This package provides methods for implementing hierarchical age length keys to estimate fish ages from lengths using data borrowing. Users can create hierarchical age length keys and use them to assign ages given length.

This package provides functions for the fitting and summarizing of heteroscedastic t-regression.

Kernel density estimation with hexagonal grid for bivariate data. Hexagonal grid has many beneficial properties like equidistant neighbours and less edge bias, making it better for spatial analyses than the more commonly used rectangular grid. Carr, D. B. et al. (1987) <doi:10.2307/2289444>. Diggle, P. J. (2010) <doi:10.1201/9781420072884>. Hill, B. (2017) <https://blog.bruce-hill.com/meandering-triangles>. Jones, M. C. (1993) <doi:10.1007/BF00147776>.

Cross-species identification of novel gene candidates using the NCBI web service is provided. Further, sets of miRNA target genes can be identified by using the targetscan.org API.

We provide functions for identifying the core community phylogeny in any microbiome, drawing phylogenetic Venn diagrams, calculating the core Faithâ s PD for a set of communities, and calculating the core UniFrac distance between two sets of communities. All functions rely on construction of a core community phylogeny, which is a phylogeny where branches are defined based on their presence in multiple samples from a single type of habitat. Our package provides two options for constructing the core community phylogeny, a tip-based approach, where the core community phylogeny is identified based on incidence of leaf nodes and a branch-based approach, where the core community phylogeny is identified based on incidence of individual branches. We suggest use of the microViz package.

This package provides a tool to format R markdown with CSS ids for HTML output. The tool may be most helpful for those using markdown to create reproducible documents. The biggest limitations in formatting is the knowledge of CSS by the document authors.

This is a collection of functions for converting coordinates between WGS84UTM, WGS84GEO, HK80UTM, HK80GEO and HK1980GRID Coordinate Systems used in Hong Kong SAR, based on the algorithms described in Explanatory Notes on Geodetic Datums in Hong Kong by Survey and Mapping Office Lands Department, Hong Kong Government (1995).

Penalized regression for generalized linear models for measurement error problems (aka. errors-in-variables). The package contains a version of the lasso (L1-penalization) which corrects for measurement error (Sorensen et al. (2015) <doi:10.5705/ss.2013.180>). It also contains an implementation of the Generalized Matrix Uncertainty Selector, which is a version the (Generalized) Dantzig Selector for the case of measurement error (Sorensen et al. (2018) <doi:10.1080/10618600.2018.1425626>).

Joint models have been widely used to study the associations between longitudinal biomarkers and a survival outcome. However, existing joint models only consider one or a few longitudinal biomarkers and cannot deal with high-dimensional longitudinal biomarkers. This package can be used to fit our recently developed penalized joint model that can handle high-dimensional longitudinal biomarkers. Specifically, an adaptive lasso penalty is imposed on the parameters for the effects of the longitudinal biomarkers on the survival outcome, which allows for variable selection. Also, our algorithm is computationally efficient, which is based on the Gaussian variational approximation method.

Multivariate conditional and marginal densities, moments, cumulative distribution functions as well as binary choice and sample selection models based on Hermite polynomial approximation which was proposed and described by A. Gallant and D. W. Nychka (1987) <doi:10.2307/1913241>.

In streaming data analysis, it is crucial to detect significant shifts in the data distribution or the accuracy of predictive models over time, a phenomenon known as concept drift. The package aims to identify when concept drift occurs and provide methodologies for adapting models in non-stationary environments. It offers a range of state-of-the-art techniques for detecting concept drift and maintaining model performance. Additionally, the package provides tools for adapting models in response to these changes, ensuring continuous and accurate predictions in dynamic contexts. Methods for concept drift detection are described in Tavares (2022) <doi:10.1007/s12530-021-09415-z>.

This package provides a data set of the Portuguese NHS hospitals.

This package provides HE plot and other functions for visualizing hypothesis tests in multivariate linear models. HE plots represent sums-of-squares-and-products matrices for linear hypotheses and for error using ellipses (in two dimensions) and ellipsoids (in three dimensions). It also provides other tools for analysis and graphical display of the models such as robust methods and homogeneity of variance covariance matrices. The related candisc package provides visualizations in a reduced-rank canonical discriminant space when there are more than a few response variables.

