This package provides a convenient way of accessing data published by the Reserve Bank of New Zealand (RBNZ) on their website, <https://www.rbnz.govt.nz/statistics>. A range of financial and economic data is provided in spreadsheet format including exchange and interest rates, commercial lending statistics, Reserve Bank market operations, financial institution statistics, household financial data, New Zealand debt security information, and economic indicators. This package provides a method to download those spreadsheets and read them directly into R.

Downloads and parses SDF (Structural Description Format) and PDB (Protein Database) files for 3D rendering.

BEAST is a Bayesian estimator of abrupt change, seasonality, and trend for decomposing univariate time series and 1D sequential data. Interpretation of time series depends on model choice; different models can yield contrasting or contradicting estimates of patterns, trends, and mechanisms. BEAST alleviates this by abandoning the single-best-model paradigm and instead using Bayesian model averaging over many competing decompositions. It detects and characterizes abrupt changes (changepoints, breakpoints, structural breaks, joinpoints), cyclic or seasonal variation, and nonlinear trends. BEAST not only detects when changes occur but also quantifies how likely the changes are true. It estimates not just piecewise linear trends but also arbitrary nonlinear trends. BEAST is generically applicable to any real-valued time series, such as those from remote sensing, economics, climate science, ecology, hydrology, and other environmental and biological systems. Example applications include identifying regime shifts in ecological data, mapping forest disturbance and land degradation from satellite image time series, detecting market trends in economic indicators, pinpointing anomalies and extreme events in climate records, and analyzing system dynamics in biological time series. Details are given in Zhao et al. (2019) <doi:10.1016/j.rse.2019.04.034>.

Interface to SWI'-'Prolog', <https://www.swi-prolog.org/>. This package is normally not loaded directly, please refer to package rolog instead. The purpose of this package is to provide the Prolog runtime on systems that do not have a software installation of SWI'-'Prolog'.

Creating 3D radial visualizations of multivariate data. The package extends traditional radial coordinate visualization (RadViz) techniques to three-dimensional space, enabling enhanced exploration and analysis of high-dimensional datasets through interactive 3D plots. Zhu, Dai & Maitra (2022) <doi:10.1080/10618600.2021.2020129>.

Functionality for performing a principled reference analysis in the Bayesian normal-normal hierarchical model used for Bayesian meta-analysis, as described in Ott, Plummer and Roos (2021) <doi:10.1002/sim.9076>. Computes a reference posterior, induced by a minimally informative improper reference prior for the between-study (heterogeneity) standard deviation. Determines additional proper anti-conservative (and conservative) prior benchmarks. Includes functions for reference analyses at both the posterior and the prior level, which, given the data, quantify the informativeness of a heterogeneity prior of interest relative to the minimally informative reference prior and the proper prior benchmarks. The functions operate on data sets which are compatible with the bayesmeta package.

An R interface to the typeform <https://www.typeform.com/> application program interface. Also provides functions for downloading your results.

Computes word, character, and non-whitespace character counts in R Markdown documents and Jupyter notebooks, with or without code chunks. Returns results as a data frame.

R Interface to JDemetra+ 3.x (<https://github.com/jdemetra>) time series analysis software. It provides functions allowing to model time series (create outlier regressors, user-defined calendar regressors, Unobserved Components AutoRegressive Integrated Moving Average (UCARIMA) models...), to test the presence of trading days or seasonal effects and also to set specifications in pre-adjustment and benchmarking when using rjd3x13 or rjd3tramoseats'.

Converts elements of roxygen documentation to markdown'.

Fits linear models with endogenous regressor using latent instrumental variable approaches. The methods included in the package are Lewbel's (1997) <doi:10.2307/2171884> higher moments approach as well as Lewbel's (2012) <doi:10.1080/07350015.2012.643126> heteroscedasticity approach, Park and Gupta's (2012) <doi:10.1287/mksc.1120.0718> joint estimation method that uses Gaussian copula and Kim and Frees's (2007) <doi:10.1007/s11336-007-9008-1> multilevel generalized method of moment approach that deals with endogeneity in a multilevel setting. These are statistical techniques to address the endogeneity problem where no external instrumental variables are needed. See the publication related to this package in the Journal of Statistical Software for more details: <doi:10.18637/jss.v107.i03>. Note that with version 2.0.0 sweeping changes were introduced which greatly improve functionality and usability but break backwards compatibility.

This package provides a fast calculation of the Blyth-Still-Casella confidence interval. The implementation follows the StatXact 9 manual (Cytel 2010) and "Refining Binomial Confidence Intervals" by George Casella (1986) <doi:10.2307/3314658>.

