Found 33 packages in 0.02 seconds
Unimodal Penalized Spline Regression using B-Splines
Univariate spline regression. It is possible to add the shape constraint of unimodality and predefined or self-defined penalties on the B-spline coefficients.
Colocalisation Tests of Two Genetic Traits
Performs the colocalisation tests described in
Giambartolomei et al (2013)
MCMC, Particle Filtering, and Programmable Hierarchical Modeling
A system for writing hierarchical statistical models largely compatible with 'BUGS' and 'JAGS', writing nimbleFunctions to operate models and do basic R-style math, and compiling both models and nimbleFunctions via custom-generated C++. 'NIMBLE' includes default methods for MCMC, Laplace Approximation, Monte Carlo Expectation Maximization, and some other tools. The nimbleFunction system makes it easy to do things like implement new MCMC samplers from R, customize the assignment of samplers to different parts of a model from R, and compile the new samplers automatically via C++ alongside the samplers 'NIMBLE' provides. 'NIMBLE' extends the 'BUGS'/'JAGS' language by making it extensible: New distributions and functions can be added, including as calls to external compiled code. Although most people think of MCMC as the main goal of the 'BUGS'/'JAGS' language for writing models, one can use 'NIMBLE' for writing arbitrary other kinds of model-generic algorithms as well. A full User Manual is available at < https://r-nimble.org>.
Document Unit Tests Roxygen-Style
Much as 'roxygen2' allows one to document functions in the same file as the function itself, 'roxut' allows one to write the unit tests in the same file as the function. Once processed, the unit tests are moved to the appropriate directory. Currently supports 'testthat' and 'tinytest' frameworks. The 'roxygen2' package provides much of the infrastructure.
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
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.
Fitting Ising Models Using the ELasso Method
This network estimation procedure eLasso, which is based on the Ising model, combines l1-regularized logistic regression with model selection based on the Extended Bayesian Information Criterion (EBIC). EBIC is a fit measure that identifies relevant relationships between variables. The resulting network consists of variables as nodes and relevant relationships as edges. Can deal with binary data.
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)
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)
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)