An implementation of 1) the tail pairwise dependence matrix (TPDM) as described in Jiang & Cooley (2020) <doi:10.1175/JCLI-D-19-0413.1> 2) the extremal pattern index (EPI) as described in Szemkus & Friederichs ('Spatial patterns and indices for heatwave and droughts over Europe using a decomposition of extremal dependency'; submitted to ASCMO 2023).

For multiscale analysis, this package carries out empirical mode decomposition and Hilbert spectral analysis. For usage of EMD, see Kim and Oh, 2009 (Kim, D and Oh, H.-S. (2009) EMD: A Package for Empirical Mode Decomposition and Hilbert Spectrum, The R Journal, 1, 40-46).

This package provides functions for analysis of rate changes in sequential events.

Fit, plot and compare several (extreme value) distribution functions. Compute (truncated) distribution quantile estimates and plot return periods on a linear scale. On the fitting method, see Asquith (2011): Distributional Analysis with L-moment Statistics [...] ISBN 1463508417.

This package provides a framework to build and evaluate diagnosis or prognosis models using stacking, voting, and bagging ensemble techniques with various base learners. The package also includes tools for visualization and interpretation of models. The development version of the package is available on GitHub at <https://github.com/xiaojie0519/E2E>. The methods are based on the foundational work of Breiman (1996) <doi:10.1007/BF00058655> on bagging and Wolpert (1992) <doi:10.1016/S0893-6080(05)80023-1> on stacking.

API wrapper to download statistical information from the Economic Statistics System (ECOS) of the Bank of Korea <https://ecos.bok.or.kr/api/#/>.

This package provides functions for the simulation and the nonparametric estimation of elliptical distributions, meta-elliptical copulas and trans-elliptical distributions, following the article Derumigny and Fermanian (2022) <doi:10.1016/j.jmva.2022.104962>.

Background correction of spectral like data. Handles variations in scaling, polynomial baselines, interferents, constituents and replicate variation. Parameters for corrections are stored for further analysis, and spectra are corrected accordingly.

This package provides tools for fitting the Extended Empirical Saddlepoint (EES) density of Fasiolo et al. (2018) <doi:10.1214/18-EJS1433>.

EB-PRS is a novel method that leverages information for effect sizes across all the markers to improve the prediction accuracy. No parameter tuning is needed in the method, and no external information is needed. This R-package provides the calculation of polygenic risk scores from the given training summary statistics and testing data. We can use EB-PRS to extract main information, estimate Empirical Bayes parameters, derive polygenic risk scores for each individual in testing data, and evaluate the PRS according to AUC and predictive r2. See Song et al. (2020) <doi:10.1371/journal.pcbi.1007565> for a detailed presentation of the method.

This package provides a collection of epidemic/network-related tools. Simulates transmission of diseases through contact networks. Performs Bayesian inference on network and epidemic parameters, given epidemic data.

This package provides a set of extensions for the ergm package to fit weighted networks whose edge weights are counts. See Krivitsky (2012) <doi:10.1214/12-EJS696> and Krivitsky, Hunter, Morris, and Klumb (2023) <doi:10.18637/jss.v105.i06>.

Modular implementation of the Differential Evolution algorithm for experimenting with different types of operators.

This package performs automated morphological character partitioning for phylogenetic analyses and analyze macroevolutionary parameter outputs from clock (time-calibrated) Bayesian inference analyses, following concepts introduced by Simões and Pierce (2021) <doi:10.1038/s41559-021-01532-x>.

Use emailjs API easily in R'. This package is not official. <https://www.emailjs.com/docs/rest-api/send/>. You can send e-mail with emailjs with function, based on httr'. You can also make a shiny ui and server function. It can be used for making feedback form, inquiry, and so on.

Descarga, lee y analiza bases de la Encuesta Nacional de Hogares (ENAHO) y otras encuestas del Instituto Nacional de Estadà stica e Informática (INEI) del Perú. (Downloads, reads, and combines data from the Peruvian Home National Survey and other surveys from the National Institute for Statistics (INEI).).

