This package provides tools to evaluate the value of using a risk prediction instrument to decide treatment or intervention (versus no treatment or intervention). Given one or more risk prediction instruments (risk models) that estimate the probability of a binary outcome, rmda provides functions to estimate and display decision curves and other figures that help assess the population impact of using a risk model for clinical decision making. Here, "population" refers to the relevant patient population. Decision curves display estimates of the (standardized) net benefit over a range of probability thresholds used to categorize observations as high risk'. The curves help evaluate a treatment policy that recommends treatment for patients who are estimated to be high risk by comparing the population impact of a risk-based policy to "treat all" and "treat none" intervention policies. Curves can be estimated using data from a prospective cohort. In addition, rmda can estimate decision curves using data from a case-control study if an estimate of the population outcome prevalence is available. Version 1.4 of the package provides an alternative framing of the decision problem for situations where treatment is the standard-of-care and a risk model might be used to recommend that low-risk patients (i.e., patients below some risk threshold) opt out of treatment. Confidence intervals calculated using the bootstrap can be computed and displayed. A wrapper function to calculate cross-validated curves using k-fold cross-validation is also provided.

The open sourced data management software Integrated Rule-Oriented Data System ('iRODS') offers solutions for the whole data life cycle (<https://irods.org/>). The loosely constructed and highly configurable architecture of iRODS frees the user from strict formatting constraints and single-vendor solutions. This package provides an interface to the iRODS HTTP API, allowing you to manage your data and metadata in iRODS with R. Storage of annotated files and R objects in iRODS ensures findability, accessibility, interoperability, and reusability of data.

Convex Least Squares Programming (CLSP) is a two-step estimator for solving underdetermined, ill-posed, or structurally constrained least-squares problems. It combines pseudoinverse-based estimation with convex-programming correction methods inspired by Lasso, Ridge, and Elastic Net to ensure numerical stability, constraint enforcement, and interpretability. The package also provides numerical stability analysis and CLSP-specific diagnostics, including partial R^2, normalized RMSE (NRMSE), Monte Carlo t-tests for mean NRMSE, and condition-number-based confidence bands.

Download up-to-date data from the Reserve Bank of Australia in a tidy data frame. Package includes functions to download current and historical statistical tables (<https://www.rba.gov.au/statistics/tables/>) and forecasts (<https://www.rba.gov.au/publications/smp/forecasts-archive.html>). Data includes a broad range of Australian macroeconomic and financial time series.

Autoencoding Random Forests ('RFAE') provide a method to autoencode mixed-type tabular data using Random Forests ('RF'), which involves projecting the data to a latent feature space of user-chosen dimensionality (usually a lower dimension), and then decoding the latent representations back into the input space. The encoding stage is useful for feature engineering and data visualisation tasks, akin to how principal component analysis ('PCA') is used, and the decoding stage is useful for compression and denoising tasks. At its core, RFAE is a post-processing pipeline on a trained random forest model. This means that it can accept any trained RF of ranger object type: RF', URF or ARF'. Because of this, it inherits Random Forests robust performance and capacity to seamlessly handle mixed-type tabular data. For more details, see Vu et al. (2025) <doi:10.48550/arXiv.2505.21441>.

Compute time-dependent Incident/dynamic accuracy measures (ROC curve, AUC, integrated AUC )from censored survival data under proportional or non-proportional hazard assumption of Heagerty & Zheng (Biometrics, Vol 61 No 1, 2005, PP 92-105).

This package provides functions to have nice rmarkdown outputs of the seasonal and trading day adjustment models made with RJDemetra'.

Flexible framework for ecological restoration planning. It aims to identify priority areas for restoration efforts using optimization algorithms (based on Justeau-Allaire et al. 2021 <doi:10.1111/1365-2664.13803>). Priority areas can be identified by maximizing landscape indices, such as the effective mesh size (Jaeger 2000 <doi:10.1023/A:1008129329289>), or the integral index of connectivity (Pascual-Hortal & Saura 2006 <doi:10.1007/s10980-006-0013-z>). Additionally, constraints can be used to ensure that priority areas exhibit particular characteristics (e.g., ensure that particular places are not selected for restoration, ensure that priority areas form a single contiguous network). Furthermore, multiple near-optimal solutions can be generated to explore multiple options in restoration planning. The package leverages the Choco-solver software to perform optimization using constraint programming (CP) techniques (<https://choco-solver.org/>).

This package contains several useful navigation helper functions, including easily building folder paths, quick viewing dataframes in Excel', creating date vectors and changing the console prompt to reflect time.

