsq",
    "compare_dist",
    "confounders_clsq",
    "confounders_lsq_barchart",
    "confounders_lsq_boxplot",
    "constant_sum_norm",
    "corr_coef",
    "DatasetExperiment_boxplot",
    "DatasetExperiment_dist",
    "DatasetExperiment_factor_boxplot",
    "DatasetExperiment_heatmap",
    "DFA",
    "dfa_scores_plot",
    "dratio_filter",
    "equal_split",
    "feature_boxplot",
    "feature_profile",
    "feature_profile_array",
    "filter_by_name",
    "filter_na_count",
    "filter_smeta",
    "fisher_exact",
    "fold_change",
    "fold_change_int",
    "fold_change_plot",
    "forward_selection_by_rank",
    "fs_line",
    "glog_opt_plot",
    "glog_transform",
    "grid_search_1d",
    "gs_line",
    "HCA",
    "hca_dendrogram",
    "HSD",
    "HSDEM",
    "kfold_xval",
    "kfoldxcv_grid",
    "kfoldxcv_metric",
    "knn_impute",
    "kw_p_hist",
    "kw_rank_sum",
    "linear_model",
    "log_transform",
    "mean_centre",
    "mean_of_medians",
    "mixed_effect",
    "model_apply",
    "model_predict",
    "model_reverse",
    "model_train",
    "MTBLS79_DatasetExperiment",
    "mv_boxplot",
    "mv_feature_filter",
    "mv_feature_filter_hist",
    "mv_histogram",
    "mv_sample_filter",
    "mv_sample_filter_hist",
    "nroot_transform",
    "ontology_cache",
    "OPLSDA",
    "OPLSR",
    "pairs_filter",
    "pareto_scale",
    "PCA",
    "pca_biplot",
    "pca_correlation_plot",
    "pca_dstat_plot",
    "pca_loadings_plot",
    "pca_scores_plot",
    "pca_scree_plot",
    "permutation_test",
    "permutation_test_plot",
    "permute_sample_order",
    "pls_regcoeff_plot",
    "pls_scores_plot",
    "pls_vip_plot",
    "PLSDA",
    "plsda_feature_importance_plot",
    "plsda_predicted_plot",
    "plsda_roc_plot",
    "plsda_scores_plot",
    "PLSR",
    "plsr_cook_dist",
    "plsr_prediction_plot",
    "plsr_qq_plot",
    "plsr_residual_hist",
    "pqn_norm",
    "pqn_norm_hist",
    "prop_na",
    "r_squared",
    "resample",
    "resample_chart",
    "rsd_filter",
    "rsd_filter_hist",
    "run",
    "sb_corr",
    "scatter_chart",
    "split_data",
    "stratified_split",
    "SVM",
    "svm_plot_2d",
    "tic_chart",
    "tSNE",
    "tSNE_scatter",
    "ttest",
    "vec_norm",
    "wilcox_p_hist",
    "wilcox_test"
  ],
  "_help": [
    {
      "page": "ANOVA",
      "title": "Analysis of Variance",
      "topics": [
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    },
    {
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      "title": "Convert to data.frame",
      "topics": [
        "as_data_frame",
        "as_data_frame,filter_na_count-method",
        "as_data_frame,ttest-method",
        "as_data_frame,wilcox_test-method"
      ]
    },
    {
      "page": "AUC",
      "title": "Area under ROC curve",
      "topics": [
        "AUC"
      ]
    },
    {
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      "title": "Autoscaling",
      "topics": [
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      ]
    },
    {
      "page": "balanced_accuracy",
      "title": "Balanced Accuracy",
      "topics": [
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      ]
    },
    {
      "page": "balanced_error",
      "title": "Balanced error",
      "topics": [
        "balanced_error"
      ]
    },
    {
      "page": "blank_filter",
      "title": "Blank filter",
      "topics": [
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      ]
    },
    {
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      "title": "Histogram of blank filter fold changes",
      "topics": [
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      ]
    },
    {
      "page": "bootstrap",
      "title": "Bootstrap resampling",
      "topics": [
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      ]
    },
    {
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      "title": "Calculate metric",
      "topics": [
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        "calculate,AUC-method",
