Shiny interfaces and graphical functions for multivariate analysis results exploration.

This comprehensive toolkit for Distributed Elliptical model is designated as "ELIC" (The LIC for Distributed Elliptical Model Analysis) analysis. It is predicated on the assumption that the error term adheres to a Elliptical distribution. The philosophy of the package is described in Guo G. (2020) <doi:10.1080/02664763.2022.2053949>.

The remit of the European Clinical Trials Data Base (EudraCT <https://eudract.ema.europa.eu/> ), or ClinicalTrials.gov <https://clinicaltrials.gov/>, is to provide open access to summaries of all registered clinical trial results; thus aiming to prevent non-reporting of negative results and provide open-access to results to inform future research. The amount of information required and the format of the results, however, imposes a large extra workload at the end of studies on clinical trial units. In particular, the adverse-event-reporting component requires entering: each unique combination of treatment group and safety event; for every such event above, a further 4 pieces of information (body system, number of occurrences, number of subjects, number exposed) for non-serious events, plus an extra three pieces of data for serious adverse events (numbers of causally related events, deaths, causally related deaths). This package prepares the required statistics needed by EudraCT and formats them into the precise requirements to directly upload an XML file into the web portal, with no further data entry by hand.

Extends the Changes-in-Changes model a la Athey and Imbens (2006) <doi:10.1111/j.1468-0262.2006.00668.x> to multiple cohorts and time periods, which generalizes difference-in-differences estimation techniques to the entire distribution. Computes quantile treatment effects for every possible two-by-two combination in ecic(). Then, aggregating all bootstrap runs adds the standard errors in summary_ecic(). Results can be plotted with plot_ecic() aggregated over all cohort-group combinations or in an event-study style for either individual periods or individual quantiles.

Evaluates the performance of binary classifiers. Computes confusion measures (TP, TN, FP, FN), derived measures (TPR, FDR, accuracy, F1, DOR, ..), and area under the curve. Outputs are well suited for nested dataframes.

Evidential regression analysis for dichotomous and quantitative outcome data. The following references described the methods in this package: Strug, L. J., Hodge, S. E., Chiang, T., Pal, D. K., Corey, P. N., & Rohde, C. (2010) <doi:10.1038/ejhg.2010.47>. Strug, L. J., & Hodge, S. E. (2006) <doi:10.1159/000094709>. Royall, R. (1997) <ISBN:0-412-04411-0>.

Empirical Bayes thresholding using the methods developed by I. M. Johnstone and B. W. Silverman. The basic problem is to estimate a mean vector given a vector of observations of the mean vector plus white noise, taking advantage of possible sparsity in the mean vector. Within a Bayesian formulation, the elements of the mean vector are modelled as having, independently, a distribution that is a mixture of an atom of probability at zero and a suitable heavy-tailed distribution. The mixing parameter can be estimated by a marginal maximum likelihood approach. This leads to an adaptive thresholding approach on the original data. Extensions of the basic method, in particular to wavelet thresholding, are also implemented within the package.

Fits engression models for nonlinear distributional regression. Predictors and targets can be univariate or multivariate. Functionality includes estimation of conditional mean, estimation of conditional quantiles, or sampling from the fitted distribution. Training is done full-batch on CPU (the python version offers GPU-accelerated stochastic gradient descent). Based on "Engression: Extrapolation through the lens of distributional regression" by Xinwei Shen and Nicolai Meinshausen (2024) in JRSSB. Also supports classification (experimental). <doi:10.1093/jrsssb/qkae108>.

The 2-D spatial and temporal Epidemic Type Aftershock Sequence ('ETAS') Model is widely used to decluster earthquake data catalogs. Usually, the calculation of standard errors of the ETAS model parameter estimates is based on the Hessian matrix derived from the log-likelihood function of the fitted model. However, when an ETAS model is fitted to a local data set over a time period that is limited or short, the standard errors based on the Hessian matrix may be inaccurate. It follows that the asymptotic confidence intervals for parameters may not always be reliable. As an alternative, this package allows for the construction of bootstrap confidence intervals based on empirical quantiles for the parameters of the 2-D spatial and temporal ETAS model. This version improves on Version 0.1.0 of the package by enabling the study space window (renamed study region') to be polygonal rather than merely rectangular. A Japan earthquake data catalog is used in a second example to illustrate this new feature.

This package provides a system for batch-marking data analysis to estimate survival probabilities, capture probabilities, and enumerate the population abundance for both marked and unmarked individuals. The estimation of only marked individuals can be achieved through the batchMarkOptim() function. Similarly, the combined marked and unmarked can be achieved through the batchMarkUnmarkOptim() function. The algorithm was also implemented for the hidden Markov model encapsulated in batchMarkUnmarkOptim() to estimate the abundance of both marked and unmarked individuals in the population. The package is based on the paper: "Hidden Markov Models for Extended Batch Data" of Cowen et al. (2017) <doi:10.1111/biom.12701>.

Multivariate modeling of data after deflation of interfering effects. EF Mosleth et al. (2021) <doi:10.1038/s41598-021-82388-w> and EF Mosleth et al. (2020) <doi:10.1016/B978-0-12-409547-2.14882-6>.

Provide the EMU Speech Database Management System (EMU-SDMS) with database management, data extraction, data preparation and data visualization facilities. See <https://ips-lmu.github.io/The-EMU-SDMS-Manual/> for more details.