Given a database of previous treatment/placebo estimates, their standard errors and sample sizes, the program calculates a significance criteria and power estimate that takes into account the among trial variation.

Uses support vector machines to identify a perfectly separating hyperplane (linear or curvilinear) between two entities in high-dimensional space. If this plane exists, the entities do not overlap. Applications include overlap detection in morphological, resource or environmental dimensions. More details can be found in: Brown et al. (2020) <doi:10.1111/2041-210X.13363> .

This package provides utility functions for, and drawing on, the data.table package. The package also collates useful miscellaneous functions extending base R not available elsewhere. The name is a portmanteau of utils and the author.

Hedgehog will eat all your bugs. Hedgehog is a property-based testing package in the spirit of QuickCheck'. With Hedgehog', one can test properties of their programs against randomly generated input, providing far superior test coverage compared to unit testing. One of the key benefits of Hedgehog is integrated shrinking of counterexamples, which allows one to quickly find the cause of bugs, given salient examples when incorrect behaviour occurs.

This package implements the hub graphical lasso and hub covariance graph proposal by Tan, KM., London, P., Mohan, K., Lee, S-I., Fazel, M., and Witten, D. (2014). Learning graphical models with hubs. Journal of Machine Learning Research 15(Oct):3297-3331.

This package implements hierarchical clustering methods (single linkage, complete linkage, average linkage, and centroid linkage) with stepwise printing and dendrograms for didactic purposes.

This package provides tools for accessing various open data APIs in the Helsinki region in Finland. Current data sources include the Service Map API, Linked Events API, and Helsinki Region Infoshare statistics API.

Various functions and algorithms are provided here for solving optimal matching tasks in the context of preclinical cancer studies. Further, various helper and plotting functions are provided for unsupervised and supervised machine learning as well as longitudinal mixed-effects modeling of tumor growth response patterns.

Computes the scores and ranks candidates according to voting rules electing the highest median grade. Based on "Tie-breaking the highest median: alternatives to the majority judgment", A. Fabre, Social Choice & Welfare (forthcoming as of 2020). The paper is available here: <https://github.com/bixiou/highest_median/raw/master/Tie-breaking%20Highest%20Median%20-%20Fabre%202019.pdf>. Functions to plot the voting profiles can be found on github: <https://github.com/bixiou/highest_median/blob/master/packages_functions_data.R>.

Binary segmentation methods for detecting and estimating multiple change-points in the mean or second-order structure of high-dimensional time series as described in Cho and Fryzlewicz (2014) <doi:10.1111/rssb.12079> and Cho (2016) <doi:10.1214/16-EJS1155>.

Can be used for paternity and maternity assignment and outperforms conventional methods where closely related individuals occur in the pool of possible parents. The method compares the genotypes of offspring with any combination of potentials parents and scores the number of mismatches of these individuals at bi-allelic genetic markers (e.g. Single Nucleotide Polymorphisms). It elaborates on a prior exclusion method based on the Homozygous Opposite Test (HOT; Huisman 2017 <doi:10.1111/1755-0998.12665>) by introducing the additional exclusion criterion HIPHOP (Homozygous Identical Parents, Heterozygous Offspring are Precluded; Cockburn et al., in revision). Potential parents are excluded if they have more mismatches than can be expected due to genotyping error and mutation, and thereby one can identify the true genetic parents and detect situations where one (or both) of the true parents is not sampled. Package hiphop can deal with (a) the case where there is contextual information about parentage of the mother (i.e. a female has been seen to be involved in reproductive tasks such as nest building), but paternity is unknown (e.g. due to promiscuity), (b) where both parents need to be assigned, because there is no contextual information on which female laid eggs and which male fertilized them (e.g. polygynandrous mating system where multiple females and males deposit young in a common nest, or organisms with external fertilisation that breed in aggregations). For details: Cockburn, A., Penalba, J.V.,Jaccoud, D.,Kilian, A., Brouwer, L., Double, M.C., Margraf, N., Osmond, H.L., van de Pol, M. and Kruuk, L.E.B. (in revision). HIPHOP: improved paternity assignment among close relatives using a simple exclusion method for bi-allelic markers. Molecular Ecology Resources, DOI to be added upon acceptance.