        "calculate,balanced_accuracy-method",
        "calculate,balanced_error-method",
        "calculate,r_squared-method"
      ]
    },
    {
      "page": "chart_plot",
      "title": "chart_plot method",
      "topics": [
        "chart_plot",
        "chart_plot,blank_filter_hist,blank_filter-method",
        "chart_plot,compare_dist,DatasetExperiment-method",
        "chart_plot,confounders_lsq_barchart,confounders_clsq-method",
        "chart_plot,confounders_lsq_boxplot,confounders_clsq-method",
        "chart_plot,DatasetExperiment_boxplot,DatasetExperiment-method",
        "chart_plot,DatasetExperiment_dist,DatasetExperiment-method",
        "chart_plot,DatasetExperiment_factor_boxplot,DatasetExperiment-method",
        "chart_plot,DatasetExperiment_heatmap,DatasetExperiment-method",
        "chart_plot,dfa_scores_plot,DFA-method",
        "chart_plot,feature_boxplot,DatasetExperiment-method",
        "chart_plot,feature_profile,DatasetExperiment-method",
        "chart_plot,feature_profile,sb_corr-method",
        "chart_plot,feature_profile_array,DatasetExperiment-method",
        "chart_plot,fold_change_plot,fold_change-method",
        "chart_plot,fs_line,forward_selection_by_rank-method",
        "chart_plot,glog_opt_plot,glog_transform-method",
        "chart_plot,gs_line,grid_search_1d-method",
        "chart_plot,hca_dendrogram,HCA-method",
        "chart_plot,kfoldxcv_grid,kfold_xval-method",
        "chart_plot,kfoldxcv_metric,kfold_xval-method",
        "chart_plot,kw_p_hist,kw_rank_sum-method",
        "chart_plot,mv_boxplot,DatasetExperiment-method",
        "chart_plot,mv_feature_filter_hist,mv_feature_filter-method",
        "chart_plot,mv_histogram,DatasetExperiment-method",
        "chart_plot,mv_sample_filter_hist,mv_sample_filter-method",
        "chart_plot,pca_biplot,PCA-method",
        "chart_plot,pca_correlation_plot,PCA-method",
        "chart_plot,pca_dstat_plot,PCA-method",
        "chart_plot,pca_loadings_plot,PCA-method",
        "chart_plot,pca_scores_plot,PCA-method",
        "chart_plot,pca_scree_plot,PCA-method",
        "chart_plot,permutation_test_plot,permutation_test-method",
        "chart_plot,plsda_feature_importance_plot,PLSDA-method",
        "chart_plot,plsda_predicted_plot,PLSDA-method",
        "chart_plot,plsda_roc_plot,PLSDA-method",
        "chart_plot,plsr_cook_dist,PLSR-method",
        "chart_plot,plsr_prediction_plot,PLSR-method",
        "chart_plot,plsr_qq_plot,PLSR-method",
        "chart_plot,plsr_residual_hist,PLSR-method",
        "chart_plot,pls_regcoeff_plot,PLSR-method",
        "chart_plot,pls_scores_plot,PLSR-method",
        "chart_plot,pls_vip_plot,PLSR-method",
        "chart_plot,pqn_norm_hist,pqn_norm-method",
        "chart_plot,resample_chart,resample-method",
        "chart_plot,rsd_filter_hist,rsd_filter-method",
        "chart_plot,scatter_chart,DatasetExperiment-method",
        "chart_plot,svm_plot_2d,SVM-method",
        "chart_plot,tic_chart,DatasetExperiment-method",
        "chart_plot,tSNE_scatter,tSNE-method",
        "chart_plot,wilcox_p_hist,wilcox_test-method"
      ]
    },
    {
      "page": "classical_lsq",
      "title": "Univariate Classical Least Squares Regression",
      "topics": [
        "classical_lsq"
      ]
    },
    {
      "page": "compare_dist",
      "title": "Compare distributions",
      "topics": [
        "compare_dist"
      ]
    },
    {
      "page": "confounders_clsq",
      "title": "Check for confounding factors",
      "topics": [
        "confounders_clsq"
      ]
    },
    {
      "page": "confounders_lsq_barchart",
      "title": "Confounding factor relative change barchart",
      "topics": [
        "confounders_lsq_barchart"
      ]
    },
    {
      "page": "confounders_lsq_boxplot",
      "title": "Confounding factor relative change boxplot",
      "topics": [
        "confounders_lsq_boxplot"
      ]
    },
    {
      "page": "constant_sum_norm",
      "title": "Normalisation to constant sum",
      "topics": [
        "constant_sum_norm"
