Convenience Functions for Arrays

Some convenient functions to work with arrays.

Soft Classification Performance Measures

An extension of sensitivity, specificity, positive and negative predictive value to continuous predicted and reference memberships in [0, 1].

Colocalisation Tests of Two Genetic Traits

Performs the colocalisation tests described in Giambartolomei et al (2013) , Wallace (2020) , Wallace (2021) .

Unit Test Add-on for 'testthat'

Enhance package 'testthat' by allowing tests to be attached to the function/object they test. This allows to keep functional and unit test code together.

Circular Genomic Permutation using Genome Wide Association p-Values

Circular genomic permutation approach uses genome wide association studies (GWAS) results to establish the significance of pathway/gene-set associations whilst accounting for genomic structure. All single nucleotide polymorphisms (SNPs) in the GWAS are placed in a 'circular genome' according to their location. Then the complete set of SNP association p-values are permuted by rotation with respect to the SNPs' genomic locations. Two testing frameworks are available: permutations at the gene level, and permutations at the SNP level. The permutation at the gene level uses Fisher's combination test to calculate a single gene p-value, followed by the hypergeometric test. The SNP count methodology maps each SNP to pathways/gene-sets and calculates the proportion of SNPs for the real and the permutated datasets above a pre-defined threshold. Genomicper requires a matrix of GWAS association p-values and SNPs annotation to genes. Pathways can be obtained from within the package or can be provided by the user. Cabrera et al (2012) .

Moving Sum Based Procedures for Changes in the Mean

Implementations of MOSUM-based statistical procedures and algorithms for detecting multiple changes in the mean. This comprises the MOSUM procedure for estimating multiple mean changes from Eichinger and Kirch (2018) and the multiscale algorithmic extension from Cho and Kirch (2022) , as well as the bootstrap procedure for generating confidence intervals about the locations of change points as proposed in Cho and Kirch (2022) . See also Meier, Kirch and Cho (2021) which accompanies the R package.

Unimodal Penalized Spline Regression using B-Splines

Univariate spline regression. It is possible to add the shape constraint of unimodality and predefined or self-defined penalties on the B-spline coefficients.

Fitting Ising Models Using the ELasso Method

This network estimation procedure eLasso, which is based on the Ising model, combines l1-regularized logistic regression with model selection based on the Extended Bayesian Information Criterion (EBIC). EBIC is a fit measure that identifies relevant relationships between variables. The resulting network consists of variables as nodes and relevant relationships as edges. Can deal with binary data.

Multi-Factor Curve Analysis for Grouped Data in 'R'

Implements multi-factor curve analysis for grouped data in 'R', replicating and extending the functionality of the the 'Stata' ado 'mfcurve' (Krähmer, 2023) < https://ideas.repec.org/c/boc/bocode/s459224.html>. Related to the idea of specification curve analysis (Simonsohn, Simmons, and Nelson, 2020) . Includes data preprocessing, statistical testing, and visualization of results with confidence intervals.

Ensemble Forecast Verification for Large Data Sets

Set of tools to simplify application of atomic forecast verification metrics for (comparative) verification of ensemble forecasts to large data sets. The forecast metrics are imported from the 'SpecsVerification' package, and additional forecast metrics are provided with this package. Alternatively, new user-defined forecast scores can be implemented using the example scores provided and applied using the functionality of this package.