Data published by the United States Federal Energy Regulatory Commission including electric company financial data, natural gas company financial data, hydropower plant data, liquified natural gas plant data, oil company financial data natural gas company financial data, and natural gas storage field data.

The Delphi Epidata API provides real-time access to epidemiological surveillance data for influenza, COVID-19', and other diseases for the USA at various geographical resolutions, both from official government sources such as the Center for Disease Control (CDC) and Google Trends and private partners such as Facebook and Change Healthcare'. It is built and maintained by the Carnegie Mellon University Delphi research group. To cite this API: David C. Farrow, Logan C. Brooks, Aaron Rumack', Ryan J. Tibshirani', Roni Rosenfeld (2015). Delphi Epidata API. <https://github.com/cmu-delphi/delphi-epidata>.

This package implements an explicit exploration strategy for evolutionary algorithms in order to have a more effective search in solving optimization problems. Along with this exploration search strategy, a set of four different Estimation of Distribution Algorithms (EDAs) are also implemented for solving optimization problems in continuous domains. The implemented explicit exploration strategy in this package is described in Salinas-Gutiérrez and Muñoz Zavala (2023) <doi:10.1016/j.asoc.2023.110230>.

This package implements event extraction and early classification of events in data streams in R. It has the functionality to generate 2-dimensional data streams with events belonging to 2 classes. These events can be extracted and features computed. The event features extracted from incomplete-events can be classified using a partial-observations-classifier (Kandanaarachchi et al. 2018) <doi:10.1371/journal.pone.0236331>.

This package provides a collection of advanced tools, methods and models specifically designed for analyzing different types of ecological networks - especially antagonistic (food webs, host-parasite), mutualistic (plant-pollinator, plant-fungus, etc) and competitive networks, as well as their variability in time and space. Statistical models are developed to describe and understand the mechanisms that determine species interactions, and to decipher the organization of these ecological networks (Ohlmann et al. (2019) <doi:10.1111/ele.13221>, Gonzalez et al. (2020) <doi:10.1101/2020.04.02.021691>, Miele et al. (2021) <doi:10.48550/arXiv.2103.10433>, Botella et al (2021) <doi:10.1111/2041-210X.13738>).

Presents a "Scenarios" class containing general parameters, risk parameters and projection results. Risk parameters are gathered together into a ParamsScenarios sub-object. The general process for using this package is to set all needed parameters in a Scenarios object, use the customPathsGeneration method to proceed to the projection, then use xxx_PriceDistribution() methods to get asset prices.

Routines for performing empirical calibration of observational study estimates. By using a set of negative control hypotheses we can estimate the empirical null distribution of a particular observational study setup. This empirical null distribution can be used to compute a calibrated p-value, which reflects the probability of observing an estimated effect size when the null hypothesis is true taking both random and systematic error into account. A similar approach can be used to calibrate confidence intervals, using both negative and positive controls. For more details, see Schuemie et al. (2013) <doi:10.1002/sim.5925> and Schuemie et al. (2018) <doi:10.1073/pnas.1708282114>.

Fits dose-response models using an evolutionary algorithm to estimate the model parameters. The procedure currently can fit 3-parameter, 4-parameter, and 5-parameter log-logistic models as well as exponential models. Functions are also provided to plot, make predictions, and calculate confidence intervals for the resulting models. For details see "Nonlinear Dose-response Modeling of High-Throughput Screening Data Using an Evolutionary Algorithm", Ma, J., Bair, E., Motsinger-Reif, A.; Dose-Response 18(2):1559325820926734 (2020) <doi:10.1177/1559325820926734>.

Runs the eDITH (environmental DNA Integrating Transport and Hydrology) model, which implements a mass balance of environmental DNA (eDNA) transport at a river network scale coupled with a species distribution model to obtain maps of species distribution. eDITH can work with both eDNA concentration (e.g., obtained via quantitative polymerase chain reaction) or metabarcoding (read count) data. Parameter estimation can be performed via Bayesian techniques (via the BayesianTools package) or optimization algorithms. An interface to the DHARMa package for posterior predictive checks is provided. See Carraro and Altermatt (2024) <doi:10.1111/2041-210X.14317> for a package introduction; Carraro et al. (2018) <doi:10.1073/pnas.1813843115> and Carraro et al. (2020) <doi:10.1038/s41467-020-17337-8> for methodological details.

Import physiologic data stored in the European Data Format (EDF and EDF+) into R. Both EDF and EDF+ files are supported. Discontinuous EDF+ files are not yet supported.

This package implements the methods of McGrath et al. (2020) <doi:10.1177/0962280219889080> and Cai et al. (2021) <doi:10.1177/09622802211047348> for estimating the sample mean and standard deviation from commonly reported quantiles in meta-analysis. These methods can be applied to studies that report the sample median, sample size, and one or both of (i) the sample minimum and maximum values and (ii) the first and third quartiles. The corresponding standard error estimators described by McGrath et al. (2023) <doi:10.1177/09622802221139233> are also included.

