An integrated toolset for the analysis of de novo (sporadic) genetic sequence variants. denovolyzeR implements a mutational model that estimates the probability of a de novo genetic variant arising in each human gene, from which one can infer the expected number of de novo variants in a given population size. Observed variant frequencies can then be compared against expectation in a Poisson framework. denovolyzeR provides a suite of functions to implement these analyses for the interpretation of de novo variation in human disease.

Shiny modules to import data into an application or addin from various sources, and to manipulate them after that.

This package provides functions that offer seamless D3Plus integration. The examples provided here are taken from the official D3Plus website <http://d3plus.org>.

This package provides methods to apply decomposition-based relative importance analysis for R functions. This package supports the application of decomposition methods by providing lapply'- or Map'-like meta-functions that compute dominance analysis (Azen, R., & Budescu, D. V. (2003) <doi:10.1037/1082-989X.8.2.129>; Grömping, U. (2007) <doi:10.1198/000313007X188252>) an extension of Shapley value regression (Lipovetsky, S., & Conklin, M. (2001) <doi:10.1002/asmb.446>) based on the values returned from other functions.

The debar sequence processing pipeline is designed for denoising high throughput sequencing data for the animal DNA barcode marker cytochrome c oxidase I (COI). The package is designed to detect and correct insertion and deletion errors within sequencer outputs. This is accomplished through comparison of input sequences against a profile hidden Markov model (PHMM) using the Viterbi algorithm (for algorithm details see Durbin et al. 1998, ISBN: 9780521629713). Inserted base pairs are removed and deleted base pairs are accounted for through the introduction of a placeholder character. Since the PHMM is a probabilistic representation of the COI barcode, corrections are not always perfect. For this reason debar censors base pairs adjacent to reported indel sites, turning them into placeholder characters (default is 7 base pairs in either direction, this feature can be disabled). Testing has shown that this censorship results in the correct sequence length being restored, and erroneous base pairs being masked the vast majority of the time (>95%).

Comparison of the accuracy of two binary diagnostic tests in a "paired" study design, i.e. when each test is applied to each subject in the study.

The function takes a DNA sequence, a start point, an end point in the sequence, dot size and dot color and draws a fractal image of the sequence. The fractal starts in the center of the canvas. The image is drawn by moving base by base along the sequence and dropping a midpoint between the actual point and the corner designated by the actual base. For more details see Jeffrey (1990) <doi:10.1093/nar/18.8.2163>, Hill, Schisler, and Singh (1992) <doi:10.1007/BF00178602>, and Löchel and Heider (2021) <doi:10.1016/j.csbj.2021.11.008>.

An anonymization algorithm to resist neighbor label attack in a dynamic network.

Allows users to quickly and easily describe data using common descriptive statistics.

Distances on dual-weighted directed graphs using priority-queue shortest paths (Padgham (2019) <doi:10.32866/6945>). 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.

This package provides a metapackage that brings together a curated collection of R packages containing domain-specific datasets. It includes time series data, educational metrics, crime records, medical datasets, and oncology research data. Designed to provide researchers, analysts, educators, and data scientists with centralized access to structured and well-documented datasets, this metapackage facilitates reproducible research, data exploration, and teaching applications across a wide range of domains. Included packages: - timeSeriesDataSets': Time series data from economics, finance, energy, and healthcare. - educationR': Datasets related to education, learning outcomes, and school metrics. - crimedatasets': Datasets on global and local crime and criminal behavior. - MedDataSets': Datasets related to medicine, public health, treatments, and clinical trials. - OncoDataSets': Datasets focused on cancer research, survival, genetics, and biomarkers.

Decomposing value added growth into explanatory factors. A cost constrained value added function is defined to specify the production frontier. Industry estimates can also be aggregated using a weighted average approach. Details about the methodology and data can be found in Diewert and Fox (2018) <doi:10.1093/oxfordhb/9780190226718.013.19> and Zeng, Parsons, Diewert and Fox (2018) <https://www.business.unsw.edu.au/research-site/centreforappliedeconomicresearch-site/Documents/emg2018-6_SZeng_EMG-Slides.pdf>.

Statistical modelling and forecasting in claims reserving in non-life insurance under the Double Chain Ladder framework by Martinez-Miranda, Nielsen and Verrall (2012).