Test Statistics for Independence in High-Dimensional Datasets. This package consists of two functions to perform the complete independence test based on test statistics proposed by Bulut (unpublished yet) and suggested by Najarzadeh (2021) <doi: 10.1080/03610926.2019.1702699>. The Bulut's statistic is not sensitive to outliers in high-dimensional data, unlike one of Najarzadeh (2021) <doi: 10.1080/03610926.2019.1702699>. So, the Bulut's statistic can be performed robustly by using RDnp function.

Calculates I30 (maximum 30-minute rainfall intensity) and EI30 (erosivity index) from rainfall breakpoint data. Supports multiple storm events, rainfall validation, and visualization for soil erosion modeling and hydrological analysis. Methods are based on Brown and Foster (1987) <doi:10.13031/2013.30422>, Wischmeier and Smith (1978) "Predicting Rainfall Erosion Losses: A Guide to Conservation Planning" <doi:10.22004/ag.econ.171903>, and Renard et al. (1997) "Predicting Soil Erosion by Water: A Guide to Conservation Planning with the Revised Universal Soil Loss Equation (RUSLE)" (USDA Agriculture Handbook No. 703).

This package provides functions to read and write ImageJ (<https://imagej.net>) Region of Interest (ROI) files, to plot the ROIs and to convert them to spatstat (<https://spatstat.org/>) spatial patterns.

This package contains various tools to estimate, analyze, and visualize Response Item Networks. ResIN dummy-codes ordered and qualitative response choices from (survey) data, calculates pairwise associations and maps the location of each item response as a node in a force-directed network. Please refer to <https://www.resinmethod.net/> for more details.

Provide helper functions for package developers to create active bindings that looks like data embedded in the package, but are downloaded from remote sources.

Robust likelihood cross validation bandwidth for uni- and multi-variate kernel densities. It is robust against fat-tailed distributions and/or outliers. Based on "Robust Likelihood Cross-Validation for Kernel Density Estimation," Wu (2019) <doi:10.1080/07350015.2018.1424633>.

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.

Imports real-time thermo cycler (qPCR) data from Real-time PCR Data Markup Language (RDML) and transforms to the appropriate formats of the qpcR and chipPCR packages, as described in Rodiger et al. (2017) <doi:10.1093/bioinformatics/btx528>. Contains a dendrogram visualization for the structure of RDML object and GUI for RDML editing.

This package provides a custom implementation of the apriori algorithm and binomial tests to identify combinations of features (genes, variants etc) significantly enriched for simultaneous mutations/events from sparse Boolean input, see Vijay Kumar Pounraja, Santhosh Girirajan (2021). Version 1.1 includes a minor adjustment to the number of combinations to be considered for multiple testing correction. This updated version is more conservative in its approach and hence more selective. <doi:10.1101/2021.10.01.462832>.

Real-time quantitative polymerase chain reaction (qPCR) data by Rutledge et al. (2004) <doi:10.1093/nar/gnh177> in tidy format. The data comprises a six-point, ten-fold dilution series, repeated in five independent runs, for two different amplicons. In each run, each standard concentration is replicated four times. For the original raw data file see the Supplementary Data section: <https://academic.oup.com/nar/article/32/22/e178/2375678#supplementary-data>.

This package provides a set of tools to streamline data analysis. Learning both R and introductory statistics at the same time can be challenging, and so we created rigr to facilitate common data analysis tasks and enable learners to focus on statistical concepts. We provide easy-to-use interfaces for descriptive statistics, one- and two-sample inference, and regression analyses. rigr output includes key information while omitting unnecessary details that can be confusing to beginners. Heteroscedasticity-robust ("sandwich") standard errors are returned by default, and multiple partial F-tests and tests for contrasts are easy to specify. A single regression function can fit both linear and generalized linear models, allowing students to more easily make connections between different classes of models.

The goal of rFIA is to increase the accessibility and use of the United States Forest Services (USFS) Forest Inventory and Analysis (FIA) Database by providing a user-friendly, open source toolkit to easily query and analyze FIA Data. Designed to accommodate a wide range of potential user objectives, rFIA simplifies the estimation of forest variables from the FIA Database and allows all R users (experts and newcomers alike) to unlock the flexibility inherent to the Enhanced FIA design. Specifically, rFIA improves accessibility to the spatial-temporal estimation capacity of the FIA Database by producing space-time indexed summaries of forest variables within user-defined population boundaries. Direct integration with other popular R packages (e.g., dplyr', tidyr', and sf') facilitates efficient space-time query and data summary, and supports common data representations and API design. The package implements design-based estimation procedures outlined by Bechtold & Patterson (2005) <doi:10.2737/SRS-GTR-80>, and has been validated against estimates and sampling errors produced by FIA EVALIDator'. Current development is focused on the implementation of spatially-enabled model-assisted and model-based estimators to improve population, change, and ratio estimates.