Collection of Machine Learning Datasets for Supervised Machine Learning

Contains a collection of datasets for working with machine learning tasks. It will contain datasets for supervised machine learning Jiang (2020) and will include datasets for classification and regression. The aim of this package is to use data generated around health and other domains.

Multiple Checks on MEDITS Trawl Survey Data

Provides quality checks for MEDITS (International Bottom Trawl Survey in the Mediterranean) trawl survey exchange data tables (TA (Haul data), TB (Catch data), TC (Biological data), TE (Biological individual data), TL (Litter data)). The main function RoME() calls all check functions in a defined sequence to perform a complete quality control of TX (Generic exchange data) data, including header validation, controlled-vocabulary checks, cross-table consistency tests, and biological plausibility checks. No automatic correction is applied: the package detects errors, warns the user, and specifies the type of error to ease data correction. Checks can be run simultaneously on multi-year datasets. An embedded 'shiny' application is also provided via run_RoME_app(). References describing the methods: MEDITS Working Group (2017) < https://www.sibm.it/MEDITS%202011/principaledownload.htm>.

Allow Misspellings of Length Function

Convenient aliases for common ways of misspelling the base R function length(). These include every permutation of the final three letters.

Automated Moderated Nonlinear Factor Analysis Using 'M-plus'

Automated generation, running, and interpretation of moderated nonlinear factor analysis models for obtaining scores from observed variables, using the method described by Gottfredson and colleagues (2019) . This package creates M-plus input files which may be run iteratively to test two different types of covariate effects on items: (1) latent variable impact (both mean and variance); and (2) differential item functioning. After sequentially testing for all effects, it also creates a final model by including all significant effects after adjusting for multiple comparisons. Finally, the package creates a scoring model which uses the final values of parameter estimates to generate latent variable scores. \n\n This package generates TEMPLATES for M-plus inputs, which can and should be inspected, altered, and run by the user. In addition to being presented without warranty of any kind, the package is provided under the assumption that everyone who uses it is reading, interpreting, understanding, and altering every M-plus input and output file. There is no one right way to implement moderated nonlinear factor analysis, and this package exists solely to save users time as they generate M-plus syntax according to their own judgment.

Analysis of MEDITS-Like Survey Data

Set of functions working with survey data in the format of the MEDITS project < https://www.sibm.it/SITO%20MEDITS/principaleprogramme.htm>. In this version, functions use TA, TB and TC tables respectively containing haul, catch and aggregated biological data.

Linear, Logistic and Generalized Linear Models Regressions for the EnvWAS/EWAS Approach

Tool for Environment-Wide Association Studies (EnvWAS / EWAS) which are repeated analysis. It includes three functions. One function for linear regression, a second for logistic regression and a last one for generalized linear models.

Auto-GO: Reproducible, Robust and High Quality Ontology Enrichment Visualizations

Auto-GO is a framework that enables automated, high quality Gene Ontology enrichment analysis visualizations. It also features a handy wrapper for Differential Expression analysis around the 'DESeq2' package described in Love et al. (2014) . The whole framework is structured in different, independent functions, in order to let the user decide which steps of the analysis to perform and which plot to produce.