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Geographically Weighted Fractal Analysis
Performs Geographically Weighted Fractal Analysis (GWFA) to calculate the local fractal dimension of a set of points. GWFA mixes the Sandbox multifractal algorithm and the Geographically Weighted Regression. Unlike fractal box-counting algorithm, the sandbox algorithm avoids border effects because the boxes are adjusted on the set of points. The Geographically Weighted approach consists in applying a kernel that describes the way the neighbourhood of each estimated point is taken into account to estimate its fractal dimension. GWFA can be used to discriminate built patterns of a city, a region, or a whole country.
Provides Progress Bars in 'knitr'
Provides a progress bar similar to 'dplyr' that can write progress out to a variety of locations, including stdout(), stderr(), or from file(). Useful when using 'knitr' or 'rmarkdown', and you still want to see progress of calculations in the terminal.
Quantify the Relationship Between Development Rate and Temperature in Ectotherms
A set of functions to quantify the relationship between development
rate and temperature and to build phenological models. The package comprises
a set of models and estimated parameters borrowed from a literature review
in ectotherms. The methods and literature review are described in Rebaudo
et al. (2018)
Sensory Data Analysis
Statistical Methods to Analyse Sensory Data. SensoMineR: A package for sensory data analysis. S. Le and F. Husson (2008)
Graphical User Interface for FactoMineR
Rcmdr Plugin for the 'FactoMineR' package.
Add Even More Interactivity to Interactive Charts
Like package 'manipulate' does for static graphics, this package helps to easily add controls like sliders, pickers, checkboxes, etc. that can be used to modify the input data or the parameters of an interactive chart created with package 'htmlwidgets'.
Simulating Longitudinal Data with Causal Inference Applications
A flexible tool for simulating complex longitudinal data using structural equations, with emphasis on problems in causal inference. Specify interventions and simulate from intervened data generating distributions. Define and evaluate treatment-specific means, the average treatment effects and coefficients from working marginal structural models. User interface designed to facilitate the conduct of transparent and reproducible simulation studies, and allows concise expression of complex functional dependencies for a large number of time-varying nodes. See the package vignette for more information, documentation and examples.
Disparity Filter Algorithm for Weighted Networks
The disparity filter algorithm is a network reduction technique to identify the 'backbone' structure of a weighted network without destroying its multi-scale nature. The algorithm is documented in M. Angeles Serrano, Marian Boguna and Alessandro Vespignani in "Extracting the multiscale backbone of complex weighted networks", Proceedings of the National Academy of Sciences 106 (16), 2009. This implementation of the algorithm supports both directed and undirected networks.
Fit and Compare Species-Area Relationship Models Using Multimodel Inference
Implements the basic elements of the multi-model
inference paradigm for up to twenty species-area relationship models (SAR), using simple
R list-objects and functions, as in Triantis et al. 2012
Bayesian Inference for the Multivariate Skew-t Model
Estimates the multivariate skew-t and nested models, as described in the
articles Liseo, B., Parisi, A. (2013). Bayesian inference for the multivariate skew-normal
model: a population Monte Carlo approach. Comput. Statist. Data Anal.