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Bindings for the 'Geospatial' Data Abstraction Library
Provides bindings to the 'Geospatial' Data Abstraction Library ('GDAL') (>= 1.11.4) and access to projection/transformation operations from the 'PROJ' library. Please note that 'rgdal' will be retired during October 2023, plan transition to sf/stars/'terra' functions using 'GDAL' and 'PROJ' at your earliest convenience (see < https://r-spatial.org/r/2023/05/15/evolution4.html> and earlier blogs for guidance). Use is made of classes defined in the 'sp' package. Raster and vector map data can be imported into R, and raster and vector 'sp' objects exported. The 'GDAL' and 'PROJ' libraries are external to the package, and, when installing the package from source, must be correctly installed first; it is important that 'GDAL' < 3 be matched with 'PROJ' < 6. From 'rgdal' 1.5-8, installed with to 'GDAL' >=3, 'PROJ' >=6 and 'sp' >= 1.4, coordinate reference systems use 'WKT2_2019' strings, not 'PROJ' strings. 'Windows' and 'macOS' binaries (including 'GDAL', 'PROJ' and their dependencies) are provided on 'CRAN'.
Nonparametric and Cox-Based Estimation of Average Treatment Effects in Competing Risks
Estimation of average treatment effects (ATE) of point interventions on time-to-event outcomes with K competing risks (K can be 1). The method uses propensity scores and inverse probability weighting for emulation of baseline randomization, which is described in Charpignon et al. (2022)
Variational Mixture Models for Clustering Categorical Data
A variational Bayesian finite mixture model for the clustering of categorical data, and can implement variable selection and semi-supervised outcome guiding if desired. Incorporates an option to perform model averaging over multiple initialisations to reduce the effects of local optima and improve the automatic estimation of the true number of clusters. For further details, see the paper by Rao and Kirk (2024)
Lexicons for Text Analysis
A collection of lexical hash tables, dictionaries, and word lists.
Reliably Return the Source and Call Location of a Command
Robust and reliable functions to return informative outputs to console with the run or source location of a command. This can be from the 'RScript'/R terminal commands or 'RStudio' console, source editor, 'Rmarkdown' document and a Shiny application.
Power Analyses for Interaction Effects in Cross-Sectional Regressions
Power analysis for regression models which test the interaction of
two or three independent variables on a single dependent variable. Includes options
for correlated interacting variables and specifying variable reliability.
Two-way interactions can include continuous, binary, or ordinal variables.
Power analyses can be done either analytically or via simulation. Includes
tools for simulating single data sets and visualizing power analysis results.
The primary functions are power_interaction_r2() and power_interaction() for two-way
interactions, and power_interaction_3way_r2() for three-way interactions. The function
run_pos_power_search() provides a stability analysis for two-way interactions.
Please cite as: Baranger DAA, Finsaas MC, Goldstein BL, Vize CE, Lynam DR,
Olino TM (2023). "Tutorial: Power analyses for interaction effects in
cross-sectional regressions."
Text Processing Tools for Turkish E-Commerce Data
Provides several datasets useful for processing and analysis of text in Turkish from an online shopping platform.
Nonparametric Models for Longitudinal Data
Support the book: Wu CO and Tian X (2018). Nonparametric Models for Longitudinal Data. Chapman & Hall/CRC (to appear); and provide fit for using global and local smoothing methods for the conditional-mean and conditional-distribution based models with longitudinal Data.
Random Survival Forest for Recurrent Events
A tool designed to analyze recurrent events when dealing with right-censored data and the potential presence of a terminal event (that prevents further occurrences, like death). It extends the random survival forest algorithm, adapting splitting rules and node estimators to handle complexities of recurrent events. The methodology is fully described in Murris, J., Bouaziz, O., Jakubczak, M., Katsahian, S., & Lavenu, A. (2024) (< https://hal.science/hal-04612431v1/document>).
The R Interface to 'SyncroSim'
'SyncroSim' is a generalized framework for managing scenario-based datasets (< https://syncrosim.com/>). 'rsyncrosim' provides an interface to 'SyncroSim'. Simulation models can be added to 'SyncroSim' in order to transform these datasets, taking advantage of general features such as defining scenarios of model inputs, running Monte Carlo simulations, and summarizing model outputs. 'rsyncrosim' requires 'SyncroSim' 2.3.5 or higher (API documentation: < https://docs.syncrosim.com/>).