Dendrogram seriation: ordering for visualisation
Re-arranges a dendrogram to optimize visualisation-based
cost functions

Re-arranges a dendrogram to optimize visualisation-based cost functions

Dendrogram Seriation: Ordering for Visualisation
Re-arranges a dendrogram to optimize visualisation-based cost functions.

A Set of Tools for Calculating Spatial Synchrony Between
Tree-Ring Chronologies
Josu G. Alday, Tatiana A. Shestakova, Victor Resco de Dios, Jordi Voltas
Provides functions for the calculation and plotting of synchrony in
tree growth from tree-ring width chronologies (TRW index). It combines
variance-covariance (VCOV) mixed modelling with functions that quantify
the degree to which the TRW chronologies contain a common temporal
signal. It also implements temporal trends in spatial synchrony using a
moving window. These methods can also be used with other kind of ecological
variables that have temporal autocorrelation corrected.

Josu G. Alday (https://orcid.org/0000-0001-7510-8655),
Tatiana A. Shestakova,
Victor Resco de Dios (https://orcid.org/0000-0002-5721-1656),
Jordi Voltas (https://orcid.org/0000-0003-4051-1158)

Parameter Estimates and Real-Time Prediction of a Single Dengue
Outbreak
Carlos Sebrango, Lizet Sanchez, Ziv Shkedy, Ewoud De Troyer
Provides functions for parameter estimation and real-time predictions  of a single dengue
outbreak taking into account model uncertainty using  model averaging.

Dens-Based Method for Partial Correlation Estimation in Large
Scale Brain Networks
Yikai Wang, Jian Kang, Phebe Brenne Kemmer and Ying Guo
Provide a Dens-based method for estimating functional connection in large scale brain networks using partial correlation.

Density.T.HoldOut: Non-combinatorial T-estimation Hold-Out for
density estimation
Nelo Magalhães (Univ. Paris Sud 11 - INRIA team Select) and Yves Rozenholc (Univ. Paris Descartes - INRIA team Select)
Implementation in the density framework of the non-combinatorial algorithm and its greedy version, introduced by Magalhães and Rozenholc (2014), for T-estimation Hold-Out proposed in Birgé (2006, Section 9). The package provide an implementation which uses several families of estimators (regular and irregular histograms, kernel estimators) which may be used alone or combined. As a complement, provides also a comparison with other Held-Out derived from least-squares and maximum-likelihood.