DESCRIPTION

Package: structToolbox
Type: Package
Title: Data processing & analysis tools for Metabolomics and other omics
Version: 1.0.0
Authors@R: c(
    person(
        c("Gavin","Rhys"),
          "Lloyd",
           role=c("aut","cre"),
           email="g.r.lloyd@bham.ac.uk"),
    person(
        c("Ralf","Johannes", "Maria"),
          "Weber",
           role=c("aut"),
           email="r.j.weber@bham.ac.uk")
           )
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