This package provides a set of spatial accessibility measures from a set of locations (demand) to another set of locations (supply). It aims, among others, to support research on spatial accessibility to health care facilities. Includes the locations and some characteristics of major public hospitals in Greece.

This package provides a software package help user to create virtual species for species distribution modelling. It includes several methods to help user to create virtual species distribution map. Those maps can be used for Species Distribution Modelling (SDM) study. SDM use environmental data for sites of occurrence of a species to predict all the sites where the environmental conditions are suitable for the species to persist, and may be expected to occur.

Data used in Taback, N. (2022). Design and Analysis of Experiments and Observational Studies using R. Chapman & Hall/CRC.

This package provides functionality to fit and simulate from stationary vine copula models for time series, see Nagler et al. (2022) <doi:10.1016/j.jeconom.2021.11.015>.

This package creates SEER (Surveillance, Epidemiology and End Results) and A-bomb data binaries from ASCII sources and provides tools for estimating SEER second cancer risks. Methods are described in <doi:10.1038/leu.2015.258>.

The definition of fuzzy random variable and the methods of simulation from fuzzy random variables are two challenging statistical problems in three recent decades. This package is organized based on a special definition of fuzzy random variable and simulate fuzzy random variable by Piecewise Linear Fuzzy Numbers (PLFNs); see Coroianua et al. (2013) <doi:10.1016/j.fss.2013.02.005> for details about PLFNs. Some important statistical functions are considered for obtaining the membership function of main statistics, such as mean, variance, summation, standard deviation and coefficient of variance. Some of applied advantages of Sim.PLFN package are: (1) Easily generating / simulation a random sample of PLFN, (2) drawing the membership functions of the simulated PLFNs or the membership function of the statistical result, and (3) Considering the simulated PLFNs for arithmetic operation or importing into some statistical computation. Finally, it must be mentioned that Sim.PLFN package works on the basis of FuzzyNumbers package.

The package is used for calibrating the design parameters for single-to-double arm transition design proposed by Shi and Yin (2017). The calibration is performed via numerical enumeration to find the optimal design that satisfies the constraints on the type I and II error rates.

Computes the Exposure-At-Default based on the standardized approach of CRR2 (SA-CCR). The simplified version of SA-CCR has been included, as well as the OEM methodology. Multiple trade types of all the five major asset classes are being supported including the Other Exposure and, given the inheritance- based structure of the application, the addition of further trade types is straightforward. The application returns a list of trees per Counterparty and CSA after automatically separating the trades based on the Counterparty, the CSAs, the hedging sets, the netting sets and the risk factors. The basis and volatility transactions are also identified and treated in specific hedging sets whereby the corresponding penalty factors are applied. All the examples appearing on the regulatory papers (both for the margined and the unmargined workflow) have been implemented including the latest CRR2 developments.

Implementation of SING algorithm to extract joint and individual non-Gaussian components from two datasets. SING uses an objective function that maximizes the skewness and kurtosis of latent components with a penalty to enhance the similarity between subject scores. Unlike other existing methods, SING does not use PCA for dimension reduction, but rather uses non-Gaussianity, which can improve feature extraction. Benjamin B.Risk, Irina Gaynanova (2021) <doi:10.1214/21-AOAS1466>.

When comparing single cases to control populations and no parameters are known researchers and clinicians must estimate these with a control sample. This is often done when testing a case's abnormality on some variable or testing abnormality of the discrepancy between two variables. Appropriate frequentist and Bayesian methods for doing this are here implemented, including tests allowing for the inclusion of covariates. These have been developed first and foremost by John Crawford and Paul Garthwaite, e.g. in Crawford and Howell (1998) <doi:10.1076/clin.12.4.482.7241>, Crawford and Garthwaite (2005) <doi:10.1037/0894-4105.19.3.318>, Crawford and Garthwaite (2007) <doi:10.1080/02643290701290146> and Crawford, Garthwaite and Ryan (2011) <doi:10.1016/j.cortex.2011.02.017>. The package is also equipped with power calculators for each method.

This package implements a suite of sensitivity analysis tools that extends the traditional omitted variable bias framework and makes it easier to understand the impact of omitted variables in regression models, as discussed in Cinelli, C. and Hazlett, C. (2020), "Making Sense of Sensitivity: Extending Omitted Variable Bias." Journal of the Royal Statistical Society, Series B (Statistical Methodology) <doi:10.1111/rssb.12348>.

Handles both vector and matrices, using a flexible S4 class for automatic differentiation. The method used is forward automatic differentiation. Many functions and methods have been defined, so that in most cases, functions written without automatic differentiation in mind can be used without change.

