This is a meta-package designed to support the installation of Rmosek (>= 6.0) and bring the optimization facilities of MOSEK (>= 6.0) to the R-language. The interface supports large-scale optimization of many kinds: Mixed-integer and continuous linear, second-order cone, exponential cone and power cone optimization, as well as continuous semidefinite optimization. Rmosek and the R-language are open-source projects. MOSEK is a proprietary product, but unrestricted trial and academic licenses are available.

This package provides tools for preprocessing and processing canopy photographs with support for raw data reading. Provides methods to address variability in sky brightness and to mitigate errors from image acquisition in non-diffuse light. Works with all types of fish-eye lenses, and some methods also apply to conventional lenses.

This package provides a set of tools to explore the behaviour statistics used for forensic DNA interpretation when close relatives are involved. The package also offers some useful tools for exploring other forensic DNA situations.

Statistical estimation of revealed preference models from data collected on bipartite matchings. The models are for matchings within a bipartite population where individuals have utility for people based on known and unknown characteristics. People can form a partnership or remain unpartnered. The model represents both the availability of potential partners of different types and preferences of individuals for such people. The software estimates preference parameters based on sample survey data on partnerships and population composition. The simulation of matchings and goodness-of-fit are considered. See Goyal, Handcock, Jackson, Rendall and Yeung (2022) <doi:10.1093/jrsssa/qnad031>.

R implementation of the FAIR Data Pipeline API'. The FAIR Data Pipeline is intended to enable tracking of provenance of FAIR (findable, accessible and interoperable) data used in epidemiological modelling.

This package provides XML parsing capability through the Rapidxml C++ header-only library.

This package provides functions to compute recentered influence functions (RIF) of a distributional variable at the mean, quantiles, variance, gini or any custom functional of interest. The package allows to regress the RIF on any number of covariates. Generic print, plot and summary functions are also provided. Reference: Firpo, Sergio, Nicole M. Fortin, and Thomas Lemieux. (2009) <doi:10.3982/ECTA6822>. "Unconditional Quantile Regressions.".

This package provides a range of functions for the design and analysis of disease surveillance activities. These functions were originally developed for animal health surveillance activities but can be equally applied to aquatic animal, wildlife, plant and human health surveillance activities. Utilities are included for sample size calculation and analysis of representative surveys for disease freedom, risk-based studies for disease freedom and for prevalence estimation. This package is based on Cameron A., Conraths F., Frohlich A., Schauer B., Schulz K., Sergeant E., Sonnenburg J., Staubach C. (2015). R package of functions for risk-based surveillance. Deliverable 6.24, WP 6 - Decision making tools for implementing risk-based surveillance, Grant Number no. 310806, RISKSUR (<https://www.fp7-risksur.eu/sites/default/files/documents/Deliverables/RISKSUR_%28310806%29_D6.24.pdf>). Many of the RSurveillance functions are incorporated into the epitools website: Sergeant, ESG, 2019. Epitools epidemiological calculators. Ausvet Pty Ltd. Available at: <http://epitools.ausvet.com.au>.

Tool for the analysis Mass Spectrometry (MS) data in the context of immunopeptidomic analysis for the identification of hybrid peptides and the predictions of binding affinity of all peptides using netMHCpan <doi:10.1093/nar/gkaa379> while providing a summary of the netMHCpan output. RHybridFinder (RHF) is destined for researchers who are looking to analyze their MS data for the purpose of identification of potential spliced peptides. This package, developed mainly in base R, is based on the workflow published by Faridi et al. in 2018 <doi:10.1126/sciimmunol.aar3947>.

An optimized method for identifying mutually exclusive genomic events. Its main contribution is a statistical analysis based on the Poisson-Binomial distribution that takes into account that some samples are more mutated than others. See [Canisius, Sander, John WM Martens, and Lodewyk FA Wessels. (2016) "A novel independence test for somatic alterations in cancer shows that biology drives mutual exclusivity but chance explains most co-occurrence." Genome biology 17.1 : 1-17. <doi:10.1186/s13059-016-1114-x>]. The mutations matrices are sparse matrices. The method developed takes advantage of the advantages of this type of matrix to save time and computing resources.

This package provides an interface to Mapzen'-based APIs (including geocode.earth, Nextzen, and NYC GeoSearch) for geographic search and geocoding, isochrone calculation, and vector data to draw map tiles. See <https://www.mapzen.com/documentation/> for more information. The original Mapzen has gone out of business, but rmapzen can be set up to work with any provider who implements the Mapzen API.