Offers a set of functions to easily download and clean Brazilian electoral data from the Superior Electoral Court and CepespData websites. Among other features, the package retrieves data on local and federal elections for all positions (city councilor, mayor, state deputy, federal deputy, governor, and president) aggregated by state, city, and electoral zones.

Training and predict functions for Single Hidden-layer Feedforward Neural Networks (SLFN) using the Extreme Learning Machine (ELM) algorithm. The ELM algorithm differs from the traditional gradient-based algorithms for very short training times (it doesn't need any iterative tuning, this makes learning time very fast) and there is no need to set any other parameters like learning rate, momentum, epochs, etc. This is a reimplementation of the elmNN package using RcppArmadillo after the elmNN package was archived. For more information, see "Extreme learning machine: Theory and applications" by Guang-Bin Huang, Qin-Yu Zhu, Chee-Kheong Siew (2006), Elsevier B.V, <doi:10.1016/j.neucom.2005.12.126>.

The production of certified reference materials (CRMs) requires various statistical tests depending on the task and recorded data to ensure that reported values of CRMs are appropriate. Often these tests are performed according to the procedures described in ISO GUIDE 35:2017'. The eCerto package contains a Shiny app which provides functionality to load, process, report and backup data recorded during CRM production and facilitates following the recommended procedures. It is described in Lisec et al (2023) <doi:10.1007/s00216-023-05099-3> and can also be accessed online <https://apps.bam.de/shn00/eCerto/> without package installation.

Experiences studies are an integral component of the actuarial control cycle. Regardless of the decrement or policyholder behavior of interest, the analyses conducted is often the same. Ultimately, this package aims to reduce time spent writing the same code used for different experience studies, therefore increasing the time for to uncover new insights inherit within the relevant experience.

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>.

Runs an evolutionary algorithm using the AlphaSimR machinery <doi:10.1093/g3journal/jkaa017> .

Exploring time series for signal detection. It is specifically designed to detect possible outbreaks using infectious disease surveillance data at the European Union / European Economic Area or country level. Automatic detection tools used are presented in the paper "Monitoring count time series in R: aberration detection in public health surveillance", by Salmon (2016) <doi:10.18637/jss.v070.i10>. The package includes: - Signal Detection tool, an interactive shiny application in which the user can import external data and perform basic signal detection analyses; - An automated report in HTML format, presenting the results of the time series analysis in tables and graphs. This report can also be stratified by population characteristics (see Population variable). This project was funded by the European Centre for Disease Prevention and Control.

In the USA, companies file different forms with the U.S. Securities and Exchange Commission (SEC) through EDGAR (Electronic Data Gathering, Analysis, and Retrieval system). The EDGAR database automated system collects all the different necessary filings and makes it publicly available. This package facilitates retrieving, storing, searching, and parsing of all the available filings on the EDGAR server. It downloads filings from SEC server in bulk with a single query. Additionally, it provides various useful functions: extracts 8-K triggering events, extract "Business (Item 1)" and "Management's Discussion and Analysis(Item 7)" sections of annual statements, searches filings for desired keywords, provides sentiment measures, parses filing header information, and provides HTML view of SEC filings.

Create list comprehensions (and other types of comprehension) similar to those in python', haskell', and other languages. List comprehension in R converts a regular for() loop into a vectorized lapply() function. Support for looping with multiple variables, parallelization, and across non-standard objects included. Package also contains a variety of functions to help with list comprehension.

This package provides functions that support estimating, assessing and mapping regional disaggregated indicators. So far, estimation methods comprise direct estimation, the model-based unit-level approach Empirical Best Prediction (see "Small area estimation of poverty indicators" by Molina and Rao (2010) <doi:10.1002/cjs.10051>), the area-level model (see "Estimates of income for small places: An application of James-Stein procedures to Census Data" by Fay and Herriot (1979) <doi:10.1080/01621459.1979.10482505>) and various extensions of it (adjusted variance estimation methods, log and arcsin transformation, spatial, robust and measurement error models), as well as their precision estimates. The assessment of the used model is supported by a summary and diagnostic plots. For a suitable presentation of estimates, map plots can be easily created. Furthermore, results can easily be exported to excel. For a detailed description of the package and the methods used see "The R Package emdi for Estimating and Mapping Regionally Disaggregated Indicators" by Kreutzmann et al. (2019) <doi:10.18637/jss.v091.i07> and the second package vignette "A Framework for Producing Small Area Estimates Based on Area-Level Models in R".

Gas/Liquid Chromatography-Mass Spectrometer(GC/LC-MS) Data Analysis for Environmental Science. This package covered topics such molecular isotope ratio, matrix effects and Short-Chain Chlorinated Paraffins analysis etc. in environmental analysis.

This package provides fast dynamic-programming algorithms in C++'/'Rcpp (with pure R fallbacks) for the exact finite-sample distributions and p-values of Christoffersen (1998) independence (IND) and conditional-coverage (CC) VaR backtests. For completeness, it also provides the exact unconditional-coverage (UC) test following Kupiec (1995) via a closed-form binomial enumeration. See Christoffersen (1998) <doi:10.2307/2527341> and Kupiec (1995) <doi:10.3905/jod.1995.407942>.