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Efficient Plotting of Large-Sized Data
A tool to plot data with a large sample size using 'shiny' and 'plotly'. Relatively small samples are obtained from the original data using a specific algorithm. The samples are updated according to a user-defined x range. Jonas Van Der Donckt, Jeroen Van Der Donckt, Emiel Deprost (2022) < https://github.com/predict-idlab/plotly-resampler>.
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.
Bayesian Latent Variable Analysis
Fit a variety of Bayesian latent variable models, including confirmatory
factor analysis, structural equation models, and latent growth curve models. References: Merkle & Rosseel (2018)
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.
Tidy Interface to 'data.table'
A tidy interface to 'data.table', giving users the speed of 'data.table' while using tidyverse-like syntax.
Data Table Back-End for 'dplyr'
Provides a data.table backend for 'dplyr'. The goal of 'dtplyr' is to allow you to write 'dplyr' code that is automatically translated to the equivalent, but usually much faster, data.table code.
Read, Write and Edit 'xlsx' Files
Simplifies the creation of 'xlsx' files by providing a high level interface to writing, styling and editing worksheets.
Analyzing Dendrometer Data
Various functions to import, verify, process and plot
high-resolution dendrometer data using daily and stem-cycle approaches
as described in Deslauriers et al, 2007
'BERTopic'-Style Topic Modeling Without 'Python'
Implements the 'BERTopic' topic modeling pipeline directly in R: transformer-based sentence embedding, Uniform Manifold Approximation and Projection dimensionality reduction, Hierarchical Density-Based Spatial Clustering of Applications with Noise clustering, and class-based term frequency-inverse document frequency topic extraction - all without any dependency on 'Python', 'conda', or 'reticulate'. Every stage runs in R through 'torch', 'safetensors', 'tok', 'uwot', and 'dbscan'. The package mirrors the accessor API of the original 'Python' package, adds integrated quality metrics and hyperparameter search tools, and introduces part-of-speech filtered and C-value-ranked representation models.
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.