Estimates a variety of Dynamic Conditional Correlation (DCC) models. More in detail, the dccmidas package allows the estimation of the corrected DCC (cDCC) of Aielli (2013) <doi:10.1080/07350015.2013.771027>, the DCC-MIDAS of Colacito et al. (2011) <doi:10.1016/j.jeconom.2011.02.013>, the Asymmetric DCC of Cappiello et al. <doi:10.1093/jjfinec/nbl005>, and the Dynamic Equicorrelation (DECO) of Engle and Kelly (2012) <doi:10.1080/07350015.2011.652048>. dccmidas offers the possibility of including standard GARCH <doi:10.1016/0304-4076(86)90063-1>, GARCH-MIDAS <doi:10.1162/REST_a_00300> and Double Asymmetric GARCH-MIDAS <doi:10.1016/j.econmod.2018.07.025> models in the univariate estimation. Moreover, also the scalar and diagonal BEKK <doi:10.1017/S0266466600009063> models can be estimated. Finally, the package calculates also the var-cov matrix under two non-parametric models: the Moving Covariance and the RiskMetrics specifications.

Metabarcoding analysis using the DBTC package is implemented here using shiny in an interactive graphical user interface to conduct Metabarcode analyses and visualize and filter results.

Perform tree-ring analyses such as detrending, chronology building, and cross dating. Read and write standard file formats used in dendrochronology.

Comparison of the accuracy of two binary diagnostic tests in a "paired" study design, i.e. when each test is applied to each subject in the study.

Various functions to import, verify, process and plot high-resolution dendrometer data using daily and stem-cycle approaches as described in Deslauriers et al, 2007 <doi:10.1016/j.dendro.2007.05.003>. For more details about the package please see: Van der Maaten et al. 2016 <doi:10.1016/j.dendro.2016.06.001>.

This package provides a system for combining two diagnostic tests using various approaches that include statistical and machine-learning-based methodologies. These approaches are divided into four groups: linear combination methods, non-linear combination methods, mathematical operators, and machine learning algorithms. See the <https://biotools.erciyes.edu.tr/dtComb/> website for more information, documentation, and examples.

This package provides new types of omnibus tests which are generally much more powerful than traditional tests (including the Kolmogorov-Smirnov, Cramer-von Mises and Anderson-Darling tests),see Zhang (2002) <doi:10.1111/1467-9868.00337>.

The load estimation method is based on a general factor model to solve the estimates of load and specific variance. The philosophy of the package is described in Guangbao Guo. (2022). <doi:10.1007/s00180-022-01270-z>.

Fit a mixture of Discrete Laplace distributions using plain numerical optimisation. This package has similar applications as the disclapmix package that uses an EM algorithm.

Draws stylized choropleth maps -- hexagonal maps and triangular multiclass hex maps -- for New Zealand District Health Boards and Regional Council areas. These allow faceted, coloured displays of quantitative information for comparison across District Health Boards or Regional Councils. The preprint Lumley (2019) <arXiv:1912.04435> is based on the methods in this package.

Statistical hypothesis testing using the Delta method as proposed by Deng et al. (2018) <doi:10.1145/3219819.3219919>. This method replaces the standard variance estimation formula in the Z-test with an approximate formula derived via the Delta method, which can account for within-user correlation.

Joint DNA-based disaster victim identification (DVI), as described in Vigeland and Egeland (2021) <doi:10.21203/rs.3.rs-296414/v1>. Identification is performed by optimising the joint likelihood of all victim samples and reference individuals. Individual identification probabilities, conditional on all available information, are derived from the joint solution in the form of posterior pairing probabilities. dvir is part of the pedsuite collection of packages for pedigree analysis.

Local linear hazard estimator and its multiplicatively bias correction, including three bandwidth selection methods: best one-sided cross-validation, double one-sided cross-validation, and standard cross-validation.

Create disposable R packages for testing. You can create, install and load multiple R packages with a single function call, and then unload, uninstall and destroy them with another function call. This is handy when testing how some R code or an R package behaves with respect to other packages.