Generate reports that enable quick visual review of temporal shifts in record-level data. Time series plots showing aggregated values are automatically created for each data field (column) depending on its contents (e.g. min/max/mean values for numeric data, no. of distinct values for categorical data), as well as overviews for missing values, non-conformant values, and duplicated rows. The resulting reports are shareable and can contribute to forming a transparent record of the entire analysis process. It is designed with Electronic Health Records in mind, but can be used for any type of record-level temporal data (i.e. tabular data where each row represents a single "event", one column contains the "event date", and other columns contain any associated values for the event).

Estimation of incidence and case fatality for a chronic disease, given partial information, using a multi-state model. Given data on age-specific mortality and either incidence or prevalence, Bayesian inference is used to estimate the posterior distributions of incidence, case fatality, and functions of these such as prevalence. The methods are described in Jackson et al. (2023) <doi:10.1093/jrsssa/qnac015>.

Draws stylized choropleth maps -- hexagonal maps and triangular multiclass hex maps -- for New Zealand District Health Boards and Regional Council areas. These allow faceted, coloured displays of quantitative information for comparison across District Health Boards or Regional Councils. The preprint Lumley (2019) <arXiv:1912.04435> is based on the methods in this package.

Analysis of historical non-decimal currencies and value systems that use tripartite or tetrapartite systems such as pounds, shillings, and pence. It introduces new vector classes to represent non-decimal currencies, making them compatible with numeric classes, and provides functions to work with these classes in data frames in the context of double-entry bookkeeping.

This package provides functions are provided to fit temporal lag models to dynamic networks. The models are build on top of exponential random graph models (ERGM) framework. There are functions for simulating or forecasting networks for future time points. Abhirup Mallik & Zack W. Almquist (2019) Stable Multiple Time Step Simulation/Prediction From Lagged Dynamic Network Regression Models, Journal of Computational and Graphical Statistics, 28:4, 967-979, <DOI: 10.1080/10618600.2019.1594834>.

This package provides a collection of widely used univariate data sets of various applied domains on applications of distribution theory. The functions allow researchers and practitioners to quickly, easily, and efficiently access and use these data sets. The data are related to different applied domains and as follows: Bio-medical, survival analysis, medicine, reliability analysis, hydrology, actuarial science, operational research, meteorology, extreme values, quality control, engineering, finance, sports and economics. The total 100 data sets are documented along with associated references for further details and uses.

This package provides tools for describing parameters of algorithms in an abstract way. Description can include an id, a description, a domain (range or list of values), and a default value. dynparam can also convert parameter sets to a ParamHelpers format, in order to be able to use dynparam in conjunction with mlrMBO'.

We offer an implementation of the series representation put forth in "A series representation for multidimensional Rayleigh distributions" by Wiegand and Nadarajah <DOI: 10.1002/dac.3510>. Furthermore we have implemented an integration approach proposed by Beaulieu et al. for 3 and 4-dimensional Rayleigh densities (Beaulieu, Zhang, "New simplest exact forms for the 3D and 4D multivariate Rayleigh PDFs with applications to antenna array geometrics", <DOI: 10.1109/TCOMM.2017.2709307>).

This package provides a `.` object which can be used for unpacking assignments. For example, `.[rows, columns] <- dim(cars)` could be used to pull the number of rows and number of columns from `dim(cars)` into individual variables `rows` and `columns` in a single step.

Double constrained correspondence analysis (dc-CA) analyzes (multi-)trait (multi-)environment ecological data by using the vegan package and native R code. Throughout the two step algorithm of ter Braak et al. (2018) is used. This algorithm combines and extends community- (sample-) and species-level analyses, i.e. the usual community weighted means (CWM)-based regression analysis and the species-level analysis of species-niche centroids (SNC)-based regression analysis. The two steps use canonical correspondence analysis to regress the abundance data on to the traits and (weighted) redundancy analysis to regress the CWM of the orthonormalized traits on to the environmental predictors. The function dc_CA() has an option to divide the abundance data of a site by the site total, giving equal site weights. This division has the advantage that the multivariate analysis corresponds with an unweighted (multi-trait) community-level analysis, instead of being weighted. The first step of the algorithm uses vegan::cca(). The second step uses wrda() but vegan::rda() if the site weights are equal. This version has a predict() function. For details see ter Braak et al. 2018 <doi:10.1007/s10651-017-0395-x>. and ter Braak & van Rossum 2025 <doi:10.1016/j.ecoinf.2025.103143>.

High-frequency time-series support via nanotime and data.table'.