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Carbon-Related Assessment of Silvicultural Concepts
A simulation model and accompanying functions that support assessing silvicultural concepts on the forest estate level with a focus on the CO2 uptake by wood growth and CO2 emissions by forest operations. For achieving this, a virtual forest estate area is split into the areas covered by typical phases of the silvicultural concept of interest. Given initial area shares of these phases, the dynamics of these areas is simulated. The typical carbon stocks and flows which are known for all phases are attributed post-hoc to the areas and upscaled to the estate level. CO2 emissions by forest operations are estimated based on the amounts and dimensions of the harvested timber. Probabilities of damage events are taken into account.
Incidence Estimation Tools
Tools for estimating incidence from biomarker data in cross-
sectional surveys, and for calibrating tests for recent infection.
Implements and extends the method of Kassanjee et al. (2012)
Multistage Sampling Allocation and Sample Selection
Multivariate optimal allocation for different domains in one and two stages stratified sample design. 'R2BEAT' extends the Neyman (1934) – Tschuprow (1923) allocation method to the case of several variables, adopting a generalization of the Bethel’s proposal (1989). 'R2BEAT' develops this methodology but, moreover, it allows to determine the sample allocation in the multivariate and multi-domains case of estimates for two-stage stratified samples. It also allows to perform both Primary Stage Units and Secondary Stage Units selection. This package requires the availability of 'ReGenesees', that can be installed from < https://github.com/DiegoZardetto/ReGenesees>.
Discriminant Non-Negative Matrix Factorization
Discriminant Non-Negative Matrix Factorization aims to extend the Non-negative Matrix Factorization algorithm in order to extract features that enforce not only the spatial locality, but also the separability between classes in a discriminant manner. It refers to three article, Zafeiriou, Stefanos, et al. "Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification." Neural Networks, IEEE Transactions on 17.3 (2006): 683-695. Kim, Bo-Kyeong, and Soo-Young Lee. "Spectral Feature Extraction Using dNMF for Emotion Recognition in Vowel Sounds." Neural Information Processing. Springer Berlin Heidelberg, 2013. and Lee, Soo-Young, Hyun-Ah Song, and Shun-ichi Amari. "A new discriminant NMF algorithm and its application to the extraction of subtle emotional differences in speech." Cognitive neurodynamics 6.6 (2012): 525-535.
Article Formats for R Markdown
A suite of custom R Markdown formats and templates for authoring journal articles and conference submissions.
k-Nearest Neighbor Mutual Information Estimator
This is a 'C++' mutual information (MI) library based on the k-nearest
neighbor (KNN) algorithm. There are three functions provided for computing MI
for continuous values, mixed continuous and discrete values, and conditional MI
for continuous values. They are based on algorithms by A. Kraskov, et. al. (2004)
Parameter Estimation for the Averaging Model of Information Integration Theory
Implementation of the R-Average method for parameter estimation of averaging models of the Anderson's Information Integration Theory by Vidotto, G., Massidda, D., & Noventa, S. (2010) < https://www.uv.es/psicologica/articulos3FM.10/3Vidotto.pdf>.
Physics-Informed Spatial and Functional Data Analysis
An implementation of regression models with partial differential regularizations, making use of the Finite Element Method. The models efficiently handle data distributed over irregularly shaped domains and can comply with various conditions at the boundaries of the domain. A priori information about the spatial structure of the phenomenon under study can be incorporated in the model via the differential regularization. See Sangalli, L. M. (2021)
Life History Metrics from Matrix Population Models
Functions for calculating life history metrics using matrix
population models ('MPMs'). Described in Jones et al. (2021)