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Reading and Writing Open Data Format Files
The Open Data Format (ODF) is a new, non-proprietary, multilingual, metadata enriched, and zip-compressed data format with metadata structured in the Data Documentation Initiative (DDI) Codebook standard. This package allows reading and writing of data files in the Open Data Format (ODF) in R, and displaying metadata in different languages. For further information on the Open Data Format, see < https://opendataformat.github.io/>.
Tools for Biological Survey Planning
A collection of tools that allows users to plan systems of sampling
sites, increasing the efficiency of biodiversity monitoring by considering
the relationship between environmental and geographic conditions in a
region. The options for selecting sampling sites included here differ from
other implementations in that they consider the environmental and geographic
conditions of a region to suggest sampling sites that could increase the
efficiency of efforts dedicated to monitoring biodiversity. The methods
proposed here are new in the sense that they combine various criteria and
points previously made in related literature; some of the theoretical and
methodological bases considered are described in:
Arita et al. (2011)
Tests on Properties of Space-Time Covariance Functions
Tests on properties of space-time covariance functions.
Tests on symmetry, separability and for assessing
different forms of non-separability are available. Moreover tests on
some classes of covariance functions, such that the classes of
product-sum models, Gneiting models and integrated product models have
been provided. It is the companion R package to the papers of
Cappello, C., De Iaco, S., Posa, D., 2018, Testing the type of non-separability
and some classes of space-time covariance function models
Conditional Autoregressive LASSO
Algorithms to fit Bayesian Conditional Autoregressive LASSO with automatic and adaptive shrinkage described in Shen and Solis-Lemus (2020)
Bayesian Spectral Inference for Time Series
Implementations of Bayesian parametric, nonparametric and semiparametric procedures for univariate and multivariate time series. The package is based on the methods presented in C. Kirch et al (2018)
Integration Network
It constructs a Consensus Network which identifies the general information of all the layers and Specific Networks for each layer with the information present only in that layer and not in all the others.The method is described in Policastro et al. (2024) "INet for network integration"
Statistical Comparison of Two Networks Based on Several Invariance Measures
This permutation based hypothesis test, suited for several types of data
supported by the estimateNetwork function of the bootnet package (Epskamp & Fried, 2018),
assesses the difference between two networks based on several invariance measures (network
structure invariance, global strength invariance, edge invariance, several centrality
measures, etc.). Network structures are estimated with l1-regularization. The Network
Comparison Test is suited for comparison of independent (e.g., two different groups) and
dependent samples (e.g., one group that is measured twice). See van Borkulo et al. (2021),
available from
Rapid Easy Synthesis to Inform Data Extraction
Developed to assist researchers with planning analysis,
prior to obtaining data from Trusted Research Environments (TREs) also known as safe havens.
With functionality to export and import marginal distributions as well as synthesise data, both with
and without correlations from these marginal distributions. Using a multivariate cumulative distribution (COPULA).
Additionally the International Stroke Trial (IST) is included as an example dataset under ODC-By licence
Sandercock et al. (2011)
Graphical Models Estimation from Multiple Sources
Estimates networks of conditional dependencies (Gaussian graphical models) from multiple classes of data (similar but not exactly, i.e. measurements on different equipment, in different locations or for various sub-types). Package also allows to generate simulation data and evaluate the performance. Implementation of the method described in Angelini, De Canditiis and Plaksienko (2022)
Pre-Process, Visualize and Analyse Spectral Data
Infrared, near-infrared and Raman spectroscopic data measured during chemical reactions, provide structural fingerprints by which molecules can be identified and quantified. The application of these spectroscopic techniques as inline process analytical tools (PAT), provides the pharmaceutical and chemical industry with novel tools, allowing to monitor their chemical processes, resulting in a better process understanding through insight in reaction rates, mechanistics, stability, etc. Data can be read into R via the generic spc-format, which is generally supported by spectrometer vendor software. Versatile pre-processing functions are available to perform baseline correction by linking to the 'baseline' package; noise reduction via the 'signal' package; as well as time alignment, normalization, differentiation, integration and interpolation. Implementation based on the S4 object system allows storing a pre-processing pipeline as part of a spectral data object, and easily transferring it to other datasets. Interactive plotting tools are provided based on the 'plotly' package. Non-negative matrix factorization (NMF) has been implemented to perform multivariate analyses on individual spectral datasets or on multiple datasets at once. NMF provides a parts-based representation of the spectral data in terms of spectral signatures of the chemical compounds and their relative proportions. See 'hNMF'-package for references on available methods. The functionality to read in spc-files was adapted from the 'hyperSpec' package.