Estimates the rank intraclass correlation coefficient (ICC) for clustered continuous and ordinal data. See Tu et al. (2023) <DOI:10.1002/sim.9864> for details.

Import data written in the JCAMP-DX format. This is an instrument-independent format used in the field of spectroscopy. Examples include IR, NMR, and Raman spectroscopy. See the vignette for background and supported formats. The official JCAMP-DX site is <http://www.jcamp-dx.org/>.

Bindings to kernel methods for enforcing security restrictions. AppArmor can apply mandatory access control (MAC) policies on a given task (process) via security profiles with detailed ACL definitions. In addition this package implements bindings for setting process resource limits (rlimit), uid, gid, affinity and priority. The high level R function eval.secure builds on these methods to perform dynamic sandboxing: it evaluates a single R expression within a temporary fork which acts as a sandbox by enforcing fine grained restrictions without affecting the main R process. A portable version of this function is now available in the unix package.

R-based access to a large set of data variables relevant to forest ecology in British Columbia (BC), Canada. Layers are in raster format at 100m resolution in the BC Albers projection, hosted at the Federated Research Data Repository (FRDR) with <doi:10.20383/101.0283>. The collection includes: elevation; biogeoclimatic zone; wildfire; cutblocks; forest attributes from Hansen et al. (2013) <doi:10.1139/cjfr-2013-0401> and Beaudoin et al. (2017) <doi:10.1139/cjfr-2017-0184>; and rasterized Forest Insect and Disease Survey (FIDS) maps for a number of insect pest species, all covering the period 2001-2018. Users supply a polygon or point location in the province of BC, and rasterbc will download the overlapping raster tiles hosted at FRDR, merging them as needed and returning the result in R as a SpatRaster object. Metadata associated with these layers, and code for downloading them from their original sources can be found in the github repository <https://github.com/deankoch/rasterbc_src>.

Standard and extensible Eddy-Covariance data post-processing (Wutzler et al. (2018) <doi:10.5194/bg-15-5015-2018>) includes uStar-filtering, gap-filling, and flux-partitioning. The Eddy-Covariance (EC) micrometeorological technique quantifies continuous exchange fluxes of gases, energy, and momentum between an ecosystem and the atmosphere. It is important for understanding ecosystem dynamics and upscaling exchange fluxes. (Aubinet et al. (2012) <doi:10.1007/978-94-007-2351-1>). This package inputs pre-processed (half-)hourly data and supports further processing. First, a quality-check and filtering is performed based on the relationship between measured flux and friction velocity (uStar) to discard biased data (Papale et al. (2006) <doi:10.5194/bg-3-571-2006>). Second, gaps in the data are filled based on information from environmental conditions (Reichstein et al. (2005) <doi:10.1111/j.1365-2486.2005.001002.x>). Third, the net flux of carbon dioxide is partitioned into its gross fluxes in and out of the ecosystem by night-time based and day-time based approaches (Lasslop et al. (2010) <doi:10.1111/j.1365-2486.2009.02041.x>).

Facilitating the creation of reproducible statistical report templates. Once created, rapport templates can be exported to various external formats (HTML, LaTeX, PDF, ODT etc.) with pandoc as the converter backend.

This package provides functions to convert an R colour specification to a colour name. The user can select and create different lists of colour names and different colour metrics for the conversion.

R package for creating, manipulating and reading RO-Crates. Latest supported version of the specification: <https://w3id.org/ro/crate/1.2/>.

Interoperability between Rcpp and the C++11 array and tuple types. Linking to this package allows fixed-length std::array objects to be converted to and from equivalent R vectors, and std::tuple objects converted to lists, via the as() and wrap() functions. There is also experimental support for std::span from C++20'.

Loads Blackrock <https://blackrockneurotech.com> neural signal data files into the memory, provides utility tools to extract the data into common formats such as plain-text tsv and HDF5'.

The RMM fits Revenue Management Models using the RDE(Robust Demand Estimation) method introduced in the paper by <doi:10.2139/ssrn.3598259>, one of the customer choice-based Revenue Management Model. Furthermore, it is possible to select a multinomial model as well as a conditional logit model as a model of RDE.

