This package implements methods described by the paper Robins and Tsiatis (1991) <DOI:10.1080/03610929108830654>. These use g-estimation to estimate the causal effect of a treatment in a two-armed randomised control trial where non-compliance exists and is measured, under an assumption of an accelerated failure time model and no unmeasured confounders.

Regression methods to quantify the relation between two measurement methods are provided by this package. The focus is on a Bayesian Deming regressions family. With a Bayesian method the Deming regression can be run in a traditional fashion or can be run in a robust way just decreasing the degree of freedom d.f. of the sampling distribution. With d.f. = 1 an extremely robust Cauchy distribution can be sampled. Moreover, models for dealing with heteroscedastic data are also provided. For reference see G. Pioda (2024) <https://piodag.github.io/bd1/>.

Create plots to visualize the alignment of a corporate lending financial portfolio to climate change scenarios based on climate indicators (production and emission intensities) across key climate relevant sectors of the PACTA methodology (Paris Agreement Capital Transition Assessment; <https://www.transitionmonitor.com/>). Financial institutions use PACTA to study how their capital allocation decisions align with climate change mitigation goals.

Plot rpart models. Extends plot.rpart() and text.rpart() in the rpart package.

Calculates and visualises speech rhythm and timing metrics. The rhythm.metrics package provides a standardised workflow to compute common metrics including Delta C, Delta V, VarcoC, VarcoV, the percentage of vocalic intervals (%V), and both raw and normalised Pairwise Variability Indices (rPVI, nPVI). It includes functions for calculating and visualising these measures to facilitate cross-linguistic and developmental rhythm research. Delta C, Delta V, and %V measures are based on Ramus et al. (1999) <doi:10.1016/S0010-0277(99)00058-X>; VarcoC and VarcoV measures are based on Dellwo (2006, ISBN: 9783631554777); and rPVI-C and nPVI-V are based on Grabe & Low (2002) <doi:10.1515/9783110197105.2.515>.

Diagnostics and data preparation for random effects within estimator, random effects within-idiosyncratic estimator, between-within-idiosyncratic model, and cross-classified between model. Mundlak, Yair (1978) <doi:10.2307/1913646>. Hausman, Jeffrey (1978) <doi:10.2307/1913827>. Allison, Paul (2009) <doi:10.4135/9781412993869>. Neuhaus, J.M., and J. D. Kalbfleisch (1998) <doi:10.2307/3109770>.

This package contains three functions that query AuriQ Systems Essentia Database and return the results in R. essQuery takes a single Essentia command and captures the output in R, where you can save the output to a dataframe or stream it directly into additional analysis. read.essentia takes an Essentia script and captures the output csv data into R, where you can save the output to a dataframe or stream it directly into additional analysis. capture.essentia takes a file containing any number of Essentia commands and captures the output of the specified statements into R dataframes. Essentia can be downloaded for free at http://www.auriq.com/documentation/source/install/index.html.

This package provides R and JavaScript functions to allow WebGL'-based 3D plotting using the three.js JavaScript library. Interactivity through roll-over highlighting and toggle buttons is also supported.

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>.

Estimation of both single- and multiple-assignment Regression Discontinuity Designs (RDDs). Provides both parametric (global) and non-parametric (local) estimation choices for both sharp and fuzzy designs, along with power analysis and assumption checks. Introductions to the underlying logic and analysis of RDDs are in Thistlethwaite, D. L., Campbell, D. T. (1960) <doi:10.1037/h0044319> and Lee, D. S., Lemieux, T. (2010) <doi:10.1257/jel.48.2.281>.

The implemented R6 class SCM aims to simplify working with structural causal models. The missing data mechanism can be defined as a part of the structural model. The class contains methods for 1) defining a structural causal model via functions, text or conditional probability tables, 2) printing basic information on the model, 3) plotting the graph for the model using packages igraph or qgraph', 4) simulating data from the model, 5) applying an intervention, 6) checking the identifiability of a query using the R packages causaleffect and dosearch', 7) defining the missing data mechanism, 8) simulating incomplete data from the model according to the specified missing data mechanism and 9) checking the identifiability in a missing data problem using the R package dosearch'. In addition, there are functions for running experiments and doing counterfactual inference using simulation.

