Found 43 packages in 0.18 seconds
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)
Ensemble Forecast Verification for Large Data Sets
Set of tools to simplify application of atomic forecast verification metrics for (comparative) verification of ensemble forecasts to large data sets. The forecast metrics are imported from the 'SpecsVerification' package, and additional forecast metrics are provided with this package. Alternatively, new user-defined forecast scores can be implemented using the example scores provided and applied using the functionality of this package.
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)
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.
Temporal Trends in Ecological Niche Models
Computes temporal trends in environmental suitability obtained from ecological niche models, based on a set of species presence point coordinates and predictor variables.