Rcpp Hidden Markov Model

Collection of functions to evaluate sequences, decode hidden states and estimate parameters from a single or multiple sequences of a discrete time Hidden Markov Model. The observed values can be modeled by a multinomial distribution for categorical/labeled emissions, a mixture of Gaussians for continuous data and also a mixture of Poissons for discrete values. It includes functions for random initialization, simulation, backward or forward sequence evaluation, Viterbi or forward-backward decoding and parameter estimation using an Expectation-Maximization approach.

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) , Soberón and Cavner (2015) , and Soberón et al. (2021).

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 and Cappello, C., De Iaco, S., Posa, D., 2020, covatest: an R package for selecting a class of space-time covariance functions .

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) , A. Meier (2018) < https://opendata.uni-halle.de//handle/1981185920/13470> and Y. Tang et al (2023) . It was supported by DFG grants KI 1443/3-1 and KI 1443/3-2.

Port of the 'Scilab' 'n1qn1' Module for Unconstrained BFGS Optimization

Provides 'Scilab' 'n1qn1'. This takes more memory than traditional L-BFGS. The n1qn1 routine is useful since it allows prespecification of a Hessian. If the Hessian is near enough the truth in optimization it can speed up the optimization problem. The algorithm is described in the 'Scilab' optimization documentation located at < https://www.scilab.org/sites/default/files/optimization_in_scilab.pdf>. This version uses manually modified code from 'f2c' to make this a C only binary.

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) , Sandercock et al. (2011) .