These tools implement in R a fundamental part of the software PACTA (Paris Agreement Capital Transition Assessment), which is a free tool that calculates the alignment between financial portfolios and climate scenarios (<https://www.transitionmonitor.com/>). Financial institutions use PACTA to study how their capital allocation decisions align with climate change mitigation goals. This package matches data from corporate lending portfolios to asset level data from market-intelligence databases (e.g. power plant capacities, emission factors, etc.). This is the first step to assess if a financial portfolio aligns with climate goals.

Robust estimators for the beta regression, useful for modeling bounded continuous data. Currently, four types of robust estimators are supported. They depend on a tuning constant which may be fixed or selected by a data-driven algorithm also implemented in the package. Diagnostic tools associated with the fitted model, such as the residuals and goodness-of-fit statistics, are implemented. Robust Wald-type tests are available. More details about robust beta regression are described in Maluf et al. (2025) <doi:10.1007/s00184-024-00949-1>.

Assess LCâ MS system performance by visualizing instrument log files and monitoring raw quality control samples within a project.

Open any data frame with visidata', a terminal-based spreadsheet application <https://www.visidata.org>.

This package provides a straightforward model to estimate soil migration rates across various soil contexts. Based on the compartmental, vertically-resolved, physically-based mass balance model of Soto and Navas (2004) <doi:10.1016/j.jaridenv.2004.02.003> and Soto and Navas (2008) <doi:10.1016/j.radmeas.2008.02.024>. RadEro provides a user-friendly interface in R, utilizing input data such as 137Cs inventories and parameters directly derived from soil samples (e.g., fine fraction density, effective volume) to accurately capture the 137Cs distribution within the soil profile. The model simulates annual 137Cs fallout, radioactive decay, and vertical diffusion, with the diffusion coefficient calculated from 137Cs reference inventory profiles. Additionally, it allows users to input custom parameters as calibration coefficients. The RadEro user manual and protocol, including detailed instructions on how to format input data and configuration files, can be found at the following link: <https://github.com/eead-csic-eesa/RadEro>.

Nuclear Decay Data for Dosimetric Calculations from the International Commission on Radiological Protection from ICRP Publication 107. Ann. ICRP 38 (3). Eckerman, Keith and Endo, Akira 2008 <doi:10.1016/j.icrp.2008.10.004> <https://www.icrp.org/publication.asp?id=ICRP%20Publication%20107>. This is a database of the physical data needed in calculations of radionuclide-specific protection and operational quantities. The data is prescribed by the ICRP, the international authority on radiation dose standards, for estimating dose from the intake of or exposure to radionuclides in the workplace and the environment. The database contains information on the half-lives, decay chains, and yields and energies of radiations emitted in nuclear transformations of 1252 radionuclides of 97 elements.

Generate, simulate and visualise ODE models of consumer-resource interactions. At its core, rescomp provides a resource competition modelling focused interface to deSolve', alongside flexible functions for visualising model properties and dynamics. More information, documentation and examples can be found on the package website.

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.

Allows to limit the rate at which one or more functions can be called.

This package provides a framework for unit testing for realistic minimalists, where we distinguish between expected, acceptable, current, fallback, ideal, or regressive behaviour. It can also be used for monitoring third-party software projects for changes.

Calculate the flow of particles between polygons by two integration methods: integration by a cubature method and integration on a grid of points. Annie Bouvier, Kien Kieu, Kasia Adamczyk and Herve Monod (2009) <doi:10.1016/j.envsoft.2008.11.006>.

Implementation of Robust Regression tailored to deal with Asymmetric noise Distribution, which was originally proposed by Takeuchi & Bengio & Kanamori (2002) <doi:10.1162/08997660260293300>. In addition, this implementation is extended as introducing potential feature regularization by LASSO etc.

Designed to be compatible with the R package DBI (Database Interface) when connecting to Amazon Web Service ('AWS') Athena <https://aws.amazon.com/athena/>. To do this Python Boto3 Software Development Kit ('SDK') <https://boto3.amazonaws.com/v1/documentation/api/latest/index.html> is used as a driver.