      ]
    },
    {
      "page": "corr_coef",
      "title": "Correlation coefficient",
      "topics": [
        "corr_coef"
      ]
    },
    {
      "page": "DatasetExperiment_boxplot",
      "title": "Feature distribution histogram",
      "topics": [
        "DatasetExperiment_boxplot"
      ]
    },
    {
      "page": "DatasetExperiment_dist",
      "title": "Feature distribution histogram",
      "topics": [
        "DatasetExperiment_dist"
      ]
    },
    {
      "page": "DatasetExperiment_factor_boxplot",
      "title": "Factor boxplot",
      "topics": [
        "DatasetExperiment_factor_boxplot"
      ]
    },
    {
      "page": "DatasetExperiment_heatmap",
      "title": "DatasetExperiment heatmap",
      "topics": [
        "DatasetExperiment_heatmap"
      ]
    },
    {
      "page": "DFA",
      "title": "Discriminant Factor Analysis",
      "topics": [
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      ]
    },
    {
      "page": "dfa_scores_plot",
      "title": "DFA scores plot",
      "topics": [
        "dfa_scores_plot"
      ]
    },
    {
      "page": "dratio_filter",
      "title": "Dispersion ratio filter",
      "topics": [
        "dratio_filter"
      ]
    },
    {
      "page": "equal_split",
      "title": "Equal group sized sampling",
      "topics": [
        "equal_split"
      ]
    },
    {
      "page": "feature_boxplot",
      "title": "Feature boxplot",
      "topics": [
        "feature_boxplot"
      ]
    },
    {
      "page": "feature_profile",
      "title": "Feature profile",
      "topics": [
        "feature_profile"
      ]
    },
    {
      "page": "feature_profile_array",
      "title": "Feature profile",
      "topics": [
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      ]
    },
    {
      "page": "filter_by_name",
      "title": "Filter by name",
      "topics": [
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    },
    {
      "page": "filter_na_count",
      "title": "Minimum number of measured values filter",
      "topics": [
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      ]
    },
    {
      "page": "filter_smeta",
      "title": "Filter by sample meta data",
      "topics": [
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      ]
    },
    {
      "page": "fisher_exact",
      "title": "Fisher Exact Test",
      "topics": [
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    },
    {
      "page": "fold_change",
      "title": "Fold change",
      "topics": [
        "fold_change"
      ]
    },
    {
      "page": "fold_change_int",
      "title": "Fold change for interactions between factors",
      "topics": [
        "fold_change_int"
      ]
    },
    {
      "page": "fold_change_plot",
      "title": "Fold change plot",
      "topics": [
        "fold_change_plot"
      ]
    },
    {
      "page": "forward_selection_by_rank",
      "title": "Forward selection by rank",
      "topics": [
        "forward_selection_by_rank"
      ]
    },
    {
      "page": "fs_line",
      "title": "Forward selection line plot",
      "topics": [
        "fs_line"
      ]
    },
    {
      "page": "glog_opt_plot",
      "title": "Glog optimisation",
      "topics": [
        "glog_opt_plot"
      ]
    },
    {
      "page": "glog_transform",
      "title": "Generalised logarithmic transform",
      "topics": [
        "glog_transform"
      ]
    },
    {
      "page": "grid_search_1d",
      "title": "One dimensional grid search",
      "topics": [
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      ]
    },
    {
      "page": "gs_line",
      "title": "Grid search line plot",
      "topics": [
        "gs_line"
      ]
    },
    {
      "page": "HCA",
      "title": "Hierarchical Cluster Analysis",
      "topics": [
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      ]
    },
    {
      "page": "hca_dendrogram",
      "title": "HCA dendrogram",
      "topics": [
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      ]
    },
    {