An implementation of robust bent line regression. It can fit the bent line regression and test the existence of change point, for the paper, "Feipeng Zhang and Qunhua Li (2016). Robust bent line regression, submitted.".

This package provides functions for simulating Markov chains using the Barker proposal to compute Markov chain Monte Carlo (MCMC) estimates of expectations with respect to a target distribution on a real-valued vector space. The Barker proposal, described in Livingstone and Zanella (2022) <doi:10.1111/rssb.12482>, is a gradient-based MCMC algorithm inspired by the Barker accept-reject rule. It combines the robustness of simpler MCMC schemes, such as random-walk Metropolis, with the efficiency of gradient-based methods, such as the Metropolis adjusted Langevin algorithm. The key function provided by the package is sample_chain(), which allows sampling a Markov chain with a specified target distribution as its stationary distribution. The chain is sampled by generating proposals and accepting or rejecting them using a Metropolis-Hasting acceptance rule. During an initial warm-up stage, the parameters of the proposal distribution can be adapted, with adapters available to both: tune the scale of the proposals by coercing the average acceptance rate to a target value; tune the shape of the proposals to match covariance estimates under the target distribution. As well as the default Barker proposal, the package also provides implementations of alternative proposal distributions, such as (Gaussian) random walk and Langevin proposals. Optionally, if BridgeStan's R interface <https://roualdes.us/bridgestan/latest/languages/r.html>, available on GitHub <https://github.com/roualdes/bridgestan>, is installed, then BridgeStan can be used to specify the target distribution to sample from.

This package provides a parallel function for multivariate outlier detection named modified Stahel-Donoho estimators is contained in this package. The function RMSDp() is for elliptically distributed datasets and recognizes outliers based on Mahalanobis distance. This function is for higher dimensional datasets that cannot be handled by a single core function RMSD() included in RMSD package. See Wada and Tsubaki (2013) <doi:10.1109/CLOUDCOM-ASIA.2013.86> for the detail of the algorithm.

Integrates population dynamics and dispersal into a mechanistic virtual species simulator. The package can be used to study the effects of environmental change on population growth and range shifts. It allows for simple and straightforward definition of population dynamics (including positive density dependence), extensive possibilities for defining dispersal kernels, and the ability to generate virtual ecologist data. Learn more about the rangr at <https://docs.ropensci.org/rangr/>.

Terrestrial laser scanning (TLS) data processing and post-hurricane damage severity classification at the individual tree level using deep Learning. Further details were published in Klauberg et al. (2023) <doi:10.3390/rs15041165>.

Extract the implied risk neutral density from options using various methods.

Duplicated restaurant data (pre-processed and formatted) for entity resolution. This package contains formatted data from a data set that contains information about different restaurants, with the Zagats portion containing 331 records and the Fodors portion containing 533 records. The following variables are included in the data set: id, name, address, city, phone, type. The data set has a respective gold data set that provides information on which records match based on id.

An approach to age-depth modelling that uses Bayesian statistics to reconstruct accumulation histories for deposits, through combining radiocarbon and other dates with prior information on accumulation rates and their variability. See Blaauw & Christen (2011).

Discretize AR(1) process following Tauchen (1986) <http://www.sciencedirect.com/science/article/pii/0165176586901680>. A discrete Markov chain that approximates in the sense of weak convergence a continuous-valued univariate Autoregressive process of first order is generated. It is a popular method used in economics and in finance.

Reads in sample description and slide description files and annotates the expression values taken from GenePix results files (text file format used by many microarray scanner and software providers). After normalization data can be visualized as boxplot, heatmap or dotplot.

Mappable vector library provides convenient way to access large datasets. Use all of your data at once, with few limits. Memory mapped data can be shared between multiple R processes. Access speed depends on storage medium, so solid state drive is recommended, preferably with PCI Express (or M.2 nvme) interface or a fast network file system. The data is memory mapped into R and then accessed using usual R list and array subscription operators. Convenience functions are provided for merging, grouping and indexing large vectors and data.frames. The layout of underlying MVL files is optimized for large datasets. The vectors are stored to guarantee alignment for vector intrinsics after memory map. The package is built on top of libMVL, which can be used as a standalone C library. libMVL has simple C API making it easy to interchange datasets with outside programs. Large MVL datasets are distributed via Academic Torrents <https://academictorrents.com/collection/mvl-datasets>.

Function for generating random gender and ethnicity correct first and/or last names. Names are chosen proportionally based upon their probability of appearing in a large scale data base of real names.

Fully DBI'-compliant interface to Greenplum <https://greenplum.org/>, an open-source parallel database. This is an extension of the RPostgres package <https://github.com/r-dbi/RPostgres>.