The Radiant Model menu includes interfaces for linear and logistic regression, naive Bayes, neural networks, classification and regression trees, model evaluation, collaborative filtering, decision analysis, and simulation. The application extends the functionality in radiant.data'.

Based on data of real user-agent strings, we can set filtering conditions and randomly sample user-agent strings from the user-agent string pool.

Interface to integrate igraph and ggplot2 graphics in a normalized coordinate system. RGraphSpace implements new geometric objects using ggplot2 prototypes, customized for side-by-side visualization of multiple graphs. By scaling shapes and graph elements, RGraphSpace can provide a framework for layered visualizations.

The regression discontinuity (RD) design is a popular quasi-experimental design for causal inference and policy evaluation. The rdmulti package provides tools to analyze RD designs with multiple cutoffs or scores: rdmc() estimates pooled and cutoff specific effects for multi-cutoff designs, rdmcplot() draws RD plots for multi-cutoff designs and rdms() estimates effects in cumulative cutoffs or multi-score designs. See Cattaneo, Titiunik and Vazquez-Bare (2020) <https://rdpackages.github.io/references/Cattaneo-Titiunik-VazquezBare_2020_Stata.pdf> for further methodological details.

Facilitating the creation of reproducible statistical report templates. Once created, rapport templates can be exported to various external formats (HTML, LaTeX, PDF, ODT etc.) with pandoc as the converter backend.

Assists in the whole process of designing and evaluating Randomized Control Trials. Robust treatment assignment by strata/blocks, that handles misfits; Power calculations of the minimum detectable treatment effect or minimum populations; Balance tables of T-test of covariates; Balance Regression: (treatment ~ all x variables) with F-test of null model; Impact_evaluation: Impact evaluation regressions. This function gives you the option to include control_vars, fixed effect variables, cluster variables (for robust SE), multiple endogenous variables and multiple heterogeneous variables (to test treatment effect heterogeneity) summary_statistics: Function that creates a summary statistics table with statistics rank observations in n groups: Creates a factor variable with n groups. Each group has a min and max label attach to each category. Athey, Susan, and Guido W. Imbens (2017) <arXiv:1607.00698>.

Allows users to easily create references to R objects then dereference when needed or modify in place without using reference classes, environments, or active bindings as workarounds. Users can also create expression references that allow subsets of any object to be referenced or expressions containing references to multiple objects.

This package provides a compact R interface for performing tensor calculations. This is achieved by allowing (upper and lower) index labeling of arrays and making use of Ricci calculus conventions to implicitly trigger contractions and diagonal subsetting. Explicit tensor operations, such as addition, subtraction and multiplication of tensors via the standard operators, raising and lowering indices, taking symmetric or antisymmetric tensor parts, as well as the Kronecker product are available. Common tensors like the Kronecker delta, Levi Civita epsilon, certain metric tensors, the Christoffel symbols, the Riemann as well as Ricci tensors are provided. The covariant derivative of tensor fields with respect to any metric tensor can be evaluated. An effort was made to provide the user with useful error messages.

Implementation of the Robust Exponential Decreasing Index (REDI), proposed in the article by Issa Moussa, Arthur Leroy et al. (2019) <https://bmjopensem.bmj.com/content/bmjosem/5/1/e000573.full.pdf>. The REDI represents a measure of cumulated workload, robust to missing data, providing control of the decreasing influence of workload over time. Various functions are provided to format data, compute REDI, and visualise results in a simple and convenient way.

The ecocrop model estimates environmental suitability for plants using a limiting factor approach for plant growth following Hackett (1991) <doi:10.1007/BF00045728>. The implementation in this package is fast and flexible: it allows for the use of any (environmental) predictor variable. Predictors can be either static (for example, soil pH) or dynamic (for example, monthly precipitation).

Implementation of Taylor Regression Estimator (TRE), Tulip Extreme Finding Estimator (TEFE), Bell Extreme Finding Estimator (BEFE), Integration Extreme Finding Estimator (IEFE) and Integration Root Finding Estimator (IRFE) for roots, extrema and inflections of a curve . Christopoulos, DT (2019) <doi:10.13140/RG.2.2.17158.32324> . Christopoulos, DT (2016) <doi:10.2139/ssrn.3043076> . Christopoulos, DT (2016) <https://demovtu.veltech.edu.in/wp-content/uploads/2016/04/Paper-04-2016.pdf> . Christopoulos, DT (2014) <doi:10.48550/arXiv.1206.5478> .

Proper L2-penalized maximum likelihood estimators for precision matrices and supporting functions to employ these estimators in a graphical modeling setting. For details, see Peeters, Bilgrau, & van Wieringen (2022) <doi:10.18637/jss.v102.i04> and associated publications.

Analyze download logs from the CRAN RStudio mirror (<http://cran.rstudio.com/>). This CRAN mirror is the default one used in RStudio. The available data is the result of parsed and anonymised raw log data from that CRAN mirror.

R-level and C++-level functionality to generate random deviates from and calculate moments of a Truncated Normal distribution using the algorithm of Robert (1995) <DOI:10.1007/BF00143942>. In addition to RNG, functions for calculating moments, densities, and entropies are provided at both levels.

Import REDATAM formats into R via the Open REDATAM C++ library. The full context of this project and details about the implementation are available in <doi:10.1017/dap.2025.4> (Open Access).

This package provides a collection of functions to estimate Rogers-Castro migration age schedules using Stan'. This model which describes the fundamental relationship between migration and age in the form of a flexible multi-exponential migration model was most notably proposed in Rogers and Castro (1978) <doi:10.1068/a100475>.

Data and Functions from the book R Graphics, Third Edition. There is a function to produce each figure in the book, plus several functions, classes, and methods defined in Chapter 8.

Build regular expressions piece by piece using human readable code. This package contains Unicode functionality, and is primarily intended to be used by package developers.

Allows remote access to satellite image time series provided by the web time series service (WTSS) available at servers such as <https://brazildatacube.dpi.inpe.br/wtss/>. The functions include listing the data sets available in WTSS servers, describing the contents of a data set, and retrieving a time series based on spatial location and temporal filters.

Adds menu items for discrete choice experiments (DCEs) to the R Commander. DCE is a question-based survey method that designs various combinations (profiles) of attribute levels using the experimental designs, asks respondents to select the most preferred profile in each choice set, and then measures preferences for the attribute levels by analyzing the responses. For details on DCEs, refer to Louviere et al. (2000) <doi:10.1017/CBO9780511753831>.

The Agricultural Production Systems sIMulator ('APSIM') is a widely used to simulate the agricultural systems for multiple crops. This package is designed to create, modify and run apsimx files in the APSIM Next Generation <https://www.apsim.info/>.

Bayesian robust fitting of linear mixed effects models through weighted likelihood equations and approximate Bayesian computation as proposed by Ruli et al. (2017) <arXiv:1706.01752>.

An implementation of a number of Global Trend models for time series forecasting that are Bayesian generalizations and extensions of some Exponential Smoothing models. The main differences/additions include 1) nonlinear global trend, 2) Student-t error distribution, and 3) a function for the error size, so heteroscedasticity. The methods are particularly useful for short time series. When tested on the well-known M3 dataset, they are able to outperform all classical time series algorithms. The models are fitted with MCMC using the rstan package.

Estimation of the conditional covariance matrix using the RiskMetrics 2006 methodology of Zumbach (2007) <doi:10.2139/ssrn.1420185>.