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Various Functions to Facilitate Visualization of Data and Analysis
When analyzing data, plots are a helpful tool for visualizing data and interpreting statistical models. This package provides a set of simple tools for building plots incrementally, starting with an empty plot region, and adding bars, data points, regression lines, error bars, gradient legends, density distributions in the margins, and even pictures. The package builds further on R graphics by simply combining functions and settings in order to reduce the amount of code to produce for the user. As a result, the package does not use formula input or special syntax, but can be used in combination with default R plot functions. Note: Most of the functions were part of the package 'itsadug', which is now split in two packages: 1. the package 'itsadug', which contains the core functions for visualizing and evaluating nonlinear regression models, and 2. the package 'plotfunctions', which contains more general plot functions.
Classification and Regression Tests
Provides wrapper functions for running classification and regression tests using different machine learning techniques, such as Random Forests and decision trees. The package provides standardized methods for preparing data to suit the algorithm's needs, training a model, making predictions, and evaluating results. Also, some functions are provided to run multiple instances of a test.
Minimization Methods for Ill-Conditioned Problems
Implementation of methods for minimizing ill-conditioned problems. Currently only includes regularized (quasi-)newton optimization (Kanzow and Steck et al. (2023),
Gene Cluster Visualizations
Provides tools for plotting gene clusters and transcripts by
importing data from GenBank, FASTA, and GFF files. It performs BLASTP and
MUMmer alignments [Altschul et al. (1990)
'shiny' Cron Expression Input Widget
A widget for 'shiny' apps to handle schedule expression input, using the 'cron-expression-input' JavaScript component. Note that this does not edit the 'crontab' file, it is just an input element for the schedules. See < https://github.com/DatalabFabriek/shinycroneditor/blob/main/inst/examples/shiny-app.R> for an example implementation.
Predictive Power Score
The Predictive Power Score (PPS) is an asymmetric, data-type-agnostic score that can detect linear or non-linear relationships between two variables. The score ranges from 0 (no predictive power) to 1 (perfect predictive power). PPS can be useful for data exploration purposes, in the same way correlation analysis is. For more information on PPS, see < https://github.com/paulvanderlaken/ppsr>.
Relative Distribution Methods
Tools for the comparison of distributions. This includes nonparametric estimation of the relative distribution PDF and CDF and numerical summaries as described in "Relative Distribution Methods in the Social Sciences" by Mark S. Handcock and Martina Morris, Springer-Verlag, 1999, Springer-Verlag, ISBN 0387987789.
R Interface to 'Keras'
Interface to 'Keras' < https://keras.io>, a high-level neural networks API. 'Keras' was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations of the two), and runs seamlessly on both CPU and GPU devices.
Multiple Imputation of Covariates by Substantive Model Compatible Fully Conditional Specification
Implements multiple imputation of missing covariates by Substantive Model Compatible Fully Conditional Specification. This is a modification of the popular FCS/chained equations multiple imputation approach, and allows imputation of missing covariate values from models which are compatible with the user specified substantive model.
Download and Tidy Data from the Australian Prudential Regulation Authority
Download the latest data from the Australian Prudential Regulation Authority < https://www.apra.gov.au/> and import it into R as a tidy data frame.