Extreme value theory, nonparametric kernel estimation, tail conditional probabilities, extreme conditional quantile, adaptive estimation, quantile regression, survival probabilities.

This package provides simple, fast, and stable functions to fit the normal means model using empirical Bayes. For available models and details, see function ebnm(). Our JSS article, Willwerscheid, Carbonetto, and Stephens (2025) <doi:10.18637/jss.v114.i03>, provides a detailed introduction to the package.

Computation of direct, chain and average (bisector) equating coefficients with standard errors using Item Response Theory (IRT) methods for dichotomous items (Battauz (2013) <doi:10.1007/s11336-012-9316-y>, Battauz (2015) <doi:10.18637/jss.v068.i07>). Test scoring can be performed by true score equating and observed score equating methods. DIF detection can be performed using a Wald-type test (Battauz (2019) <doi:10.1007/s10260-018-00442-w>). The package includes tests to assess the stability of the equating transformations (Battauz(2022) <doi:10.1111/stan.12277>).

This package creates simple or stacked epidemic curves for hourly, daily, weekly or monthly outcome data.

This package provides tools to compute the neural fragility matrix from intracranial electrocorticographic (iEEG) recordings, enabling the analysis of brain dynamics during seizures. The package implements the method described by Li et al. (2017) <doi:10.23919/ACC.2017.7963378> and includes functions for data preprocessing ('Epoch'), fragility computation ('calcAdjFrag'), and visualization.

This package provides methods for constructing confidence or credible regions for exceedance sets and contour lines.

This package provides statistical and visualization tools for the analysis of demographic indicators, and spatio-temporal behavior and characterization of outbreaks of vector-borne diseases (VBDs) in Colombia. It implements travel times estimated in Bravo-Vega C., Santos-Vega M., & Cordovez J.M. (2022), and the endemic channel method (Bortman, M. (1999) <https://iris.paho.org/handle/10665.2/8562>).

Recently many new p-value based multiple test procedures have been proposed, and these new methods are more powerful than the widely used Hochberg procedure. These procedures strongly control the familywise error rate (FWER). This is a comprehensive collection of p-value based FWER-control stepwise multiple test procedures, including six procedure families and thirty multiple test procedures. In this collection, the conservative Hochberg procedure, linear time Hommel procedures, asymptotic Rom procedure, Gou-Tamhane-Xi-Rom procedures, and Quick procedures are all developed in recent five years since 2014. The package name "elitism" is an acronym of "e"quipment for "l"ogarithmic and l"i"near "ti"me "s"tepwise "m"ultiple hypothesis testing. See Gou, J. (2022), "Quick multiple test procedures and p-value adjustments", Statistics in Biopharmaceutical Research 14(4), 636-650.

Package implements the EDNE-test for equivalence according to Hoffelder et al. (2015) <DOI:10.1080/10543406.2014.920344>. "EDNE" abbreviates "Euclidean Distance between the Non-standardized Expected values". The EDNE-test for equivalence is a multivariate two-sample equivalence test. Distance measure of the test is the Euclidean distance. The test is an asymptotically valid test for the family of distributions fulfilling the assumptions of the multivariate central limit theorem (see Hoffelder et al.,2015). The function EDNE.EQ() implements the EDNE-test for equivalence according to Hoffelder et al. (2015). The function EDNE.EQ.dissolution.profiles() implements a variant of the EDNE-test for equivalence analyses of dissolution profiles (see Suarez-Sharp et al.,2020 <DOI:10.1208/s12248-020-00458-9>). EDNE.EQ.dissolution.profiles() checks whether the quadratic mean of the differences of the expected values of both dissolution profile populations is statistically significantly smaller than 10 [\% of label claim]. The current regulatory standard approach for equivalence analyses of dissolution profiles is the similarity factor f2. The statistical hypotheses underlying EDNE.EQ.dissolution.profiles() coincide with the hypotheses for f2 (see Hoffelder et al.,2015, Suarez-Sharp et al., 2020).

This package provides a small collection of datasets supporting Pearson correlation and linear regression analysis. It includes the precomputed dataset sos100', with integer values summing to zero and squared sum equal to 100. For other values of n and user-defined parameters, the sos() function from the exams.forge package can be used to generate datasets on the fly. In addition, the package contains around 500 german R Markdown exercises that illustrate the usage of exams.forge commands.

Get high-resolution (1 km) daily climate data (precipitation, minimum and maximum temperatures) for points and polygons within Europe.

Digital simulation of electrochemical processes. Each function allows for implicit and explicit solution of the differential equation using methods like Euler, Backwards implicit, Runge Kutta 4, Crank Nicholson and Backward differentiation formula as well as different number of points for derivative approximation. Several electrochemical processes can be simulated such as: Chronoamperometry, Potential Step, Linear Sweep, Cyclic Voltammetry, Cyclic Voltammetry with electrochemical reaction followed by chemical reaction (EC mechanism) and CV with two following electrochemical reaction (EE mechanism). In update 1.1.0 has been added a general purpose CV function that allow to simulate up to 4 EE mechanism combined with chemical reaction for each species.Update 1.2.0 improved the accuracy of the measurements and allow personalized data resolution for simulation. Bibliography regarding this methods can be found in the following texts. Dieter Britz, Jorg Strutwolf (2016) <ISBN:978-3-319-30292-8>. Allen J. Bard, Larry R. Faulkner (2000) <ISBN:978-0-471-04372-0>.