      "page": "HSD",
      "title": "Tukey's Honest Significant Difference",
      "topics": [
        "HSD"
      ]
    },
    {
      "page": "HSDEM",
      "title": "Tukey's Honest Significant Difference using estimated marginal means",
      "topics": [
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      ]
    },
    {
      "page": "kfold_xval",
      "title": "k-fold cross-validation",
      "topics": [
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      ]
    },
    {
      "page": "kfoldxcv_grid",
      "title": "k-fold cross validation plot",
      "topics": [
        "kfoldxcv_grid"
      ]
    },
    {
      "page": "kfoldxcv_metric",
      "title": "kfoldxcv metric plot",
      "topics": [
        "kfoldxcv_metric"
      ]
    },
    {
      "page": "knn_impute",
      "title": "kNN missing value imputation",
      "topics": [
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      ]
    },
    {
      "page": "kw_p_hist",
      "title": "Histogram of p values",
      "topics": [
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      ]
    },
    {
      "page": "kw_rank_sum",
      "title": "Kruskal-Wallis rank sum test",
      "topics": [
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    },
    {
      "page": "linear_model",
      "title": "Linear model",
      "topics": [
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      ]
    },
    {
      "page": "log_transform",
      "title": "logarithm transform",
      "topics": [
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      ]
    },
    {
      "page": "mean_centre",
      "title": "Mean centre",
      "topics": [
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      ]
    },
    {
      "page": "mean_of_medians",
      "title": "Mean of medians",
      "topics": [
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      ]
    },
    {
      "page": "mixed_effect",
      "title": "Mixed effects model",
      "topics": [
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      ]
    },
    {
      "page": "model_apply",
      "title": "Apply method",
      "topics": [
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        "model_apply,ANOVA,DatasetExperiment-method",
        "model_apply,classical_lsq,DatasetExperiment-method",
        "model_apply,confounders_clsq,DatasetExperiment-method",
        "model_apply,constant_sum_norm,DatasetExperiment-method",
        "model_apply,corr_coef,DatasetExperiment-method",
        "model_apply,equal_split,DatasetExperiment-method",
        "model_apply,filter_smeta,DatasetExperiment-method",
        "model_apply,fisher_exact,DatasetExperiment-method",
        "model_apply,fold_change,DatasetExperiment-method",
        "model_apply,fold_change_int,DatasetExperiment-method",
        "model_apply,HCA,DatasetExperiment-method",
        "model_apply,HSD,DatasetExperiment-method",
        "model_apply,HSDEM,DatasetExperiment-method",
        "model_apply,knn_impute,DatasetExperiment-method",
        "model_apply,kw_rank_sum,DatasetExperiment-method",
        "model_apply,log_transform,DatasetExperiment-method",
        "model_apply,mean_of_medians,DatasetExperiment-method",
        "model_apply,mixed_effect,DatasetExperiment-method",
        "model_apply,nroot_transform,DatasetExperiment-method",
        "model_apply,pairs_filter,DatasetExperiment-method",
        "model_apply,prop_na,DatasetExperiment-method",
        "model_apply,rsd_filter,DatasetExperiment-method",
        "model_apply,sb_corr,DatasetExperiment-method",
        "model_apply,split_data,DatasetExperiment-method",
        "model_apply,stratified_split,DatasetExperiment-method",
        "model_apply,tSNE,DatasetExperiment-method",
        "model_apply,ttest,DatasetExperiment-method",
        "model_apply,vec_norm,DatasetExperiment-method",
        "model_apply,wilcox_test,DatasetExperiment-method"
      ]
    },
    {
      "page": "model_predict",
      "title": "Model prediction",
      "topics": [
        "model_predict",