This package contains the support functions for the Time Series Analysis book. We present a function to calculate MSE and MAE for inputs of actual and forecast values. We also have the code for disaggregation as found in Wei and Stram (1990, <doi:10.1111/j.2517-6161.1990.tb01799.x>), and Hodgess and Wei (1996, "Temporal Disaggregation of Time Series").

Various methods for the identification of trend and seasonal components in time series (TS) are provided. Among them is a data-driven locally weighted regression approach with automatically selected bandwidth for equidistant short-memory time series. The approach is a combination / extension of the algorithms by Feng (2013) <doi:10.1080/02664763.2012.740626> and Feng, Y., Gries, T., and Fritz, M. (2020) <doi:10.1080/10485252.2020.1759598> and a brief description of this new method is provided in the package documentation. Furthermore, the package allows its users to apply the base model of the Berlin procedure, version 4.1, as described in Speth (2004) <https://www.destatis.de/DE/Methoden/Saisonbereinigung/BV41-methodenbericht-Heft3_2004.pdf?__blob=publicationFile>. Permission to include this procedure was kindly provided by the Federal Statistical Office of Germany.

Define a spatial Area of Interest (AOI) around a constructed dam using hydrology data. Dams have environmental and social impacts, both positive and negative. Current analyses of dams have no consistent way to specify at what spatial extent we should evaluate these impacts. damAOI implements methods to adjust reservoir polygons to match satellite-observed surface water areas, plot upstream and downstream rivers using elevation data and accumulated river flow, and draw buffers clipped by river basins around reservoirs and relevant rivers. This helps to consistently determine the areas which could be impacted by dam construction, facilitating comparative analysis and informed infrastructure investments.

Three global value chain (GVC) decompositions are implemented. The Leontief decomposition derives the value added origin of exports by country and industry as in Hummels, Ishii and Yi (2001). The Koopman, Wang and Wei (2014) decomposition splits country-level exports into 9 value added components, and the Wang, Wei and Zhu (2013) decomposition splits bilateral exports into 16 value added components. Various GVC indicators based on these decompositions are computed in the complimentary gvc package. --- References: --- Hummels, D., Ishii, J., & Yi, K. M. (2001). The nature and growth of vertical specialization in world trade. Journal of international Economics, 54(1), 75-96. Koopman, R., Wang, Z., & Wei, S. J. (2014). Tracing value-added and double counting in gross exports. American Economic Review, 104(2), 459-94. Wang, Z., Wei, S. J., & Zhu, K. (2013). Quantifying international production sharing at the bilateral and sector levels (No. w19677). National Bureau of Economic Research.

Designed for network analysis, leveraging the personalized PageRank algorithm to calculate node scores in a given graph. This innovative approach allows users to uncover the importance of nodes based on a customized perspective, making it particularly useful in fields like bioinformatics, social network analysis, and more.

Implement some deep learning architectures and neural network algorithms, including BP,RBM,DBN,Deep autoencoder and so on.

Bayesian inference algorithms based on the population-based "differential evolution" (DE) algorithm. Users can obtain posterior mode (MAP) estimates via DEMAP, posterior samples via DEMCMC, and variational approximations via DEVI.

High-frequency time-series support via nanotime and data.table'.

An implementation of major general-purpose mechanisms for privatizing statistics, models, and machine learners, within the framework of differential privacy of Dwork et al. (2006) <doi:10.1007/11681878_14>. Example mechanisms include the Laplace mechanism for releasing numeric aggregates, and the exponential mechanism for releasing set elements. A sensitivity sampler (Rubinstein & Alda, 2017) <arXiv:1706.02562> permits sampling target non-private function sensitivity; combined with the generic mechanisms, it permits turn-key privatization of arbitrary programs.

An integrated toolset for the analysis of de novo (sporadic) genetic sequence variants. denovolyzeR implements a mutational model that estimates the probability of a de novo genetic variant arising in each human gene, from which one can infer the expected number of de novo variants in a given population size. Observed variant frequencies can then be compared against expectation in a Poisson framework. denovolyzeR provides a suite of functions to implement these analyses for the interpretation of de novo variation in human disease.