Emissions are the mass of pollutants released into the atmosphere. Air quality models need emissions data, with spatial and temporal distribution, to represent air pollutant concentrations. This package, eixport, creates inputs for the air quality models WRF-Chem Grell et al (2005) <doi:10.1016/j.atmosenv.2005.04.027>, MUNICH Kim et al (2018) <doi:10.5194/gmd-11-611-2018> , BRAMS-SPM Freitas et al (2005) <doi:10.1016/j.atmosenv.2005.07.017> and RLINE Snyder et al (2013) <doi:10.1016/j.atmosenv.2013.05.074>. See the eixport website (<https://atmoschem.github.io/eixport/>) for more information, documentations and examples. More details in Ibarra-Espinosa et al (2018) <doi:10.21105/joss.00607>.

Process and analyze electronic health record (EHR) data. The EHR package provides modules to perform diverse medication-related studies using data from EHR databases. Especially, the package includes modules to perform pharmacokinetic/pharmacodynamic (PK/PD) analyses using EHRs, as outlined in Choi, Beck, McNeer, Weeks, Williams, James, Niu, Abou-Khalil, Birdwell, Roden, Stein, Bejan, Denny, and Van Driest (2020) <doi:10.1002/cpt.1787>. Additional modules will be added in future. In addition, this package provides various functions useful to perform Phenome Wide Association Study (PheWAS) to explore associations between drug exposure and phenotypes obtained from EHR data, as outlined in Choi, Carroll, Beck, Mosley, Roden, Denny, and Van Driest (2018) <doi:10.1093/bioinformatics/bty306>.

Pupillometry offers a non-invasive window into the mind and has been used extensively as a psychophysiological readout of arousal signals linked with cognitive processes like attention, stress, and emotional states [Clewett et al. (2020) <doi:10.1038/s41467-020-17851-9>; Kret & Sjak-Shie (2018) <doi:10.3758/s13428-018-1075-y>; Strauch (2024) <doi:10.1016/j.tins.2024.06.002>]. Yet, despite decades of pupillometry research, many established packages and workflows to date lack design patterns based on Findability, Accessibility, Interoperability, and Reusability (FAIR) principles [see Wilkinson et al. (2016) <doi:10.1038/sdata.2016.18>]. eyeris provides a modular, performant, and extensible preprocessing framework for pupillometry data with BIDS-like organization and interactive output reports [Esteban et al. (2019) <doi:10.1038/s41592-018-0235-4>; Gorgolewski et al. (2016) <doi:10.1038/sdata.2016.44>]. Development was supported, in part, by the Stanford Wu Tsai Human Performance Alliance, Stanford Ric Weiland Graduate Fellowship, Stanford Center for Mind, Brain, Computation and Technology, NIH National Institute on Aging Grants (R01-AG065255, R01-AG079345), NSF GRFP (DGE-2146755), McKnight Brain Research Foundation Clinical Translational Research Scholarship in Cognitive Aging and Age-Related Memory Loss, American Brain Foundation, and the American Academy of Neurology.

Special functions that enhance other mixed effect model packages by creating overlayed, reduced rank, and reduced model matrices together with multiple data sets to practice the use of these models. For more details see Covarrubias-Pazaran (2016) <doi:10.1371/journal.pone.0156744>.

Count regression models for underdispersed small counts (lambda < 20) based on the three-parameter exponentially weighted Poisson distribution of Ridout & Besbeas (2004) <DOI:10.1191/1471082X04st064oa>.

Facilitates the aggregation of species geographic ranges from vector or raster spatial data, and that enables the calculation of various morphological and phylogenetic community metrics across geography. Citation: Title, PO, DL Swiderski and ML Zelditch (2022) <doi:10.1111/2041-210X.13914>.

This is an R package implementing the epidemic volatility index (EVI), as discussed by Kostoulas et. al. (2021) and variations by Pateras et. al. (2023). EVI is a new, conceptually simple, early warning tool for oncoming epidemic waves. EVI is based on the volatility of newly reported cases per unit of time, ideally per day, and issues an early warning when the volatility change rate exceeds a threshold.

This package provides a consistent set of functions for enriching and analyzing sovereign-level economic data. Economists, data scientists, and financial professionals can use the package to add standardized identifiers, demographic and macroeconomic indicators, and derived metrics such as gross domestic product per capita or government expenditure shares.