        "model_predict,autoscale,DatasetExperiment-method",
        "model_predict,blank_filter,DatasetExperiment-method",
        "model_predict,constant_sum_norm,DatasetExperiment-method",
        "model_predict,DFA,DatasetExperiment-method",
        "model_predict,dratio_filter,DatasetExperiment-method",
        "model_predict,filter_by_name,DatasetExperiment-method",
        "model_predict,filter_na_count,DatasetExperiment-method",
        "model_predict,filter_smeta,DatasetExperiment-method",
        "model_predict,glog_transform,DatasetExperiment-method",
        "model_predict,linear_model,DatasetExperiment-method",
        "model_predict,mean_centre,DatasetExperiment-method",
        "model_predict,mv_feature_filter,DatasetExperiment-method",
        "model_predict,mv_sample_filter,DatasetExperiment-method",
        "model_predict,OPLSDA,DatasetExperiment-method",
        "model_predict,OPLSR,DatasetExperiment-method",
        "model_predict,pareto_scale,DatasetExperiment-method",
        "model_predict,PCA,DatasetExperiment-method",
        "model_predict,PLSDA,DatasetExperiment-method",
        "model_predict,PLSR,DatasetExperiment-method",
        "model_predict,pqn_norm,DatasetExperiment-method",
        "model_predict,SVM,DatasetExperiment-method",
        "model_predict,vec_norm,DatasetExperiment-method"
      ]
    },
    {
      "page": "model_reverse",
      "title": "Reverse preprocessing",
      "topics": [
        "model_reverse",
        "model_reverse,autoscale,DatasetExperiment-method",
        "model_reverse,mean_centre,DatasetExperiment-method"
      ]
    },
    {
      "page": "model_train",
      "title": "Train a model",
      "topics": [
        "model_train",
        "model_train,autoscale,DatasetExperiment-method",
        "model_train,blank_filter,DatasetExperiment-method",
        "model_train,constant_sum_norm,DatasetExperiment-method",
        "model_train,DFA,DatasetExperiment-method",
        "model_train,dratio_filter,DatasetExperiment-method",
        "model_train,filter_by_name,DatasetExperiment-method",
        "model_train,filter_na_count,DatasetExperiment-method",
        "model_train,filter_smeta,DatasetExperiment-method",
        "model_train,glog_transform,DatasetExperiment-method",
        "model_train,linear_model,DatasetExperiment-method",
        "model_train,mean_centre,DatasetExperiment-method",
        "model_train,mv_feature_filter,DatasetExperiment-method",
        "model_train,mv_sample_filter,DatasetExperiment-method",
        "model_train,OPLSDA,DatasetExperiment-method",
        "model_train,OPLSR,DatasetExperiment-method",
        "model_train,pareto_scale,DatasetExperiment-method",
        "model_train,PCA,DatasetExperiment-method",
        "model_train,PLSDA,DatasetExperiment-method",
        "model_train,PLSR,DatasetExperiment-method",
        "model_train,pqn_norm,DatasetExperiment-method",
        "model_train,SVM,DatasetExperiment-method",
        "model_train,vec_norm,DatasetExperiment-method"
      ]
    },
    {
      "page": "MTBLS79_DatasetExperiment",
      "title": "MTBLS79: Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control",
      "topics": [
        "MTBLS79_DatasetExperiment"
      ]
    },
    {
      "page": "mv_boxplot",
      "title": "Missing value boxplots",
      "topics": [
        "mv_boxplot"
      ]
    },
    {
      "page": "mv_feature_filter",
      "title": "Filter features by missing values",
      "topics": [
        "mv_feature_filter"
      ]
    },
    {
      "page": "mv_feature_filter_hist",
      "title": "Histogram of missing values per feature",
      "topics": [
        "mv_feature_filter_hist"
      ]
    },
    {
      "page": "mv_histogram",
      "title": "Missing value histogram",
      "topics": [
        "mv_histogram"
      ]
    },
    {
      "page": "mv_sample_filter",
      "title": "Missing value sample filter",
      "topics": [
        "mv_sample_filter"
      ]
    },
    {
      "page": "mv_sample_filter_hist",
      "title": "Histogram of missing values per sample",
