This package provides a set of functions to solve Erlang-C model. The Erlang C formula was invented by the Danish Mathematician A.K. Erlang and is used to calculate the number of advisors and the service level.

Extracts Exchangeable Image File Format (EXIF) metadata, such as camera make and model, ISO speed and the date-time the picture was taken on, from JPEG images. Incorporates the easyexif <https://github.com/mayanklahiri/easyexif> library.

This package contains all the datasets that were used in Social Science Experiments: A Hands-On Introduction and in its R Companion. Relevant materials can be found at <https://osf.io/b78je>.

This package provides a tool for the preparation and enrichment of health datasets for analysis (Toner et al. (2023) <doi:10.1093/gigascience/giad030>). Provides functionality for assessing data quality and for improving the reliability and machine interpretability of a dataset. eHDPrep also enables semantic enrichment of a dataset where metavariables are discovered from the relationships between input variables determined from user-provided ontologies.

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.

Dissimilarity-based analysis functions including ordination and Mantel test functions, intended for use with spatial and community ecological data. The original package description is in Goslee and Urban (2007) <doi:10.18637/jss.v022.i07>, with further statistical detail in Goslee (2010) <doi:10.1007/s11258-009-9641-0>.

Endpoint selection and sample size reassessment for multiple binary endpoints based on blinded and/or unblinded data. Trial design that allows an adaptive modification of the primary endpoint based on blinded information obtained at an interim analysis. The decision rule chooses the endpoint with the lower estimated required sample size. Additionally, the sample size is reassessed using the estimated event probabilities and correlation between endpoints. The implemented design is proposed in Bofill Roig, M., Gómez Melis, G., Posch, M., and Koenig, F. (2022). <doi:10.48550/arXiv.2206.09639>.

Perform analysis of variance and other important complementary analyses. The functions are easy to use. Performs analysis in various designs, with balanced and unbalanced data.

This package provides convenience functions for researching experiences including user, customer, patient, employee, and other human experiences. It provides a suite of tools to simplify data exploration such as benchmarking, comparing groups, and checking for differences. The outputs translate statistical approaches in applied experience research to human readable output.

Fits extreme value mixture models, which are models for tails not requiring selection of a threshold, for continuous data. It includes functions for model comparison, estimation of quantity of interest in extreme value analysis and plotting. Reference: CN Behrens, HF Lopes, D Gamerman (2004) <doi:10.1191/1471082X04st075oa>. FF do Nascimento, D. Gamerman, HF Lopes <doi:10.1007/s11222-011-9270-z>.

Analytical methods to locate and characterise ecotones, ecosystems and environmental patchiness along ecological gradients. Methods are implemented for isolated sampling or for space/time series. It includes Detrended Correspondence Analysis (Hill & Gauch (1980) <doi:10.1007/BF00048870>), fuzzy clustering (De Cáceres et al. (2010) <doi:10.1080/01621459.1963.10500845>), biodiversity indices (Jost (2006) <doi:10.1111/j.2006.0030-1299.14714.x>), and network analyses (Epskamp et al. (2012) <doi:10.18637/jss.v048.i04>) - as well as tools to explore the number of clusters in the data. Functions to produce synthetic ecological datasets are also provided.

Testing for and dating periods of explosive dynamics (exuberance) in time series using the univariate and panel recursive unit root tests proposed by Phillips et al. (2015) <doi:10.1111/iere.12132> and Pavlidis et al. (2016) <doi:10.1007/s11146-015-9531-2>.The recursive least-squares algorithm utilizes the matrix inversion lemma to avoid matrix inversion which results in significant speed improvements. Simulation of a variety of periodically-collapsing bubble processes. Details can be found in Vasilopoulos et al. (2022) <doi:10.18637/jss.v103.i10>.

Goodness-of-fit tests for discrete multivariate data. It is tested if a given observation is likely to have occurred under the assumption of an ab-initio model. Monte Carlo methods are provided to make the package capable of solving high-dimensional problems.

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.

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.

This package provides an interface to the European Central Bank's Data Portal API, allowing for programmatic retrieval of a vast quantity of statistical data.

This package implements entropy balancing, a data preprocessing procedure described in Hainmueller (2012, <doi:10.1093/pan/mpr025>) that allows users to reweight a dataset such that the covariate distributions in the reweighted data satisfy a set of user-specified moment conditions. Useful for creating balanced samples in observational studies with a binary treatment where the control group is reweighted to match the covariate moments of the treatment group, and for reweighting a survey sample to known characteristics from a target population.

Speed up common tasks, particularly logical or relational comparisons and routine follow up tasks such as finding the indices and subsetting. Inspired by mathematics, where something like: 3 < x < 6 is a standard, elegant and clear way to assert that x is both greater than 3 and less than 6 (see for example <https://en.wikipedia.org/wiki/Relational_operator>), a chaining operator is implemented. The chaining operator, %c%, allows multiple relational operations to be used in quotes on the right hand side for the same object, on the left hand side. The %e% operator allows something like set-builder notation (see for example <https://en.wikipedia.org/wiki/Set-builder_notation>) to be used on the right hand side. Operators have built in prefixes defined for all, any, subset, and which to reduce the amount of code needed for common tasks, such as return those values that are true.

Simulates cyclic voltammetry, linear-sweep voltammetry (both with and without stirring of the solution), and single-pulse and double-pulse chronoamperometry and chronocoulometry experiments using the implicit finite difference method outlined in Gosser (1993, ISBN: 9781560810261) and in Brown (2015) <doi:10.1021/acs.jchemed.5b00225>. Additional functions provide ways to display and to examine the results of these simulations. The primary purpose of this package is to provide tools for use in courses in analytical chemistry.

Ever read or wrote source files containing sectioning comments? If these comments are markdown style section comments, you can excerpt them and set a table of contents using the python package excerpts (<https://pypi.org/project/excerpts/>).

Calculate cutoff values for model fit measures used in structural equation modeling (SEM) by simulating and testing data sets (cf. Hu & Bentler, 1999 <doi:10.1080/10705519909540118>) with the same parameters (population model, number of observations, etc.) as the model under consideration.

Some wrappers, functions and data sets for for spatial point pattern analysis (mainly based on spatstat'), used in the book "Introduccion al Analisis Espacial de Datos en Ecologia y Ciencias Ambientales: Metodos y Aplicaciones" and in the papers by De la Cruz et al. (2008) <doi:10.1111/j.0906-7590.2008.05299.x> and Olano et al. (2009) <doi:10.1051/forest:2008074>.

This package contains utilities and functions for the cleaning, processing and management of patient level public health data for surveillance and analysis held by the UK Health Security Agency, UKHSA.

This package provides a set of extensions for the ergm package to fit multilayer/multiplex/multirelational networks and samples of multiple networks. ergm.multi is a part of the Statnet suite of packages for network analysis. See Krivitsky, Koehly, and Marcum (2020) <doi:10.1007/s11336-020-09720-7> and Krivitsky, Coletti, and Hens (2023) <doi:10.1080/01621459.2023.2242627>.