Forecasting univariate time series with Variational Mode Decomposition (VMD) based time delay neural network models.For method details see Konstantin, D.and Dominique, Z. (2014). <doi:10.1109/TSP.2013.2288675>.

This package implements the novel testing approach by Janitza et al.(2015) <http://nbn-resolving.de/urn/resolver.pl?urn=nbn:de:bvb:19-epub-25587-4> for the permutation variable importance measure in a random forest and the PIMP-algorithm by Altmann et al.(2010) <doi:10.1093/bioinformatics/btq134>. Janitza et al.(2015) <http://nbn-resolving.de/urn/resolver.pl?urn=nbn:de:bvb:19-epub-25587-4> do not use the "standard" permutation variable importance but the cross-validated permutation variable importance for the novel test approach. The cross-validated permutation variable importance is not based on the out-of-bag observations but uses a similar strategy which is inspired by the cross-validation procedure. The novel test approach can be applied for classification trees as well as for regression trees. However, the use of the novel testing approach has not been tested for regression trees so far, so this routine is meant for the expert user only and its current state is rather experimental.

In order to make it easy to use variance reduction algorithms for any simulation, this framework can help you. We propose user friendly and easy to extend framework. Antithetic Variates, Inner Control Variates, Outer Control Variates and Importance Sampling algorithms are available in the framework. User can write its own simulation function and use the Variance Reduction techniques in this package to obtain more efficient simulations. An implementation of Asian Option simulation is already available within the package. See Kemal Dinçer Dingeç & Wolfgang Hörmann (2012) <doi:10.1016/j.ejor.2012.03.046>.

This is a package for creating and running Agent Based Models (ABM). It provides a set of base classes with core functionality to allow bootstrapped models. For more intensive modeling, the supplied classes can be extended to fit researcher needs.

Interactive variogram diagnostics.

This package provides templates and functions to simplify the production and maintenance of curriculum vitae.

Historical results for the state of Virginia lottery draw games. Data were downloaded from https://www.valottery.com/.

Variance function estimation for models proposed by W. Sadler in his variance function program ('VFP', www.aacb.asn.au/AACB/Resources/Variance-Function-Program). Here, the idea is to fit multiple variance functions to a data set and consequently assess which function reflects the relationship Var ~ Mean best. For in-vitro diagnostic ('IVD') assays modeling this relationship is of great importance when individual test-results are used for defining follow-up treatment of patients.

This package provides a suite of analytical functionalities to process and analyze visual meteor observations from the Visual Meteor Database of the International Meteor Organization <https://www.imo.net/>.

This package provides a tool for calculating and drawing "variable trees". Variable trees display information about nested subsets of a data frame. <doi:10.18637/jss.v114.i04>.

Static and dynamic 3D plots to be used with ordination results and in diversity analysis, especially with the vegan package.

Generates interactive plots for analysing and visualising three-class high dimensional data. It is particularly suited to visualising differences in continuous attributes such as gene/protein/biomarker expression levels between three groups. Differential gene/biomarker expression analysis between two classes is typically shown as a volcano plot. However, with three groups this type of visualisation is particularly difficult to interpret. This package generates 3D volcano plots and 3-way polar plots for easier interpretation of three-class data.

Simulates and evaluates stochastic scenarios of death and lapse events in life reinsurance contracts with profit commissions. The methodology builds on materials published by the Institute of Actuaries of Japan <https://www.actuaries.jp/examin/textbook/pdf/modeling.pdf>. A paper describing the detailed algorithms will be published by the author within a few months after the initial release of this package.

This package contains functions for visualization univariate data: ccdplot and qddplot.

Visualize and compute percentiles/probabilities of normal, t, f, chi square and binomial distributions.

Calculate point estimates of and valid confidence intervals for nonparametric, algorithm-agnostic variable importance measures in high and low dimensions, using flexible estimators of the underlying regression functions. For more information about the methods, please see Williamson et al. (Biometrics, 2020), Williamson et al. (JASA, 2021), and Williamson and Feng (ICML, 2020).

This package implements D-vine quantile regression models with parametric or nonparametric pair-copulas. See Kraus and Czado (2017) <doi:10.1016/j.csda.2016.12.009> and Schallhorn et al. (2017) <doi:10.48550/arXiv.1705.08310>.

This package provides retail and wholesale vegetable price data from two major market hubs in Sri Lanka, Dambulla and Pettah. Includes tools for analyzing, visualizing, and comparing vegetable prices across markets.

Under a different representation of the multivariate normal (MVN) probability, we can use the Vecchia approximation to sample the integrand at a linear complexity with respect to n. Additionally, both the SOV algorithm from Genz (92) and the exponential-tilting method from Botev (2017) can be adapted to linear complexity. The reference for the method implemented in this package is Jian Cao and Matthias Katzfuss (2024) "Linear-Cost Vecchia Approximation of Multivariate Normal Probabilities" <doi:10.48550/arXiv.2311.09426>. Two major references for the development of our method are Alan Genz (1992) "Numerical Computation of Multivariate Normal Probabilities" <doi:10.1080/10618600.1992.10477010> and Z. I. Botev (2017) "The Normal Law Under Linear Restrictions: Simulation and Estimation via Minimax Tilting" <doi:10.48550/arXiv.1603.04166>.

The Bank of Canada updated their Valet API <https://www.bankofcanada.ca/valet/docs>, and no R client currently exists. This provides access to all of Valet's endpoints and serves responses in wide format easy for researchers to handle but also provides tools to access API responses as a list.

This package provides tools for reporting and forecasting viral respiratory infections, using case surveillance data. Report generation tools for short-term forecasts, and validation metrics for an arbitrary number of customizable respiratory viruses. Estimation of the effective reproduction number is based on the EpiEstim framework described in work by Cori and colleagues. (2013) <doi:10.1093/aje/kwt133>.

This package creates Vertex Similarity matrix of an undirected graph based on the method stated by E. A. Leicht, Petter Holme, AND M. E. J. Newman in their paper <DOI:10.1103/PhysRevE.73.026120>.

This package provides an easy to calculate local variable importance measure based on Ceteris Paribus profile and global variable importance measure based on Partial Dependence Profiles.

The base tools union() intersect(), etc., follow the algebraic definition that each element of a set must be unique. Since it's often helpful to compare all elements of two vectors, this toolset treats every element as unique for counting purposes. For ease of use, all functions in vecsets have an argument multiple which, when set to FALSE, reverts them to the base::sets (alias for all the items) tools functionality.