Yandex Clickhouse (<https://clickhouse.com/>) is a high-performance relational column-store database to enable big data exploration and analytics scaling to petabytes of data. Methods are provided that enable working with Yandex Clickhouse databases via DBI methods and using dplyr'/'dbplyr idioms.

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.

This package provides read and write support for the Conic Benchmark Format (CBF, version 4) within the R Optimization Infrastructure ('ROI'). Supported cone types include the positive orthant, second-order (SOC), rotated second-order (bridged automatically to standard SOC), exponential (primal and dual), power (primal and dual), and semidefinite (symmetric-vectorised) cones, as well as their mixed-integer variants. The reader translates a .cbf file into an ROI OP object, handling coordinate-convention differences between CBF and ROI transparently; the writer serialises an ROI OP object back to CBF plain-text.

This package provides a lightweight, table-driven engine for executing insurance rating plans, including coverage-specific rating specifications, entity aggregation, and trace output for auditing. Given policy data, an ordered rating specification, and rating factor tables, it returns rated policies and, optionally, a step-by-step trace of the calculation.

In data science, it is a common practice to compute a series of columns (e.g. features) against a common response vector. Various metrics are provided with efficient computation implemented with Rcpp'.

Build regular expressions piece by piece using human readable code. This package contains core functionality, and is primarily intended to be used by package developers.

Extends R Commander with a unified menu of new and pre-existing statistical functions related to public management and policy analysis statistics. Functions and menus have been renamed according to the usage in PMGT 630 in the Master of Public Administration program at Brigham Young University.

It helps you to read (.dim) images with CRS directly into R programming. One can import both Sentinel 1 and 2 images or any processed data with this software.

Parse scientific names using gnparser (<https://github.com/gnames/gnparser>), written in Go. gnparser parses scientific names into their component parts; it utilizes a Parsing Expression Grammar specifically for scientific names.

Parser generator for R using combinatory parsers. It is inspired by combinatory parsers developed in Haskell.

Supports modelling case data to facilitate. The package provides automated computational grid generation over an area of interest with methods to map covariates between geographies, model fitting including spatially aggregated case counts, and predictions and visualisation. Monte Carlo maximum likelihood is the main fitting method with a low-rank approximation for Gaussian processes described by Solin and Särkkä (2020) <doi:10.1007/s11222-019-09886-w> and a stochastic partial differential equation approximation. Bayesian methods are also provided for some methods. Log-Gaussian Cox Processes are described by Diggle et al. (2013) <doi:10.1214/13-STS441>.

Multiscale Curvature Classification of ground returns in 3-D LiDAR point clouds, designed for forested environments. RMCC is a porting to R of the MCC-lidar method by Evans and Hudak (2007) <doi:10.1109/TGRS.2006.890412>.

Designed to support the application of plant trait data providing easy applicable functions for the basic steps of data preprocessing, e.g. data import, data exploration, selection of columns and rows, excluding trait data according to different attributes, geocoding, long- to wide-table transformation, and data export. rtry was initially developed as part of the TRY R project to preprocess trait data received via the TRY database.

This package performs comparative bioavailability calculations for Average Bioequivalence with Expanding Limits (ABEL). Implemented are Method A / Method B and the detection of outliers. If the design allows, assessment of the empiric Type I Error and iteratively adjusting alpha to control the consumer risk. Average Bioequivalence - optionally with a tighter (narrow therapeutic index drugs) or wider acceptance range (South Africa: Cmax) - is implemented as well.

