We implement full-ranked, rank-penalized, and adaptive nuclear norm penalized estimation methods using multivariate mixture models proposed by Kang, Chen, and Yao (2022+).

Assesses the robustness of the community structure of a network found by one or more community detection algorithm to give indications about their reliability. It detects if the community structure found by a set of algorithms is statistically significant and compares the different selected detection algorithms on the same network. robin helps to choose among different community detection algorithms the one that better fits the network of interest. Reference in Policastro V., Righelli D., Carissimo A., Cutillo L., De Feis I. (2021) <https://journal.r-project.org/archive/2021/RJ-2021-040/index.html>.

This package provides a methodology to perform multivariate measurement error adjustment using external validation data. Allows users to remove the attenuating effect of measurement error by incorporating a distribution of external validation data, and allows for plotting of all resultant adjustments. Sensitivity analyses can also be run through this package to test how different ranges of validity coefficients can impact the effect of the measurement error adjustment. The methods implemented in this package are based on the work by Muoka, A., Agogo, G., Ngesa, O., Mwambi, H. (2020): <doi:10.12688/f1000research.27892.1>.

This package provides a collection of tools for measuring the similarity of text messages and tracing the flow of messages over time and across media.

Work with the PhyloPic Web Service (<http://api-docs.phylopic.org/v2/>) to fetch silhouette images of organisms. Includes functions for adding silhouettes to both base R plots and ggplot2 plots.

Routines to select and visualize the maxima for a given strict partial order. This especially includes the computation of the Pareto frontier, also known as (Top-k) Skyline operator (see Börzsönyi, et al. (2001) <doi:10.1109/ICDE.2001.914855>), and some generalizations known as database preferences (see Kieà ling (2002) <doi:10.1016/B978-155860869-6/50035-4>).

Perform the complete processing of a set of proton nuclear magnetic resonance spectra from the free induction decay (raw data) and based on a processing sequence (macro-command file). An additional file specifies all the spectra to be considered by associating their sample code as well as the levels of experimental factors to which they belong. More detail can be found in Jacob et al. (2017) <doi:10.1007/s11306-017-1178-y>.

This package provides a proof of concept implementation of regularized non-negative matrix factorization optimization. A non-negative matrix factorization factors non-negative matrix Y approximately as L R, for non-negative matrices L and R of reduced rank. This package supports such factorizations with weighted objective and regularization penalties. Allowable regularization penalties include L1 and L2 penalties on L and R, as well as non-orthogonality penalties. This package provides multiplicative update algorithms, which are a modification of the algorithm of Lee and Seung (2001) <http://papers.nips.cc/paper/1861-algorithms-for-non-negative-matrix-factorization.pdf>, as well as an additive update derived from that multiplicative update. See also Pav (2004) <doi:10.48550/arXiv.2410.22698>.

An implementation of a method based on information theory devised for the identification of genes showing a significant variation of expression across multiple conditions. Given expression estimates from any number of RNA-Seq samples and conditions it identifies genes or transcripts with a significant variation of expression across all the conditions studied, together with the samples in which they are over- or under-expressed. Zambelli et al. (2018) <doi:10.1093/nar/gky055>.

This package provides a robust backfitting algorithm for additive models based on (robust) local polynomial kernel smoothers. It includes both bounded and re-descending (kernel) M-estimators, and it computes predictions for points outside the training set if desired. See Boente, Martinez and Salibian-Barrera (2017) <doi:10.1080/10485252.2017.1369077> and Martinez and Salibian-Barrera (2021) <doi:10.21105/joss.02992> for details.

This is a port of Jonathan Shewchuk's Triangle library to R. From his description: "Triangle generates exact Delaunay triangulations, constrained Delaunay triangulations, conforming Delaunay triangulations, Voronoi diagrams, and high-quality triangular meshes. The latter can be generated with no small or large angles, and are thus suitable for finite element analysis.".

This package provides functions for phylogenetic analysis (Castiglione et al., 2018 <doi:10.1111/2041-210X.12954>). The functions perform the estimation of phenotypic evolutionary rates, identification of phenotypic evolutionary rate shifts, quantification of direction and size of evolutionary change in multivariate traits, the computation of ontogenetic shape vectors and test for morphological convergence.

Download the latest data from the Australian Prudential Regulation Authority <https://www.apra.gov.au/> and import it into R as a tidy data frame.

An approach to age-depth modelling that uses Bayesian statistics to reconstruct accumulation histories for 210Pb-dated deposits using prior information. It can combine 210Pb, radiocarbon, and other dates in the chronologies. See Aquino et al. (2018) <doi:10.1007/s13253-018-0328-7>. Note that parts of the code underlying rplum are derived from the rbacon package by the same authors, and there remains a degree of overlap between the two packages.

Creation, manipulation, simulation of linear Gaussian Bayesian networks from text files and more...

This package provides a novel bias-bound approach for non-parametric inference is introduced, focusing on both density and conditional expectation estimation. It constructs valid confidence intervals that account for the presence of a non-negligible bias and thus make it possible to perform inference with optimal mean squared error minimizing bandwidths. This package is based on Schennach (2020) <doi:10.1093/restud/rdz065>.

Interface to integrate igraph and ggplot2 graphics in a normalized coordinate system. RGraphSpace implements new geometric objects using ggplot2 prototypes, customized for side-by-side visualization of multiple graphs. By scaling shapes and graph elements, RGraphSpace helps to create layered visualizations that stay coherent across multiple graphs.

This package contains tools for reading and writing data from or to files in the formats: akterm, dmna, Scintec Format-1, and Campbell Scientific TOA5.

Assemble the panels of computerized adaptive multistage testing by the bottom-up and the top-down approach, and simulate the administration of the assembled panels. The full documentation and tutorials are at <https://github.com/xluo11/Rmst>. Reference: Luo and Kim (2018) <doi:10.1111/jedm.12174>.

Collection of tools for the analysis of the resilience of dynamic networks. Created as a classroom project.

Represents high-dimensional data as tables of features, samples and measurements, and a design list for tracking the meaning of individual variables. Using this format, filtering, normalization, and other transformations of a dataset can be carried out in a flexible manner. romic takes advantage of these transformations to create interactive shiny apps for exploratory data analysis such as an interactive heatmap.

Create interactive radar charts using the Chart.js JavaScript library and the htmlwidgets package. Chart.js <http://www.chartjs.org/> is a lightweight library that supports several types of simple chart using the HTML5 canvas element. This package provides an R interface specifically to the radar chart, sometimes called a spider chart, for visualising multivariate data.

This package provides tools to access, search, and manipulate ILO's ilostat database, including bulk download of statistical data, dictionary lookups, and table of contents.

Access to Boost Date_Time functionality for dates, durations (both for days and date time objects), time zones, and posix time ('ptime') is provided by using Rcpp modules'. The posix time implementation can support high-resolution of up to nano-second precision by using 96 bits (instead of 64 with R) to present a ptime object (but this needs recompilation with a #define set).