Create Upset plots using a combination of ggplot2 and patchwork'.

SCEPtER pipeline for estimating the stellar age for double-lined detached binary systems. The observational constraints adopted in the recovery are the effective temperature, the metallicity [Fe/H], the mass, and the radius of the two stars. The results are obtained adopting a maximum likelihood technique over a grid of pre-computed stellar models.

This package provides a set of functions is provided for 1) the stratum lengths analysis along a chosen direction, 2) fast estimation of continuous lag spatial Markov chains model parameters and probability computing (also for large data sets), 3) transition probability maps and transiograms drawing, 4) simulation methods for categorical random fields. More details on the methodology are discussed in Sartore (2013) <doi:10.32614/RJ-2013-022> and Sartore et al. (2016) <doi:10.1016/j.cageo.2016.06.001>.

Sometimes it is handy to be able to view an image file on an R graphics device. This package just does that. Currently it supports PNG files.

Stacked ensemble for regression tasks based on mlr3 framework with a pipeline for preprocessing numeric and factor features and hyper-parameter tuning using grid or random search.

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.

Identifies single nucleotide variants in next-generation sequencing data by estimating their local false discovery rates. For more details, see Karimnezhad, A. and Perkins, T. J. (2024) <doi:10.1038/s41598-024-51958-z>.

This package creates simulated data from structural equation models with standardized loading. Data generation methods are described in Schneider (2013) <doi:10.1177/0734282913478046>.

This package contains methods for simulation and for evaluating the pdf, cdf, and quantile functions for symmetric stable, symmetric classical tempered stable, and symmetric power tempered stable distributions.

This package provides tools for processing and evaluating seasonal weather forecasts, with an emphasis on tercile forecasts. We follow the World Meteorological Organization's "Guidance on Verification of Operational Seasonal Climate Forecasts", S.J.Mason (2018, ISBN: 978-92-63-11220-0, URL: <https://library.wmo.int/idurl/4/56227>). The development was supported by the European Unionâ s Horizon 2020 research and innovation programme under grant agreement no. 869730 (CONFER). A comprehensive online tutorial is available at <https://seasonalforecastingengine.github.io/SeaValDoc/>.

Construct subtests from a pool of items by using ant-colony-optimization, genetic algorithms, brute force, or random sampling. Schultze (2017) <doi:10.17169/refubium-622>.

This package provides function for area level of small area estimation using hierarchical Bayesian (HB) method with Zero-Inflated Binomial distribution for variables of interest. Some dataset produced by a data generation are also provided. The rjags package is employed to obtain parameter estimates. Model-based estimators involves the HB estimators which include the mean and the variation of mean.

This package provides functions and data sets for data sharpening. Nonparametric regressions are computed subject to smoothness and other kinds of penalties.

Created for population health analytics and monitoring. The functions in this package work best when working with patient level Master Patient Index-like datasets . Built to be used by NHS bodies and other health service providers.

Create a skeleton shiny application with create_template() that is reproducible, can be saved and meets academic standards for attribution. Forked from wallace'. Code is split into modules that are loaded and linked together automatically and each call one function. Guidance pages explain modules to users and flexible logging informs them of any errors. Options enable asynchronous operations, viewing of source code, interactive maps and data tables. Use to create complex analytical applications, following best practices in open science and software development. Includes functions for automating repetitive development tasks and an example application at run_shinyscholar() that requires install.packages("shinyscholar", dependencies = TRUE). A guide to developing applications can be found on the package website.

Given a list of substance compositions, a list of substances involved in a process, and a list of constraints in addition to mass conservation of elementary constituents, the package contains functions to build the substance composition matrix, to analyze the uniqueness of process stoichiometry, and to calculate stoichiometric coefficients if process stoichiometry is unique. (See Reichert, P. and Schuwirth, N., A generic framework for deriving process stoichiometry in enviromental models, Environmental Modelling and Software 25, 1241-1251, 2010 for more details.).

ML and GM estimation and diagnostic testing of econometric models for spatial panel data.

This package contains methods to generate and evaluate semi-artificial data sets. Based on a given data set different methods learn data properties using machine learning algorithms and generate new data with the same properties. The package currently includes the following data generators: i) a RBF network based generator using rbfDDA() from package RSNNS', ii) a Random Forest based generator for both classification and regression problems iii) a density forest based generator for unsupervised data Data evaluation support tools include: a) single attribute based statistical evaluation: mean, median, standard deviation, skewness, kurtosis, medcouple, L/RMC, KS test, Hellinger distance b) evaluation based on clustering using Adjusted Rand Index (ARI) and FM c) evaluation based on classification performance with various learning models, e.g., random forests.

Tidies up the forecasting modeling and prediction work flow, extends the broom package with sw_tidy', sw_glance', sw_augment', and sw_tidy_decomp functions for various forecasting models, and enables converting forecast objects to "tidy" data frames with sw_sweep'.

Simulate genotypes in SNP (single nucleotide polymorphisms) Matrix as random numbers from an uniform distribution, for diploid organisms (coded by 0, 1, 2), Sikorska et al., (2013) <doi:10.1186/1471-2105-14-166>, or half-sib/full-sib SNP matrix from real or simulated parents SNP data, assuming mendelian segregation. Simulate phenotypic traits for real or simulated SNP data, controlled by a specific number of quantitative trait loci and their effects, sampled from a Normal or an Uniform distributions, assuming a pure additive model. This is useful for testing association and genomic prediction models or for educational purposes.

This package provides a general framework for statistical simulation, which allows researchers to make use of a wide range of simulation designs with minimal programming effort. The package provides functionality for drawing samples from a distribution or a finite population, for adding outliers and missing values, as well as for visualization of the simulation results. It follows a clear object-oriented design and supports parallel computing to increase computational performance.

Spatial versions of Regression Discontinuity Designs (RDDs) are becoming increasingly popular as tools for causal inference. However, conducting state-of-the-art analyses often involves tedious and time-consuming steps. This package offers comprehensive functionalities for executing all required spatial and econometric tasks in a streamlined manner. Moreover, it equips researchers with tools for performing essential placebo and balancing checks comprehensively. The fact that researchers do not have to rely on APIs of external GIS software ensures replicability and raises the standard for spatial RDDs.

We provide functions for estimation and inference of nonlinear and non-stationary time series regression using the sieve methods and bootstrapping procedure.

Shortest paths between points in grids. Optional barriers and custom transition functions. Applications regarding planet Earth, as well as generally spheres and planes. Optimized for computational performance, customizability, and user friendliness. Graph-theoretical implementation tailored to gridded data. Currently focused on Dijkstra's (1959) <doi:10.1007/BF01386390> algorithm. Future updates broaden the scope to other least cost path algorithms and to centrality measures.