Found 18 packages in 0.03 seconds
SOM Bound to Realize Euclidean and Relational Outputs
The stochastic (also called on-line) version of the Self-Organising
Map (SOM) algorithm is provided. Different versions of the
algorithm are implemented, for numeric and relational data and for
contingency tables as described, respectively, in Kohonen (2001)
Log-Linear Poisson Graphical Model with Hot-Deck Multiple Imputation
Infer log-linear Poisson Graphical Model with an auxiliary data
set. Hot-deck multiple imputation method is used to improve the reliability
of the inference with an auxiliary dataset. Standard log-linear Poisson
graphical model can also be used for the inference and the Stability
Approach for Regularization Selection (StARS) is implemented to drive the
selection of the regularization parameter. The method is fully described in
Select Intervals Suited for Functional Regression
Interval fusion and selection procedures for regression with
functional inputs. Methods include a semiparametric approach based
on Sliced Inverse Regression (SIR), as described in
Omics Data Integration Using Kernel Methods
Kernel-based methods are powerful methods for integrating
heterogeneous types of data. mixKernel aims at providing methods to combine
kernel for unsupervised exploratory analysis. Different solutions are
provided to compute a meta-kernel, in a consensus way or in a way that
best preserves the original topology of the data. mixKernel also integrates
kernel PCA to visualize similarities between samples in a non linear space
and from the multiple source point of view
Monazite Dating for the NiLeDAM Team
Th-U-Pb electron microprobe age dating of monazite, as originally
described in
Testing Differences Between Families of Trees
Perform test to detect differences in structure between families of trees. The method is based on cophenetic distances and aggregated Student's tests.
Adjacency-Constrained Clustering of a Block-Diagonal Similarity Matrix
Implements a constrained version of hierarchical agglomerative
clustering, in which each observation is associated to a position, and only
adjacent clusters can be merged. Typical application fields in
bioinformatics include Genome-Wide Association Studies or Hi-C data
analysis, where the similarity between items is a decreasing function of
their genomic distance. Taking advantage of this feature, the implemented
algorithm is time and memory efficient. This algorithm is described in
Ambroise et al (2019)
An MCMC Sampler Using the t-Walk Algorithm
Implements the t-walk algorithm, a general-purpose, self-adjusting
Markov Chain Monte Carlo (MCMC) sampler for continuous distributions as
described by Christen & Fox (2010)
Taxonomic Distance and Phylogenetic Lineage Computation
Computes phylogenetic distances between any two taxa using hierarchical lineage data retrieved from The Taxonomicon < http://taxonomicon.taxonomy.nl>, a comprehensive curated classification of all life based on Systema Naturae 2000 (Brands, 1989 < http://taxonomicon.taxonomy.nl>). Given any two taxon names, retrieves their full lineages, identifies the most recent common ancestor (MRCA), and computes a dissimilarity index based on lineage depth. Outputs native dist objects, enabling direct integration with the R statistical ecosystem for hierarchical clustering, principal coordinate analysis (PCoA), and multivariate ecological analyses. Supports individual distance queries, pairwise distance matrices, clade filtering, and lineage utilities.
Download and Read Brazilian Meteorological Data from INMET
Automates the download and processing of historical weather data from the Brazilian National Institute of Meteorology (INMET). It resolves formatting inconsistencies in raw CSV files across different years, removes structural artifacts, standardizes column names, converts timestamps to local Brazilian time zones, and outputs tidy data frames ready for analysis. Data are retrieved from < https://portal.inmet.gov.br/dadoshistoricos>.