      "topics": [
        "mv_sample_filter_hist"
      ]
    },
    {
      "page": "nroot_transform",
      "title": "nth root transform",
      "topics": [
        "nroot_transform"
      ]
    },
    {
      "page": "ontology_cache",
      "title": "ontology cache",
      "topics": [
        "ontology_cache"
      ]
    },
    {
      "page": "OPLSDA",
      "title": "Orthogonal Partial Least Squares regression",
      "topics": [
        "OPLSDA"
      ]
    },
    {
      "page": "OPLSR",
      "title": "Orthogonal Partial Least Squares regression",
      "topics": [
        "OPLSR"
      ]
    },
    {
      "page": "pairs_filter",
      "title": "Pairs filter",
      "topics": [
        "pairs_filter"
      ]
    },
    {
      "page": "pareto_scale",
      "title": "Pareto scaling",
      "topics": [
        "pareto_scale"
      ]
    },
    {
      "page": "PCA",
      "title": "Principal Component Analysis (PCA)",
      "topics": [
        "PCA"
      ]
    },
    {
      "page": "pca_biplot",
      "title": "PCA biplot",
      "topics": [
        "pca_biplot"
      ]
    },
    {
      "page": "pca_correlation_plot",
      "title": "PCA correlation plot",
      "topics": [
        "pca_correlation_plot"
      ]
    },
    {
      "page": "pca_dstat_plot",
      "title": "d-statistic plot",
      "topics": [
        "pca_dstat_plot"
      ]
    },
    {
      "page": "pca_loadings_plot",
      "title": "PCA loadings plot",
      "topics": [
        "pca_loadings_plot"
      ]
    },
    {
      "page": "pca_scores_plot",
      "title": "PCA scores plot",
      "topics": [
        "pca_scores_plot"
      ]
    },
    {
      "page": "pca_scree_plot",
      "title": "Scree plot",
      "topics": [
        "pca_scree_plot"
      ]
    },
    {
      "page": "permutation_test",
      "title": "Permutation test",
      "topics": [
        "permutation_test"
      ]
    },
    {
      "page": "permutation_test_plot",
      "title": "permutation_test_plot class",
      "topics": [
        "permutation_test_plot"
      ]
    },
    {
      "page": "permute_sample_order",
      "title": "Permute Sample Order",
      "topics": [
        "permute_sample_order"
      ]
    },
    {
      "page": "pls_regcoeff_plot",
      "title": "pls_regcoeff_plot class",
      "topics": [
        "pls_regcoeff_plot"
      ]
    },
    {
      "page": "pls_scores_plot",
      "title": "PLSDA scores plot",
      "topics": [
        "plsda_scores_plot",
        "pls_scores_plot",
        "pls_scores_plot,"
      ]
    },
    {
      "page": "pls_vip_plot",
      "title": "PLSDA VIP plot",
      "topics": [
        "pls_vip_plot"
      ]
    },
    {
      "page": "PLSDA",
      "title": "Partial least squares discriminant analysis",
      "topics": [
        "PLSDA"
      ]
    },
    {
      "page": "plsda_feature_importance_plot",
      "title": "PLSDA feature importance summary plot",
      "topics": [
        "plsda_feature_importance_plot"
      ]
    },
    {
      "page": "plsda_predicted_plot",
      "title": "PLSDA predicted plot",
      "topics": [
        "plsda_predicted_plot"
      ]
    },
    {
      "page": "plsda_roc_plot",
      "title": "PLSDA ROC plot",
      "topics": [
        "plsda_roc_plot"
      ]
    },
    {
      "page": "PLSR",
      "title": "Partial least squares regression",
      "topics": [
        "PLSR"
      ]
    },
    {
      "page": "plsr_cook_dist",
      "title": "Cook's distance barchart",
      "topics": [
        "plsr_cook_dist"
      ]
    },
    {
      "page": "plsr_prediction_plot",
      "title": "PLSR prediction plot",
      "topics": [
        "plsr_prediction_plot"
      ]
    },
    {
      "page": "plsr_qq_plot",
      "title": "PLSR QQ plot",
      "topics": [
        "plsr_qq_plot"
      ]
    },
    {
      "page": "plsr_residual_hist",
      "title": "PLSR residuals histogram",
      "topics": [
        "plsr_residual_hist"
      ]
    },
    {
      "page": "pqn_norm",
