This package provides a set of functions designed to calculate the standardised precipitation and standardised precipitation evapotranspiration indices using NASA POWER data as described in Blain et al. (2023) <doi:10.2139/ssrn.4442843>. These indices are calculated using a reference data source. The functions verify if the indices estimates meet the assumption of normality and how well NASA POWER estimates represent real-world data. Indices are calculated in a routine mode. Potential evapotranspiration amounts and the difference between rainfall and potential evapotranspiration are also calculated. The functions adopt a basic time scale that splits each month into four periods. Days 1 to 7, days 8 to 14, days 15 to 21, and days 22 to 28, 29, 30, or 31, where TS=4 corresponds to a 1-month length moving window (calculated 4 times per month) and TS=48 corresponds to a 12-month length moving window (calculated 4 times per month).

This package provides a collection of miscellaneous functions for passive acoustics. Much of the content here is adapted to R from code written by other people. If you have any ideas of functions to add, please contact Taiki Sakai.

Spectral response data for broadband ultraviolet and visible radiation sensors. Angular response data for broadband ultraviolet and visible radiation sensors and diffusers used as entrance optics. Data obtained from multiple sources were used: author-supplied data from scientific research papers, sensor-manufacturer supplied data, and published sensor specifications. Part of the r4photobiology suite Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.

This package provides a robust approach for omics data integration and disease subtyping. PINSPlus is fast and supports the analysis of large datasets with hundreds of thousands of samples and features. The software automatically determines the optimal number of clusters and then partitions the samples in a way such that the results are robust against noise and data perturbation (Nguyen et al. (2019) <DOI: 10.1093/bioinformatics/bty1049>, Nguyen et al. (2017)<DOI: 10.1101/gr.215129.116>, Nguyen et al. (2021)<DOI: 10.3389/fonc.2021.725133>).

Interactive shiny application for working with Probability Distributions. Calculations and Graphs are provided.

Learn optimal policies via doubly robust empirical welfare maximization over trees. Given doubly robust reward estimates, this package finds a rule-based treatment prescription policy, where the policy takes the form of a shallow decision tree that is globally (or close to) optimal.

This package provides a broad-view perspective on data via linear mapping of data onto a radial coordinate system. The package contains functions to visualize the residual values of linear regression and Cartesian data in the defined radial scheme. See the pacviz documentation page for more information: <https://pacviz.sriley.dev/>.

This package provides tools for loading and processing passive acoustic data. Read in data that has been processed in Pamguard (<https://www.pamguard.org/>), apply a suite processing functions, and export data for reports or external modeling tools. Parameter calculations implement methods by Oswald et al (2007) <doi:10.1121/1.2743157>, Griffiths et al (2020) <doi:10.1121/10.0001229> and Baumann-Pickering et al (2010) <doi:10.1121/1.3479549>.

This package provides wrapper functions to access the ProPublica's Congress and Campaign Finance APIs. The Congress API provides near real-time access to legislative data from the House of Representatives, the Senate and the Library of Congress. The Campaign Finance API provides data from United States Federal Election Commission filings and other sources. The API covers summary information for candidates and committees, as well as certain types of itemized data. For more information about these APIs go to: <https://www.propublica.org/datastore/apis>.

This package provides functions to perform Bayesian inference on absorption time data for Phase-type distributions. The methods of Bladt et al (2003) <doi:10.1080/03461230110106435> and Aslett (2012) <https://www.louisaslett.com/PhD_Thesis.pdf> are provided.

Set the R prompt dynamically, from a function. The package contains some examples to include various useful dynamic information in the prompt: the status of the last command (success or failure); the amount of memory allocated by the current R process; the name of the R package(s) loaded by pkgload and/or devtools'; various git information: the name of the active branch, whether it is dirty, if it needs pushes pulls. You can also create your own prompt if you don't like the predefined examples.

Statically determine and visualize the function dependencies within and across packages. This may be useful for managing function dependencies across a code base of multiple R packages.

Improved methods to construct prediction intervals for network meta-analysis. The parametric bootstrap and Kenward-Roger-type adjustment by Noma et al. (2022) <forthcoming> are implementable.

Programmatic interface to the PhenoCam web services (<https://phenocam.nau.edu/webcam>). Allows for easy downloading of PhenoCam data directly to your R workspace or your computer and provides post-processing routines for consistent and easy timeseries outlier detection, smoothing and estimation of phenological transition dates. Methods for this package are described in detail in Hufkens et. al (2018) <doi:10.1111/2041-210X.12970>.

Perform flexible simulation studies using one or multiple computer cores. The package is set up to be usable on high-performance clusters in addition to being run locally, see examples on <https://github.com/SachaEpskamp/parSim>.

Routines for two different test types, the Constant Conditional Correlation (CCC) test and the Vectorial Independence (VI) test are provided (Kurz and Spanhel (2022) <doi:10.1214/22-EJS2051>). The tests can be applied to check whether a conditional copula coincides with its partial copula. Functions to test whether a regular vine copula satisfies the so-called simplifying assumption or to test a single copula within a regular vine copula to be a (j-1)-th order partial copula are available. The CCC test comes with a decision tree approach to allow testing in high-dimensional settings.

This package provides a set of Analysis Data Model (ADaM) datasets constructed using the Study Data Tabulation Model (SDTM) datasets contained in the pharmaversesdtm package and the template scripts from the admiral family of packages. ADaM dataset specifications are described in the CDISC ADaM implementation guide, accessible by creating a free account on <https://www.cdisc.org/>.

This package implements fast, safe, and customizable assertions routines, which can be used in place of base::stopifnot().

Automated backtesting of multiple portfolios over multiple datasets of stock prices in a rolling-window fashion. Intended for researchers and practitioners to backtest a set of different portfolios, as well as by a course instructor to assess the students in their portfolio design in a fully automated and convenient manner, with results conveniently formatted in tables and plots. Each portfolio design is easily defined as a function that takes as input a window of the stock prices and outputs the portfolio weights. Multiple portfolios can be easily specified as a list of functions or as files in a folder. Multiple datasets can be conveniently extracted randomly from different markets, different time periods, and different subsets of the stock universe. The results can be later assessed and ranked with tables based on a number of performance criteria (e.g., expected return, volatility, Sharpe ratio, drawdown, turnover rate, return on investment, computational time, etc.), as well as plotted in a number of ways with nice barplots and boxplots.

Reproducible, programmatic retrieval of survey datasets from the Pew Research Center.

This package provides some easy-to-use functions for time series analyses of (plant-) phenological data sets. These functions mainly deal with the estimation of combined phenological time series and are usually wrappers for functions that are already implemented in other R packages adapted to the special structure of phenological data and the needs of phenologists. Some date conversion functions to handle Julian dates are also provided.

Bayesian toolbox for quantitative proteomics. In particular, this package provides functions to generate synthetic datasets, execute Bayesian differential analysis methods, and display results as, described in the associated article Marie Chion and Arthur Leroy (2023) <arXiv:2307.08975>.

Computes the exact probability density function of X/Y conditioned on positive quadrant for series of bivariate distributions,for more details see Nadarajah,Song and Si (2019) <DOI:10.1080/03610926.2019.1576893>.

Data analysis for Project Risk Management via the Second Moment Method, Monte Carlo Simulation, Contingency Analysis, Sensitivity Analysis, Earned Value Management, Learning Curves, Design Structure Matrices, and more.