This package implements the Bayesian and likelihood methods proposed in Imai, Lu, and Strauss (2008 <doi:10.1093/pan/mpm017>) and (2011 <doi:10.18637/jss.v042.i05>) for ecological inference in 2 by 2 tables as well as the method of bounds introduced by Duncan and Davis (1953). The package fits both parametric and nonparametric models using either the Expectation-Maximization algorithms (for likelihood models) or the Markov chain Monte Carlo algorithms (for Bayesian models). For all models, the individual-level data can be directly incorporated into the estimation whenever such data are available. Along with in-sample and out-of-sample predictions, the package also provides a functionality which allows one to quantify the effect of data aggregation on parameter estimation and hypothesis testing under the parametric likelihood models.

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.

Detect outliers in one-dimensional data.

This is a utility for transforming Ecological Metadata Language ('EML') files into JSON-LD and back into EML. Doing so creates a list-based representation of EML in R, so that EML data can easily be manipulated using standard R tools. This makes this package an effective backend for other R'-based tools working with EML. By abstracting away the complexity of XML Schema, developers can build around native R list objects and not have to worry about satisfying many of the additional constraints of set by the schema (such as element ordering, which is handled automatically). Additionally, the JSON-LD representation enables the use of developer-friendly JSON parsing and serialization that may facilitate the use of EML in contexts outside of R, as well as the informatics-friendly serializations such as RDF and SPARQL queries.

Builds contingency tables that cross-tabulate multiple categorical variables and also calculates various summary measures. Export to a variety of formats is supported, including: HTML', LaTeX', and Excel'.

Generates interactive circle plots with the nodes around the circumference and linkages between the connected nodes using hierarchical edge bundling via the D3 JavaScript library. See <http://d3js.org/> for more information on D3.

Likelihood-based approaches to estimate linear regression parameters and treatment effects in the presence of endogeneity. Specifically, this package includes James Heckman's classical simultaneous equation models-the sample selection model for outcome selection bias and hybrid model with structural shift for endogenous treatment. For more information, see the seminal paper of Heckman (1978) <DOI:10.3386/w0177> in which the details of these models are provided. This package accommodates repeated measures on subjects with a working independence approach. The hybrid model further accommodates treatment effect modification.

Analysis and visualization tools for electroencephalography (EEG) data. Includes functions for (i) plotting EEG data, (ii) filtering EEG data, (iii) smoothing EEG data; (iv) frequency domain (Fourier) analysis of EEG data, (v) Independent Component Analysis of EEG data, and (vi) simulating event-related potential EEG data.

This package provides tools for modelling electric vehicle charging sessions into generic groups with similar connection patterns called "user profiles", using Gaussian Mixture Models clustering. The clustering and profiling methodology is described in Cañigueral and Meléndez (2021, ISBN:0142-0615) <doi:10.1016/j.ijepes.2021.107195>.

An implementation for using efficient initials to compute the maximal eigenpair in R. It provides three algorithms to find the efficient initials under two cases: the tridiagonal matrix case and the general matrix case. Besides, it also provides two algorithms for the next to the maximal eigenpair under these two cases.

This package provides functions for the method of effect stars as proposed by Tutz and Schauberger (2013) <doi:10.1080/10618600.2012.701379>. Effect stars can be used to visualize estimates of parameters corresponding to different groups, for example in multinomial logit models. Beside the main function effectstars there exist methods for special objects, for example for vglm objects from the VGAM package.

Implementations of the expected shortfall backtests of Bayer and Dimitriadis (2020) <doi:10.1093/jjfinec/nbaa013> as well as other well known backtests from the literature. Can be used to assess the correctness of forecasts of the expected shortfall risk measure which is e.g. used in the banking and finance industry for quantifying the market risk of investments. A special feature of the backtests of Bayer and Dimitriadis (2020) <doi:10.1093/jjfinec/nbaa013> is that they only require forecasts of the expected shortfall, which is in striking contrast to all other existing backtests, making them particularly attractive for practitioners.

Estimates RxC (R by C) vote transfer matrices (ecological contingency tables) from aggregate data building on Thomsen (1987) and Park (2008) approaches. References: Park, W.-H. (2008). Ecological Inference and Aggregate Analysis of Election''. PhD Dissertation. University of Michigan. <https://deepblue.lib.umich.edu/bitstream/handle/2027.42/58525/wpark_1.pdf> Thomsen, S.R. (1987, ISBN:87-7335-037-2). Danish Elections 1920 79: a Logit Approach to Ecological Analysis and Inference''. Politica, Aarhus, Denmark.