      "title": "Probabilistic Quotient Normalisation (PQN)",
      "topics": [
        "pqn_norm"
      ]
    },
    {
      "page": "pqn_norm_hist",
      "title": "PQN coefficient histogram",
      "topics": [
        "pqn_norm_hist"
      ]
    },
    {
      "page": "prop_na",
      "title": "Fisher's exact test for missing values",
      "topics": [
        "prop_na"
      ]
    },
    {
      "page": "r_squared",
      "title": "Coefficient of determination (R-squared)",
      "topics": [
        "r_squared"
      ]
    },
    {
      "page": "resample",
      "title": "Data resampling",
      "topics": [
        "resample"
      ]
    },
    {
      "page": "resample_chart",
      "title": "resample_chart class",
      "topics": [
        "resample_chart"
      ]
    },
    {
      "page": "rsd_filter",
      "title": "RSD filter",
      "topics": [
        "rsd_filter"
      ]
    },
    {
      "page": "rsd_filter_hist",
      "title": "RSD histogram",
      "topics": [
        "rsd_filter_hist"
      ]
    },
    {
      "page": "run",
      "title": "Runs an iterator, applying the chosen model multiple times.",
      "topics": [
        "run",
        "run,bootstrap,DatasetExperiment,metric-method",
        "run,forward_selection_by_rank,DatasetExperiment,metric-method",
        "run,grid_search_1d,DatasetExperiment,metric-method",
        "run,kfold_xval,DatasetExperiment,metric-method",
        "run,permutation_test,DatasetExperiment,metric-method",
        "run,permute_sample_order,DatasetExperiment,metric-method",
        "run,resample,DatasetExperiment,metric-method"
      ]
    },
    {
      "page": "sb_corr",
      "title": "Signal/batch correction for mass spectrometry data",
      "topics": [
        "sb_corr"
      ]
    },
    {
      "page": "scatter_chart",
      "title": "Group scatter chart",
      "topics": [
        "scatter_chart"
      ]
    },
    {
      "page": "split_data",
      "title": "Split data",
      "topics": [
        "split_data"
      ]
    },
    {
      "page": "stratified_split",
      "title": "Stratified sampling",
      "topics": [
        "stratified_split"
      ]
    },
    {
      "page": "SVM",
      "title": "Support Vector Machine Classifier",
      "topics": [
        "SVM"
      ]
    },
    {
      "page": "svm_plot_2d",
      "title": "SVM scatter plot",
      "topics": [
        "svm_plot_2d"
      ]
    },
    {
      "page": "tic_chart",
      "title": "Total Ion Count chart.",
      "topics": [
        "tic_chart"
      ]
    },
    {
      "page": "tSNE",
      "title": "tSNE",
      "topics": [
        "tSNE"
      ]
    },
    {
      "page": "tSNE_scatter",
      "title": "Feature boxplot",
      "topics": [
        "tSNE_scatter"
      ]
    },
    {
      "page": "ttest",
      "title": "t-test",
      "topics": [
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      ]
    },
    {
      "page": "vec_norm",
      "title": "Vector normalisation",
      "topics": [
        "vec_norm"
      ]
    },
    {
      "page": "wilcox_p_hist",
      "title": "Histogram of p values",
      "topics": [
        "wilcox_p_hist"
      ]
    },
    {
      "page": "wilcox_test",
      "title": "wilcoxon signed rank test",
      "topics": [
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      ]
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  "_vignettes": [
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      "source": "data_analysis_omics_using_the_structtoolbox.Rmd",
      "filename": "data_analysis_omics_using_the_structtoolbox.html",
      "title": "Data analysis of metabolomics and other omics datasets using the structToolbox",
      "author": "Gavin R Lloyd, Andris Jankevics, Ralf J Weber",
      "engine": "knitr::rmarkdown",
      "headings": [
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        "Getting started",
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      "created": "2020-04-22 20:15:10",
      "modified": "2026-02-21 12:45:44",
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