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Functions for Optimal Matching
Distance based bipartite matching using minimum cost flow, oriented
to matching of treatment and control groups in observational studies ('Hansen'
and 'Klopfer' 2006
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>.
Efficient Leave-One-Out Cross-Validation and WAIC for Bayesian Models
Efficient approximate leave-one-out cross-validation (LOO)
for Bayesian models fit using Markov chain Monte Carlo, as described
in Vehtari, Gelman, and Gabry (2017)
R Code for Mark Analysis
An interface to the software package MARK that constructs input files for MARK and extracts the output. MARK was developed by Gary White and is freely available at < http://www.phidot.org/software/mark/downloads/> but is not open source.
Vectorised Tools for URL Handling and Parsing
A toolkit for all URL-handling needs, including encoding and decoding, parsing, parameter extraction and modification. All functions are designed to be both fast and entirely vectorised. It is intended to be useful for people dealing with web-related datasets, such as server-side logs, although may be useful for other situations involving large sets of URLs.
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.
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
"Hit and Run" and "Shake and Bake" for Sampling Uniformly from Convex Shapes
The "Hit and Run" Markov Chain Monte Carlo method for sampling uniformly from convex shapes defined by linear constraints, and the "Shake and Bake" method for sampling from the boundary of such shapes. Includes specialized functions for sampling normalized weights with arbitrary linear constraints. Tervonen, T., van Valkenhoef, G., Basturk, N., and Postmus, D. (2012)
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.
Easily Work with 'Bootstrap' Icons
Easily use 'Bootstrap' icons inside 'Shiny' apps and 'R Markdown' documents. More generally, icons can be inserted in any 'htmltools' document through inline 'SVG'.