Estimation of the components of an ETAS (Epidemic Type Aftershock Sequence) model for earthquake description. Non-parametric background seismicity can be estimated through FLP (Forward Likelihood Predictive). New version 2.0.0: covariates have been introduced to explain the effects of external factors on the induced seismicity; the parametrization has been changed; Chiodi, Adelfio (2017)<doi:10.18637/jss.v076.i03>.

This data management package provides some helper classes for publicly available data sources (HMD, DESTATIS) in Demography. Similar to ideas developed in the Bioconductor project <https://bioconductor.org> we strive to encapsulate data in easy to use S4 objects. If original data is provided in a text file, the resulting S4 object contains all information from that text file. But the information is somehow structured (header, footer, etc). Further the classes provide methods to make a subset for selected calendar years or selected regions. The resulting subset objects still contain the original header and footer information.

This package provides a convenient toolbox to import data exported from Electronic Data Capture (EDC) software TrialMaster'.

This package provides tools for simulating draws from continuous time processes with well-defined exponential family random graph (ERGM) equilibria, i.e. ERGM generating processes (EGPs). A number of EGPs are supported, including the families identified in Butts (2023) <doi:10.1080/0022250X.2023.2180001>, as are functions for hazard calculation and timing calibration.

Bayesian estimation of spatial weight matrices in spatial econometric panel models. Allows for estimation of spatial autoregressive (SAR), spatial error (SEM), spatial Durbin (SDM), spatial error Durbin (SDEM) and spatially lagged explanatory variable (SLX) type specifications featuring an unknown spatial weight matrix. Methodological details are given in Krisztin and Piribauer (2022) <doi:10.1080/17421772.2022.2095426>.

This package provides a set of extensions for the ergm package to fit weighted networks whose edge weights are ranks. See Krivitsky and Butts (2017) <doi:10.1177/0081175017692623> and Krivitsky, Hunter, Morris, and Klumb (2023) <doi:10.18637/jss.v105.i06>.

Total Time on Test plot and routines for parameter estimation of any lifetime distribution implemented in R via maximum likelihood (ML) given a data set. It is implemented thinking on parametric survival analysis, but it feasible to use in parameter estimation of probability density or mass functions in any field. The main routines maxlogL and maxlogLreg are wrapper functions specifically developed for ML estimation. There are included optimization procedures such as nlminb and optim from base package, and DEoptim Mullen (2011) <doi:10.18637/jss.v040.i06>. Standard errors are estimated with numDeriv Gilbert (2011) <https://CRAN.R-project.org/package=numDeriv> or the option Hessian = TRUE of optim function.

Maximum likelihood estimation of nonlinear mixed effects models of epidemic growth using Template Model Builder ('TMB'). Enables joint estimation for collections of disease incidence time series, including time series that describe multiple epidemic waves. Supports a set of widely used phenomenological models: exponential, logistic, Richards (generalized logistic), subexponential, and Gompertz. Provides methods for interrogating model objects and several auxiliary functions, including one for computing basic reproduction numbers from fitted values of the initial exponential growth rate. Preliminary versions of this software were applied in Ma et al. (2014) <doi:10.1007/s11538-013-9918-2> and in Earn et al. (2020) <doi:10.1073/pnas.2004904117>.

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 built-in Nemaplex database for nematodes, which can be used to search for various nematodes. Also supports various nematode community and functional analyses such as nematode diversity, maturity index, metabolic footprint, and functional guild. The methods are based on <https://shiny.wur.nl/ninja/>, Bongers, T. (1990) <doi:10.1007/BF00324627>, Ferris, H. (2010) <doi:10.1016/j.ejsobi.2010.01.003>, Wan, B. et al. (2022) <doi:10.1016/j.soilbio.2022.108695>, and Van Den Hoogen, J. et al. (2019) <doi:10.1038/s41586-019-1418-6>.

This package provides functions for signal detection and identification designed for Event-Related Potentials (ERP) data in a linear model framework. The functional F-test proposed in Causeur, Sheu, Perthame, Rufini (2018, submitted) for analysis of variance issues in ERP designs is implemented for signal detection (tests for mean difference among groups of curves in One-way ANOVA designs for example). Once an experimental effect is declared significant, identification of significant intervals is achieved by the multiple testing procedures reviewed and compared in Sheu, Perthame, Lee and Causeur (2016, <DOI:10.1214/15-AOAS888>). Some of the methods gathered in the package are the classical FDR- and FWER-controlling procedures, also available using function p.adjust. The package also implements the Guthrie-Buchwald procedure (Guthrie and Buchwald, 1991 <DOI:10.1111/j.1469-8986.1991.tb00417.x>), which accounts for the auto-correlation among t-tests to control erroneous detection of short intervals. The Adaptive Factor-Adjustment method is an extension of the method described in Causeur, Chu, Hsieh and Sheu (2012, <DOI:10.3758/s13428-012-0230-0>). It assumes a factor model for the correlation among tests and combines adaptively the estimation of the signal and the updating of the dependence modelling (see Sheu et al., 2016, <DOI:10.1214/15-AOAS888> for further details).

Extracting desired data using the proper Census variable names can be time-consuming. This package takes the pain out of that process by providing functions to quickly locate variables and download labeled tables from the Census APIs (<https://www.census.gov/data/developers/data-sets.html>).