An algorithm that trains a meta-learning procedure that combines screening and wrapper methods to find a set of extremely low-dimensional attribute combinations. This package works on top of the caret package and proceeds in a forward-step manner. More specifically, it builds and tests learners starting from very few attributes until it includes a maximal number of attributes by increasing the number of attributes at each step. Hence, for each fixed number of attributes, the algorithm tests various (randomly selected) learners and picks those with the best performance in terms of training error. Throughout, the algorithm uses the information coming from the best learners at the previous step to build and test learners in the following step. In the end, it outputs a set of strong low-dimensional learners.

Random Forest-like tree ensemble that works with groups of predictor variables. When building a tree, a number of variables is taken randomly from each group separately, thus ensuring that it considers variables from each group for the splits. Useful when rows contain information about different things (e.g. user information and product information) and it's not sensible to make a prediction with information from only one group of variables, or when there are far more variables from one group than the other and it's desired to have groups appear evenly on trees. Trees are grown using the C5.0 algorithm rather than the usual CART algorithm. Supports parallelization (multithreaded), missing values in predictors, and categorical variables (without doing One-Hot encoding in the processing). Can also be used to create a regular (non-stratified) Random Forest-like model, but made up of C5.0 trees and with some additional control options. As it's built with C5.0 trees, it works only for classification (not for regression).

Software that leverages the capabilities of Circos by manipulating data, preparing configuration files, and running the Perl-native Circos directly from the R environment with minimal user intervention. Circos is a novel software that addresses the challenges in visualizing genetic data by creating circular ideograms composed of tracks of heatmaps, scatter plots, line plots, histograms, links between common markers, glyphs, text, and etc. Please see <http://www.circos.ca>.

Collection of custom input controls and user interface components for Shiny applications. Give your applications a unique and colorful style !

An extension of the AlphaSimR package (<https://cran.r-project.org/package=AlphaSimR>) for stochastic simulations of honeybee populations and breeding programmes. SIMplyBee enables simulation of individual bees that form a colony, which includes a queen, fathers (drones the queen mated with), virgin queens, workers, and drones. Multiple colony can be merged into a population of colonies, such as an apiary or a whole country of colonies. Functions enable operations on castes, colony, or colonies, to ease R scripting of whole populations. All AlphaSimR functionality with respect to genomes and genetic and phenotype values is available and further extended for honeybees, including haplo-diploidy, complementary sex determiner locus, colony events (swarming, supersedure, etc.), and colony phenotype values.

Survival analysis with sparse longitudinal covariates under right censoring scheme. Different hazards models are involved. Please cite the manuscripts corresponding to this package: Sun, Z. et al. (2022) <doi:10.1007/s10985-022-09548-6>, Sun, Z. and Cao, H. (2023) <arXiv:2310.15877> and Sun, D. et al. (2023) <arXiv:2308.15549>.

Spatial coverage sampling and random sampling from compact geographical strata created by k-means. See Walvoort et al. (2010) <doi:10.1016/j.cageo.2010.04.005> for details.

This package provides functions for performing set-theoretic multi-method research, QCA for clustered data, theory evaluation, Enhanced Standard Analysis, indirect calibration, radar visualisations. Additionally it includes data to replicate the examples in the books by Oana, I.E, C. Q. Schneider, and E. Thomann. Qualitative Comparative Analysis (QCA) using R: A Beginner's Guide. Cambridge University Press and C. Q. Schneider and C. Wagemann "Set Theoretic Methods for the Social Sciences", Cambridge University Press.

Univariate time series forecasting with STL decomposition based auto regressive integrated moving average (ARIMA) hybrid model. For method details see Xiong T, Li C, Bao Y (2018). <doi:10.1016/j.neucom.2017.11.053>.

M-estimators of location and shape following the power family (Frahm, Nordhausen, Oja (2020) <doi:10.1016/j.jmva.2019.104569>) are provided in the case of complete data and also when observations have missing values together with functions aiding their visualization.

Analyse species-habitat associations in R. Therefore, information about the location of the species (as a point pattern) is needed together with environmental conditions (as a categorical raster). To test for significance habitat associations, one of the two components is randomized. Methods are mainly based on Plotkin et al. (2000) <doi:10.1006/jtbi.2000.2158> and Harms et al. (2001) <doi:10.1111/j.1365-2745.2001.00615.x>.

Computes the optimal sample size for various 2-group designs (e.g., when comparing the means of two groups assuming equal variances, unequal variances, or comparing proportions) when the aim is to maximize the rewards over the full decision procedure of a) running a trial (with the computed sample size), and b) subsequently administering the winning treatment to the remaining N-n units in the population. Sample sizes and expected rewards for standard t- and z- tests are also provided.