This package provides a collection of functions to perform core tasks within Energy Trading and Risk Management (ETRM). Calculation of maximum smoothness forward price curves for electricity and natural gas contracts with flow delivery, as presented in F. E. Benth, S. Koekebakker, and F. Ollmar (2007) <doi:10.3905/jod.2007.694791> and F. E. Benth, J. S. Benth, and S. Koekebakker (2008) <doi:10.1142/6811>. Portfolio insurance trading strategies for price risk management in the forward market, see F. Black (1976) <doi:10.1016/0304-405X(76)90024-6>, T. Bjork (2009) <https://EconPapers.repec.org/RePEc:oxp:obooks:9780199574742>, F. Black and R. W. Jones (1987) <doi:10.3905/jpm.1987.409131> and H. E. Leland (1980) <http://www.jstor.org/stable/2327419>.

Enhanced False Discovery Rate (EFDR) is a tool to detect anomalies in an image. The image is first transformed into the wavelet domain in order to decorrelate any noise components, following which the coefficients at each resolution are standardised. Statistical tests (in a multiple hypothesis testing setting) are then carried out to find the anomalies. The power of EFDR exceeds that of standard FDR, which would carry out tests on every wavelet coefficient: EFDR choose which wavelets to test based on a criterion described in Shen et al. (2002). The package also provides elementary tools to interpolate spatially irregular data onto a grid of the required size. The work is based on Shen, X., Huang, H.-C., and Cressie, N. Nonparametric hypothesis testing for a spatial signal. Journal of the American Statistical Association 97.460 (2002): 1122-1140.

This package implements a Markov Chain Monte Carlo algorithm to approximate exact conditional inference for logistic regression models. Exact conditional inference is based on the distribution of the sufficient statistics for the parameters of interest given the sufficient statistics for the remaining nuisance parameters. Using model formula notation, users specify a logistic model and model terms of interest for exact inference. See Zamar et al. (2007) <doi:10.18637/jss.v021.i03> for more details.

Simplifies some complicated and labor intensive processes involved in exploring and explaining data. Allows you to quickly and efficiently visualize the interaction between variables and simplifies the process of discovering covariation in your data. Also includes some convenience features designed to remove as much redundant typing as possible.

Reads, writes, and edits EXIF and other file metadata using ExifTool <https://exiftool.org/>, returning read results as a data frame. ExifTool supports many different metadata formats including EXIF, GPS, IPTC, XMP, JFIF, GeoTIFF, ICC Profile, Photoshop IRB, FlashPix, AFCP and ID3, Lyrics3, as well as the maker notes of many digital cameras by Canon, Casio, DJI, FLIR, FujiFilm, GE, GoPro, HP, JVC/Victor, Kodak, Leaf, Minolta/Konica-Minolta, Motorola, Nikon, Nintendo, Olympus/Epson, Panasonic/Leica, Pentax/Asahi, Phase One, Reconyx, Ricoh, Samsung, Sanyo, Sigma/Foveon and Sony.

This package provides functions for estimating catalytic constant and Michaelis-Menten constant for enzyme kinetics model using Metropolis-Hasting algorithm within Gibbs sampler based on the Bayesian framework.

This package implements the hybrid framework for event prediction described in Fang & Zheng (2011, <doi:10.1016/j.cct.2011.05.013>). To estimate the survival function the event prediction is based on, a piecewise exponential hazard function is fit to the time-to-event data to infer the potential change points. Prior to the last identified change point, the survival function is estimated using Kaplan-Meier, and the tail after the change point is fit using piecewise exponential.

Instead of counting observations before and after a subset() call, the ExclusionTable() function reports the number before and after each subset() call together with the number of observations that have been excluded. This is especially useful in observational studies for keeping track how many observations have been excluded for each in-/ or exclusion criteria. You just need to provide ExclusionTable() with a dataset and a list of logical filter statements.

This package provides functions to extract and process data from the FDA Adverse Event Reporting System (FAERS). It facilitates the conversion of raw FAERS data published after 2014Q3 into structured formats for analysis. See Yang et al. (2022) <doi:10.3389/fphar.2021.772768> for related information.

Fits a variety of hidden Markov models, structured in an extended generalized linear model framework. See T. Rolf Turner, Murray A. Cameron, and Peter J. Thomson (1998) <doi:10.2307/3315677>, and Rolf Turner (2008) <doi:10.1016/j.csda.2008.01.029> and the references cited therein.

