Package: structToolbox

 Type: Package

 Title: Data processing & analysis tools for Metabolomics and other omics

 Version: 1.0.1

 Authors@R: c(
     person(
         c("Gavin","Rhys"),
           "Lloyd",
            role=c("aut","cre"),
            email="[email protected]"),
     person(
         c("Ralf","Johannes", "Maria"),
           "Weber",
            role=c("aut"),
            email="[email protected]")
            )

 Description: An extensive set of data (pre-)processing and analysis methods and 
     tools for metabolomics and other omics, with a strong emphasis on statistics and 
     machine learning. This toolbox allows the user to build extensive and 
     standardised workflows for data analysis. The methods and tools have been 
     implemented using class-based templates provided by the struct (Statistics 
     in R Using Class-based Templates) package. The toolbox includes pre-processing 
     methods (e.g. signal drift and batch correction, normalisation, missing value 
     imputation and scaling), univariate (e.g. ttest, various forms of ANOVA, 
     Kruskal–Wallis test and more) and multivariate statistical methods (e.g. PCA and
     PLS, including cross-validation and permutation testing) as well as machine 
     learning methods (e.g. Support Vector Machines). The 
     STATistics Ontology (STATO) has been integrated and implemented to provide 
     standardised definitions for the different methods, inputs and outputs.

 License: GPL-3
 Encoding: UTF-8
 LazyData: true
 Collate:

     'AUC_metric_class.R'

     'entity_objects.R'

     'DFA_class.R'

     'anova_class.R'
     'HSD_class.R'
     'mixed_effect_class.R'
     'HSDEM_class.R'

     'MTBLS79_dataset_class.R'

     'PCA_class.R'
     'PCA_plotfcns.R'
     'PLSDA_class.R'
     'PLSDA_charts.R'
     'PLSR_class.R'

     'as_data_frame_doc.R'

     'autoscale_class.R'
     'balanced_accuracy_class.R'
     'blank_filter_class.R'

     'bootstrap_class.R'

     'calculate_doc.R'
     'chart_plot_doc.R'

     'classical_lsq_class.R'
     'confounders_clsq_class.R'

     'constant_sum_norm_class.R'

     'corr_coef_class.R'

     'd_ratio_filter_class.R'
     'dataset_chart_classes.R'
     'factor_barchart_class.R'
     'feature_profile_class.R'
     'filter_by_name_class.R'
     'filter_na_count.R'
     'filter_smeta_class.R'
     'fisher_exact_class.R'

     'fold_change_class.R'

     'fold_change_int_class.R'
     'forward_selection_by_rank_class.R'
     'ggplot_theme_pub.R'
     'glog_class.R'
     'grid_search_1d_class.R'
     'hca_class.R'
     'kfold_xval_class.R'
     'kfold_xval_charts.R'
     'knn_impute_class.R'
     'kw_rank_sum_class.R'
     'linear_model_class.R'
     'log_transform.R'
     'mean_centre_class.R'

     'mean_of_medians.R'

     'model_apply_doc.R'
     'model_predict_doc.R'
     'model_reverse_doc.R'
     'model_train_doc.R'

     'mv_feature_filter_class.R'
     'mv_sample_filter_class.R'

     'nroot_transform_class.R'

     'pairs_filter_class.R'

     'paretoscale_class.R'

     'permutation_test_class.R'
     'permute_sample_order_class.R'
     'pqn_norm_method_class.R'
     'prop_na_class.R'
     'r_squared_class.R'
     'rsd_filter.R'

     'run_doc.R'

     'sb_corr.R'

     'split_data_class.R'

     'stratified_split_class.R'

     'structToolbox.R'

     'svm_classifier_class.R'

     'tSNE_class.R'
     'ttest_class.R'
     'vec_norm_class.R'
     'wilcox_test_class.R'

     'zzz.R'

 Depends: R (>= 4.0), struct (>= 0.99.10)

 Imports: ggplot2,

     ggthemes,
     grid,
     gridExtra,
     methods,
     scales,
     sp,
     stats

 RoxygenNote: 7.1.0

 Suggests: agricolae,

  BiocFileCache,

  BiocStyle,

  car,

  covr,

  cowplot,
  e1071,

  emmeans,
  ggdendro,
  knitr,

  magick,

  nlme,

  openxlsx,

  pls,

  pmp,

  reshape2,

  ropls,

  rmarkdown,
  Rtsne,
  testthat

 VignetteBuilder: knitr

 biocViews: WorkflowStep, Metabolomics