Bayesian Calibration of Complex Computer Codes

Performs Bayesian calibration of computer models as per Kennedy and O'Hagan 2001. The package includes routines to find the hyperparameters and parameters; see the help page for stage1() for a worked example using the toy dataset. A tutorial is provided in the calex.Rnw vignette; and a suite of especially simple one dimensional examples appears in inst/doc/one.dim/.

Sparse Arrays and Multivariate Polynomials

Sparse arrays interpreted as multivariate polynomials. Uses 'disordR' discipline (Hankin, 2022, ). To cite the package in publications please use Hankin (2022) .

Fast, Plain-English Readouts for A/B/n Tests

Sizes and reads out A/B/n tests on conversion rates in a few function calls. Supports unequal traffic splits, multiple treatment arms with multiplicity correction, sample ratio mismatch checks, achieved minimum detectable effects, and plain-English summaries.

Optimal Exact Tests for Multiple Binary Endpoints

Calculates exact hypothesis tests to compare a treatment and a reference group with respect to multiple binary endpoints. The tested null hypothesis is an identical multidimensional distribution of successes and failures in both groups. The alternative hypothesis is a larger success proportion in the treatment group in at least one endpoint. The tests are based on the multivariate permutation distribution of subjects between the two groups. For this permutation distribution, rejection regions are calculated that satisfy one of different possible optimization criteria. In particular, regions with maximal exhaustion of the nominal significance level, maximal power under a specified alternative or maximal number of elements can be found. Optimization is achieved by a branch-and-bound algorithm. By application of the closed testing principle, the global hypothesis tests are extended to multiple testing procedures.

Connect to 'Google Firebase'

Provides an interface to 'Google Firebase' services < https://firebase.google.com/>, including 'Firebase Realtime Database', 'Cloud Firestore', 'Firebase Authentication', and 'Cloud Storage for Firebase'. Supports interactive use and 'shiny' applications as well as automated server-side workflows. Data frames and objects can be stored and retrieved, users can be authenticated, and files can be managed through the services' application programming interfaces.

Optimum Contribution Selection and Population Genetics

A framework for the optimization of breeding programs via optimum contribution selection and mate allocation. An easy to use set of function for computation of optimum contributions of selection candidates, and of the population genetic parameters to be optimized. These parameters can be estimated using pedigree or genotype information, and include kinships, kinships at native haplotype segments, and breed composition of crossbred individuals. They are suitable for managing genetic diversity, removing introgressed genetic material, and accelerating genetic gain. Additionally, functions are provided for computing genetic contributions from ancestors, inbreeding coefficients, the native effective size, the native genome equivalent, pedigree completeness, and for preparing and plotting pedigrees. The methods are described in:\n Wellmann, R., and Pfeiffer, I. (2009) .\n Wellmann, R., and Bennewitz, J. (2011) .\n Wellmann, R., Hartwig, S., Bennewitz, J. (2012) .\n de Cara, M. A. R., Villanueva, B., Toro, M. A., Fernandez, J. (2013) .\n Wellmann, R., Bennewitz, J., Meuwissen, T.H.E. (2014) .\n Wellmann, R. (2019) .

Confounder-Adjusted Survival Curves and Cumulative Incidence Functions

Estimate and plot confounder-adjusted survival curves using either 'Direct Adjustment', 'Direct Adjustment with Pseudo-Values', various forms of 'Inverse Probability of Treatment Weighting', two forms of 'Augmented Inverse Probability of Treatment Weighting', 'Empirical Likelihood Estimation' or 'Targeted Maximum Likelihood Estimation'. Also includes a significance test for the difference between two adjusted survival curves and the calculation of adjusted restricted mean survival times. Additionally enables the user to estimate and plot cause-specific confounder-adjusted cumulative incidence functions in the competing risks setting using the same methods (with some exceptions). For details, see Denz et. al (2023) .

Calculate the Care Density or Fragmented Care Density Given a Patient-Sharing Network

Given a patient-sharing network, calculate either the classic care density as proposed by Pollack et al. (2013) or the fragmented care density as proposed by Engels et al. (2024) . By utilizing the 'igraph' and 'data.table' packages, the provided functions scale well for very large graphs.

Open GenBank Files

Opens complete record(s) with .gb extension from the NCBI/GenBank Nucleotide database and returns a list containing shaped record(s). These kind of files contains detailed records of DNA samples (locus, organism, type of sequence, source of the sequence...). An example of record can be found at < https://www.ncbi.nlm.nih.gov/nuccore/HE799070>.

Distances on Directed Graphs

Distances on dual-weighted directed graphs using priority-queue shortest paths (Padgham (2019) ). Weighted directed graphs have weights from A to B which may differ from those from B to A. Dual-weighted directed graphs have two sets of such weights. A canonical example is a street network to be used for routing in which routes are calculated by weighting distances according to the type of way and mode of transport, yet lengths of routes must be calculated from direct distances.