The SPRITE algorithm creates possible distributions of discrete responses based on reported sample parameters, such as mean, standard deviation and range (Heathers et al., 2018, <doi:10.7287/peerj.preprints.26968v1>). This package implements it, drawing heavily on the code for Nick Brown's rSPRITE Shiny app <https://shiny.ieis.tue.nl/sprite/>. In addition, it supports the modeling of distributions based on multi-item (Likert-type) scales and the use of restrictions on the frequency of particular responses.

R interface for china national data <http://data.stats.gov.cn/>, some convenient functions for accessing the national data are provided.

This package provides a toolkit for Commodities analytics', risk management and trading professionals. Includes functions for API calls to <https://commodities.morningstar.com/#/>, <https://developer.genscape.com/>, and <https://www.bankofcanada.ca/valet/docs>.

Fits non-linear regression models on dependant data with Generalised Least Square (GLS) based Random Forest (RF-GLS) detailed in Saha, Basu and Datta (2021) <doi:10.1080/01621459.2021.1950003>.

Get data from Linkedin Advertising API <https://learn.microsoft.com/en-us/linkedin/marketing/overview?view=li-lms-2023-10>. You can load ad account hierarchy (accounts, users, campaign groups, campaigns and creatives) and also you can load ad analytics data from your Linkedin Ad account.

Calculates tide heights based on tide station harmonics. It includes the harmonics data for 637 US stations. The harmonics data was converted from <https://github.com/poissonconsulting/rtide/blob/main/data-raw/harmonics-dwf-20151227-free.tar.bz2>, NOAA web site data processed by David Flater for XTide'. The code to calculate tide heights from the harmonics is based on XTide'.

This package provides a template model module, tools to help find model modules derived from this template and a programming syntax to use these modules in health economic analyses. These elements are the foundation for a prototype software framework for developing living and transferable models and using those models in reproducible health economic analyses. The software framework is extended by other R libraries. For detailed documentation about the framework and how to use it visit <https://www.ready4-dev.com/>. For a background to the methodological issues that the framework is attempting to help solve, see Hamilton et al. (2024) <doi:10.1007/s40273-024-01378-8>.

Routines to select and visualize the maxima for a given strict partial order. This especially includes the computation of the Pareto frontier, also known as (Top-k) Skyline operator (see Börzsönyi, et al. (2001) <doi:10.1109/ICDE.2001.914855>), and some generalizations known as database preferences (see Kieà ling (2002) <doi:10.1016/B978-155860869-6/50035-4>).

Risk ratios and risk differences are estimated using regression models that allow for binary, categorical, and continuous exposures and confounders. Implemented are marginal standardization after fitting logistic models (g-computation) with delta-method and bootstrap standard errors, Miettinen's case-duplication approach (Schouten et al. 1993, <doi:10.1002/sim.4780121808>), log-binomial (Poisson) models with empirical variance (Zou 2004, <doi:10.1093/aje/kwh090>), binomial models with starting values from Poisson models (Spiegelman and Hertzmark 2005, <doi:10.1093/aje/kwi188>), and others.

Easily Download Analysis-Ready Crash Data from the U.S. National Highway Traffic Safety Administration.

IUCN Red List (<https://api.iucnredlist.org/>) client. The IUCN Red List is a global list of threatened and endangered species. Functions cover all of the Red List API routes. An API key is required.

Collection of functions to evaluate sequences, decode hidden states and estimate parameters from a single or multiple sequences of a discrete time Hidden Markov Model. The observed values can be modeled by a multinomial distribution for categorical/labeled emissions, a mixture of Gaussians for continuous data and also a mixture of Poissons for discrete values. It includes functions for random initialization, simulation, backward or forward sequence evaluation, Viterbi or forward-backward decoding and parameter estimation using an Expectation-Maximization approach.

Reference-based multiple imputation of ordinal and binary responses under Bayesian framework, as described in Wang and Liu (2022) <arXiv:2203.02771>. Methods for missing-not-at-random include Jump-to-Reference (J2R), Copy Reference (CR), and Delta Adjustment which can generate tipping point analysis.

Perform optimal transport on somatic point mutations and kernel regression hypothesis testing by integrating pathway level similarities at the gene level (Little et al. (2023) <doi:10.1111/biom.13769>). The software implements balanced and unbalanced optimal transport and omnibus tests with C++ across a set of tumor samples and allows for multi-threading to decrease computational runtime.

Parser for SQL statements. Currently, it supports parsing of only SELECT statements.