This package provides functions for implementing robust methods for functional linear regression. In the functional linear regression, we consider scalar-on-function linear regression and function-on-function linear regression.

This package provides tools for large, sparse optimal matching of treated units and control units in observational studies. Provisions are made for refined covariate balance constraints, which include fine and near-fine balance as special cases. Matches are optimal in the sense that they are computed as solutions to network optimization problems rather than greedy algorithms. See Pimentel, et al.(2015) <doi:10.1080/01621459.2014.997879> and Pimentel (2016), Obs. Studies 2(1):4-23. The rrelaxiv package, which provides an alternative solver for the underlying network flow problems, carries an academic license and is not available on CRAN, but may be downloaded from Github at <https://github.com/josherrickson/rrelaxiv/>.

This package provides R functions to selectively rasterize components of grid output.

This package provides a Java implementation of the RAKE algorithm ('Rose', S., Engel', D., Cramer', N. and Cowley', W. (2010) <doi:10.1002/9780470689646.ch1>), which can be used to extract keywords from documents without any training data.

Query functions to the GPlates <https://www.gplates.org/> Desktop Application and the GPlates Web Service <https://gws.gplates.org/> allow users to reconstruct past positions of geographic entities based on user-selected rotation models without leaving the R running environment. The online method (GPlates Web Service) makes the rotation of static plates, coastlines, and a low number of geographic coordinates available using nothing but an internet connection. The offline method requires an external installation of the GPlates Desktop Application, but allows the efficient batch rotation of thousands of coordinates, Simple Features (sf) and Spatial (sp) objects with custom reconstruction trees and partitioning polygons. Examples of such plate tectonic models are accessible via the chronosphere <https://cran.r-project.org/package=chronosphere>. This R extension is developed under the umbrella of the DFG (Deutsche Forschungsgemeinschaft) Research Unit TERSANE2 (For 2332, TEmperature Related Stressors in ANcient Extinctions).

Interface around JDemetra+ (<https://github.com/jdemetra/jdemetra-app>), the seasonal adjustment software officially recommended to the members of the European Statistical System (ESS) and the European System of Central Banks. It offers full access to all options and outputs of JDemetra+', including the two leading seasonal adjustment methods TRAMO/SEATS+ and X-12ARIMA/X-13ARIMA-SEATS.

This package provides functions to calculate Sample Number and Average Sample Number for Repetitive Group Sampling Plan Based on Cpk as given in Aslam et al. (2013) (<DOI:10.1080/00949655.2012.663374>).

Generate random positions (latitude/longitude), Well-known text ('WKT') points or polygons, or GeoJSON points or polygons.

This package provides formatting linting to roxygen2 tags. Linters report roxygen2 tags that do not conform to a standard style. These linters can be a helpful check for building more consistent documentation and to provide reminders about best practices or checks for typos. Default linting suites are provided for common style guides such as the one followed by the tidyverse', though custom linters can be registered by other packages or be custom-tailored to a specific package.

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>.

This package implements the Zig-Zag algorithm (Bierkens, Fearnhead, Roberts, 2016) <arXiv:1607.03188> applied and Bouncy Particle Sampler <arXiv:1510.02451> for a Gaussian target and Student distribution.

Analyze multi-level one-way experimental designs where there are unequal sample sizes and population variance homogeneity can not be assumed. To conduct the Gabriel test <doi:10.2307/2286265>, create two vectors: one for your observations and one for the factor level of each observation. The function, rgabriel, conduct the test and save the output as a vector to input into the gabriel.plot function, which produces a confidence interval plot for Multiple Comparison.

Add-in to the RJDemetra package on seasonal adjustments. It allows to produce dashboards to summarise models and quickly check the quality of the seasonal adjustment.

The function RepaymentPlan() calculates repayment schedule for repayment/mortgage plans.

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.".

Implementation of the Johnson Quantile-Parameterised Distribution in R. The Johnson Quantile-Parameterised Distribution (J-QPD) is a flexible distribution system that is parameterised by a symmetric percentile triplet of quantile values (typically the 10th-50th-90th) along with known support bounds for the distribution. The J-QPD system was developed by Hadlock and Bickel (2017) <doi:10.1287/deca.2016.0343>. This package implements the density, quantile, CDF and random number generator functions.

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'.

This package provides a client for (1) querying the DHS API for survey indicators and metadata (<https://api.dhsprogram.com/#/index.html>), (2) identifying surveys and datasets for analysis, (3) downloading survey datasets from the DHS website, (4) loading datasets and associate metadata into R, and (5) extracting variables and combining datasets for pooled analysis.

Uses Elsevier Scopus API <https://dev.elsevier.com/sc_apis.html> to download information about authors and their citations.

Computes attributable effects based confidence interval, permutation test confidence interval, or asymptotic confidence interval for the average treatment effect on a binary outcome. Methods outlined in further detail in Rigdon and Hudgens (2015) <doi:10.1002/sim.6384>.

The method generate() is extended for spatial multi-site stochastic generation of daily precipitation. It generates precipitation occurrence in several sites using logit regression (Generalized Linear Models) and the approach by D.S. Wilks (1998) <doi:10.1016/S0022-1694(98)00186-3> .

Access to some of the C level functions of the xts package. In its current state, the package is mostly a proof-of-concept to support adding useful functions, and does not yet add any of its own.

This package provides a strong type system for R which supports symbol declaration and assignment with type checking and condition checking.

This package provides methods and tools for Singular Spectrum Analysis including decomposition, forecasting and gap-filling for univariate and multivariate time series. General description of the methods with many examples can be found in the book Golyandina (2018, <doi:10.1007/978-3-662-57380-8>). See citation("Rssa") for details.