Derivation of Regression-Based Normative Data

Normative data are often used to estimate the relative position of a raw test score in the population. This package allows for deriving regression-based normative data. It includes functions that enable the fitting of regression models for the mean and residual (or variance) structures, test the model assumptions, derive the normative data in the form of normative tables or automatic scoring sheets, and estimate confidence intervals for the norms. This package accompanies the book Van der Elst, W. (2024). Regression-based normative data for psychological assessment. A hands-on approach using R. Springer Nature.

Community Ecology Package

Ordination methods, diversity analysis and other functions for community and vegetation ecologists.

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>.

Highly Adaptive Lasso Conditional Density Estimation

An algorithm for flexible conditional density estimation based on application of pooled hazard regression to an artificial repeated measures dataset constructed by discretizing the support of the outcome variable. To facilitate flexible estimation of the conditional density, the highly adaptive lasso, a non-parametric regression function shown to estimate cadlag (RCLL) functions at a suitably fast convergence rate, is used. The use of pooled hazards regression for conditional density estimation as implemented here was first described for by Díaz and van der Laan (2011) . Building on the conditional density estimation utilities, non-parametric inverse probability weighted (IPW) estimators of the causal effects of additive modified treatment policies are implemented, using conditional density estimation to estimate the generalized propensity score. Non-parametric IPW estimators based on this can be coupled with undersmoothing of the generalized propensity score estimator to attain the semi-parametric efficiency bound (per Hejazi, Díaz, and van der Laan ).

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 ). Routines are provided to generate distances from generalised linear models (propensity score matching), formulas giving variables on which to limit matched distances, stratified or exact matching directives, or calipers, alone or in combination.

Signal Processing

R implementation of the 'Octave' package 'signal', containing a variety of signal processing tools, such as signal generation and measurement, correlation and convolution, filtering, filter design, filter analysis and conversion, power spectrum analysis, system identification, decimation and sample rate change, and windowing.

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 . For more details about the package please see: Van der Maaten et al. 2016 .

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.

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.

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.