This package provides functions for studying realized genetic relatedness between people. Users will be able to simulate inheritance patterns given pedigree structures, generate SNP marker data given inheritance patterns, and estimate realized relatedness between pairs of individuals using SNP marker data. See Wang (2017) <doi:10.1534/genetics.116.197004>. This work was supported by National Institutes of Health grants R37 GM-046255.

Sample size and confidence interval calculations in reversible catalytic models, with applications in malaria research. Further details can be found in the paper by Sepúlveda and Drakeley (2015, <doi:10.1186/s12936-015-0661-z>).

Utilities for processing input and output files associated with the Raven Hydrological Modelling Framework. Includes various plotting functions, model diagnostics, reading output files into extensible time series format, and support for writing Raven input files. The RavenR package is also archived at Chlumsky et al. (2020) <doi:10.5281/zenodo.4248183>. The Raven Hydrologic Modelling Framework method can be referenced with Craig et al. (2020) <doi:10.1016/j.envsoft.2020.104728>.

Connector to the REST API of a Rock R server, to perform operations on a remote R server session, or administration tasks. See Rock documentation at <https://rockdoc.obiba.org/>.

This is a Google Forms and Google Classroom API Wrapper for R for managing Google Classrooms from R. The documentation for these APIs is here <https://developers.google.com/forms/api/guides> .

Export all data, including metadata, from a REDCap (Research Electronic Data Capture) Project via the REDCap API <https://projectredcap.org/wp-content/resources/REDCapTechnicalOverview.pdf>. The exported (meta)data will be processed and formatted into a stand alone R data package which can be installed and shared between researchers. Several default reports are generated as vignettes in the resulting package.

Collection of functions designed to compute risk-based portfolios as described in Ardia et al. (2017) <doi:10.1007/s10479-017-2474-7> and Ardia et al. (2017) <doi:10.21105/joss.00171>.

The rema package implements a permutation-based approach for binary meta-analyses of 2x2 tables, founded on conditional logistic regression, that provides more reliable statistical tests when heterogeneity is observed in rare event data (Zabriskie et al. 2021 <doi:10.1002/sim.9142>). To adjust for the effect of heterogeneity, this method conditions on the sufficient statistic of a proxy for the heterogeneity effect as opposed to estimating the heterogeneity variance. While this results in the model not strictly falling under the random-effects framework, it is akin to a random-effects approach in that it assumes differences in variability due to treatment. Further, this method does not rely on large-sample approximations or continuity corrections for rare event data. This method uses the permutational distribution of the test statistic instead of asymptotic approximations for inference. The number of observed events drives the computation complexity for creating this permutational distribution. Accordingly, for this method to be computationally feasible, it should only be applied to meta-analyses with a relatively low number of observed events. To create this permutational distribution, a network algorithm, based on the work of Mehta et al. (1992) <doi:10.2307/1390598> and Corcoran et al. (2001) <doi:10.1111/j.0006-341x.2001.00941.x>, is employed using C++ and integrated into the package.

Enhances the R Optimization Infrastructure ('ROI') package by registering the ipop solver from package kernlab'.

This package provides a collection of HTML', JavaScript', CSS and fonts assets that generate RapiDoc documentation from an OpenAPI Specification: <https://mrin9.github.io/RapiDoc/>.

An expansion of R's stats random wishart matrix generation. This package allows the user to generate singular, Uhlig and Harald (1994) <doi:10.1214/aos/1176325375>, and pseudo wishart, Diaz-Garcia, et al.(1997) <doi:10.1006/jmva.1997.1689>, matrices. In addition the user can generate wishart matrices with fractional degrees of freedom, Adhikari (2008) <doi:10.1061/(ASCE)0733-9399(2008)134:12(1029)>, commonly used in volatility modeling. Users can also use this package to create random covariance matrices.

Create an R Journal Rmarkdown template article, that will generate html and pdf versions of your paper. Check that the paper folder has all the required components needed for submission. Examples of R Journal publications can be found at <https://journal.r-project.org>.

Collection of functions for fitting distributions to given data or by known quantiles. Two main functions fit.perc() and fit.cont() provide users a GUI that allows to choose a most appropriate distribution without any knowledge of the R syntax. Note, this package is a part of the rrisk project.