This package provides a collection of randomization tests, data sets and examples. The current version focuses on five testing problems and their implementation in empirical work. First, it facilitates the empirical researcher to test for particular hypotheses, such as comparisons of means, medians, and variances from k populations using robust permutation tests, which asymptotic validity holds under very weak assumptions, while retaining the exact rejection probability in finite samples when the underlying distributions are identical. Second, the description and implementation of a permutation test for testing the continuity assumption of the baseline covariates in the sharp regression discontinuity design (RDD) as in Canay and Kamat (2018) <https://goo.gl/UZFqt7>. More specifically, it allows the user to select a set of covariates and test the aforementioned hypothesis using a permutation test based on the Cramer-von Misses test statistic. Graphical inspection of the empirical CDF and histograms for the variables of interest is also supported in the package. Third, it provides the practitioner with an effortless implementation of a permutation test based on the martingale decomposition of the empirical process for testing for heterogeneous treatment effects in the presence of an estimated nuisance parameter as in Chung and Olivares (2021) <doi:10.1016/j.jeconom.2020.09.015>. Fourth, this version considers the two-sample goodness-of-fit testing problem under covariate adaptive randomization and implements a permutation test based on a prepivoted Kolmogorov-Smirnov test statistic. Lastly, it implements an asymptotically valid permutation test based on the quantile process for the hypothesis of constant quantile treatment effects in the presence of an estimated nuisance parameter.

Reads tabular data from xlsx files with a specialized C parser. Worksheet XML is scanned in a single pass and decoded directly into R vectors, with no intermediate document model. Bundles the miniz and libdeflate decompressors to read the underlying archive.

Variational flow-based methods for modeling rare events using Kullbackâ Leibler (KL) divergence, normalizing flows, Girsanov change of measure, and Freidlinâ Wentzell action functionals. The package provides tools for rare-event inference, minimum-action paths, and quasi-potential computation in stochastic dynamical systems. Methods are based on Rezende and Mohamed (2015) <doi:10.48550/arXiv.1505.05770>, Girsanov (1960) <doi:10.1137/1105027>, and Freidlin and Wentzell (2012, ISBN:978-0387955477).

Feature selection and clustering classification under the presence of multivariate outliers in high-dimensional unlabeled data.

Rasterize images using a 3D software renderer. 3D scenes are created either by importing external files, building scenes out of the included objects, or by constructing meshes manually. Supports point and directional lights, anti-aliased lines, shadow mapping, transparent objects, translucent objects, multiple materials types, reflection, refraction, environment maps, multicore rendering, bloom, tone-mapping, and screen-space ambient occlusion.

Enables the use of color palettes inspired by the Dune movies. These palettes are compatible with ggplot2'. See Wickham (2016) <doi:10.1007/978-3-319-24277-4> for more details on ggplot2'.

This package performs Principal Components Analysis (also known as PCA) dimensionality reduction in the context of a linear regression. In most cases, PCA dimensionality reduction is performed independent of the response variable for a regression. This captures the majority of the variance of the model's predictors, but may not actually be the optimal dimensionality reduction solution for a regression against the response variable. An alternative method, optimized for a regression against the response variable, is to use both PCA and a relative importance measure. This package applies PCA to a given data frame of predictors, and then calculates the relative importance of each PCA factor against the response variable. It outputs ordered factors that are optimized for model fit. By performing dimensionality reduction with this method, an individual can achieve a the same r-squared value as performing just PCA, but with fewer PCA factors. References: Yuri Balasanov (2017) <https://ilykei.com>.

An integrated package for constructing random forest prediction intervals using a fast implementation package ranger'. This package can apply the following three methods described in Haozhe Zhang, Joshua Zimmerman, Dan Nettleton, and Daniel J. Nordman (2019) <doi:10.1080/00031305.2019.1585288>: the out-of-bag prediction interval, the split conformal method, and the quantile regression forest.

Data sets are often corrupted by outliers. When data are multivariate outliers can be classified as case-wise or cell-wise. The latters are particularly challenge to handle. We implement a robust estimation procedure for Seemingly Unrelated Regression Models which is able to cope well with both type of outliers. Giovanni Saraceno, Fatemah Alqallaf, Claudio Agostinelli (2021) <doi:10.48550/arXiv.2107.00975>.

R access to the FOAAS (F... Off As A Service) web service is provided.