Application of Ensemble Empirical Mode Decomposition and its variant based Support Vector regression model for univariate time series forecasting. For method details see Das (2020).<http://krishi.icar.gov.in/jspui/handle/123456789/44138>.

Genotyping the population using next generation sequencing data is essentially important for the rare variant detection. In order to distinguish the genomic structural variation from sequencing error, we propose a statistical model which involves the genotype effect through a latent variable to depict the distribution of non-reference allele frequency data among different samples and different genome loci, while decomposing the sequencing error into sample effect and positional effect. An ECM algorithm is implemented to estimate the model parameters, and then the genotypes and SNPs are inferred based on the empirical Bayes method.

Data sets for the chapter "Ensemble Postprocessing with R" of the book Stephane Vannitsem, Daniel S. Wilks, and Jakob W. Messner (2018) "Statistical Postprocessing of Ensemble Forecasts", Elsevier, 362pp. These data sets contain temperature and precipitation ensemble weather forecasts and corresponding observations at Innsbruck/Austria. Additionally, a demo with the full code of the book chapter is provided.

Forecasting time series with different decomposition based ARIMA models. For method details see Yu L, Wang S, Lai KK (2008). <doi:10.1016/j.eneco.2008.05.003>.

Empirical Bayes ranking applicable to parallel-estimation settings where the estimated parameters are asymptotically unbiased and normal, with known standard errors. A mixture normal prior for each parameter is estimated using Empirical Bayes methods, subsequentially ranks for each parameter are simulated from the resulting joint posterior over all parameters (The marginal posterior densities for each parameter are assumed independent). Finally, experiments are ordered by expected posterior rank, although computations minimizing other plausible rank-loss functions are also given.

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.

Gene regulatory network constructed using combined score obtained from individual network inference method. The combined score measures the significance of edges in the ensemble network. Fisher's weighted method has been implemented to combine the outcomes of different methods based on the probability values. The combined score follows chi-square distribution with 2n degrees of freedom. <doi:10.22271/09746315.2020.v16.i3.1358>.

Computes a series of indices commonly used in the fields of economic geography, economic complexity, and evolutionary economics to describe the location, distribution, spatial organization, structure, and complexity of economic activities. Functions include basic spatial indicators such as the location quotient, the Krugman specialization index, the Herfindahl or the Shannon entropy indices but also more advanced functions to compute different forms of normalized relatedness between economic activities or network-based measures of economic complexity. Most of the functions use matrix calculus and are based on bipartite (incidence) matrices consisting of region - industry pairs. These are described in Balland (2017) <http://econ.geo.uu.nl/peeg/peeg1709.pdf>.

This package provides functions for treatment effect estimation, hypothesis testing, and future study design for settings where the surrogate is used in place of the primary outcome for individuals for whom the surrogate is valid, and the primary outcome is purposefully measured in the remaining patients. More details are available in: Knowlton, R., Parast, L. (2024) ``Efficient Testing Using Surrogate Information," Biometrical Journal, 67(6): e70086, <doi:10.1002/bimj.70086>. A tutorial for this package can be found at <https://www.laylaparast.com/etsi>.

We introduced a novel ensemble-based explainable machine learning model using Model Confidence Set (MCS) and two stage Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) algorithm. The model combined the predictive capabilities of different machine-learning models and integrates the interpretability of explainability methods. To develop the proposed algorithm, a two-stage Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) framework was employed. The package has been developed using the algorithm of Paul et al. (2023) <doi:10.1007/s40009-023-01218-x> and Yeasin and Paul (2024) <doi:10.1007/s11227-023-05542-3>.

Embed interactive charts to their Shiny applications. These charts will be generated by ECharts library developed by Baidu (<http://echarts.baidu.com/>). Current version supports line chart, bar chart, pie chart, scatter plot, gauge, word cloud, radar chart, tree map, and heat map.

This extension of the pattern-oriented modeling framework of the poems package provides a collection of modules and functions customized for modeling disease transmission on a population scale in a spatiotemporally explicit manner. This includes seasonal time steps, dispersal functions that track disease state of dispersers, results objects that store disease states, and a population simulator that includes disease dynamics.

This package provides a robust and efficient solution for working with Ethiopian dates. It can seamlessly convert to and from Gregorian dates. It is designed to be compatible with the tidyverse data workflow, including plotting with ggplot2'. It ensures lightning-fast computations by integrating high-performance C++ code through Rcpp package.

Model-based clustering for paired data based on the regression of a mixture of Bayesian hierarchical models on covariates. Zhang et al. (2023) <doi:10.1186/s12859-023-05556-x>.