Merged Block Randomization

Package to carry out merged block randomization (Van der Pas (2019), ), a restricted randomization method designed for small clinical trials (at most 100 subjects) or trials with small strata, for example in multicentre trials. It can be used for more than two groups or unequal randomization ratios.

Graphical Analysis of Structural Causal Models

A port of the web-based software 'DAGitty', available at < https://dagitty.net>, for analyzing structural causal models (also known as directed acyclic graphs or DAGs). This package computes covariate adjustment sets for estimating causal effects, enumerates instrumental variables, derives testable implications (d-separation and vanishing tetrads), generates equivalent models, and includes a simple facility for data simulation.

An Ensemble Method for Combining Subset-Specific Algorithm Fits

The Subsemble algorithm is a general subset ensemble prediction method, which can be used for small, moderate, or large datasets. Subsemble partitions the full dataset into subsets of observations, fits a specified underlying algorithm on each subset, and uses a unique form of k-fold cross-validation to output a prediction function that combines the subset-specific fits. An oracle result provides a theoretical performance guarantee for Subsemble. The paper, "Subsemble: An ensemble method for combining subset-specific algorithm fits" is authored by Stephanie Sapp, Mark J. van der Laan & John Canny (2014) .

Classes for Relational Data

Tools to create and modify network objects. The network class can represent a range of relational data types, and supports arbitrary vertex/edge/graph attributes.

Smooth Survival Models, Including Generalized Survival Models

R implementation of generalized survival models (GSMs), smooth accelerated failure time (AFT) models and Markov multi-state models. For the GSMs, g(S(t|x))=eta(t,x) for a link function g, survival S at time t with covariates x and a linear predictor eta(t,x). The main assumption is that the time effect(s) are smooth . For fully parametric models with natural splines, this re-implements Stata's 'stpm2' function, which are flexible parametric survival models developed by Royston and colleagues. We have extended the parametric models to include any smooth parametric smoothers for time. We have also extended the model to include any smooth penalized smoothers from the 'mgcv' package, using penalized likelihood. These models include left truncation, right censoring, interval censoring, gamma frailties and normal random effects , and copulas. For the smooth AFTs, S(t|x) = S_0(t*eta(t,x)), where the baseline survival function S_0(t)=exp(-exp(eta_0(t))) is modelled for natural splines for eta_0, and the time-dependent cumulative acceleration factor eta(t,x)=\int_0^t exp(eta_1(u,x)) du for log acceleration factor eta_1(u,x). The Markov multi-state models allow for a range of models with smooth transitions to predict transition probabilities, length of stay, utilities and costs, with differences, ratios and standardisation.

Fast R and C++ Access to NIfTI Images

Provides very fast read and write access to images stored in the NIfTI-1, NIfTI-2 and ANALYZE-7.5 formats, with seamless synchronisation of in-memory image objects between compiled C and interpreted R code. Also provides a simple image viewer, and a C/C++ API that can be used by other packages. Not to be confused with 'RNiftyReg', which performs image registration and applies spatial transformations.

Derivation of Regression-Based Normative Data

Normative data are often used to estimate the relative position of a raw test score in the population. This package allows for deriving regression-based normative data. It includes functions that enable the fitting of regression models for the mean and residual (or variance) structures, test the model assumptions, derive the normative data in the form of normative tables or automatic scoring sheets, and estimate confidence intervals for the norms. This package accompanies the book Van der Elst, W. (2024). Regression-based normative data for psychological assessment. A hands-on approach using R. Springer Nature.

R Code for Mark Analysis

An interface to the software package MARK that constructs input files for MARK and extracts the output. MARK was developed by Gary White and is freely available at < http://www.phidot.org/software/mark/downloads/> but is not open source.

Efficient Plotting of Large-Sized Data

A tool to plot data with a large sample size using 'shiny' and 'plotly'. Relatively small samples are obtained from the original data using a specific algorithm. The samples are updated according to a user-defined x range. Jonas Van Der Donckt, Jeroen Van Der Donckt, Emiel Deprost (2022) < https://github.com/predict-idlab/plotly-resampler>.

Flexible Co-Data Learning for High-Dimensional Prediction

Fit linear, logistic and Cox survival regression models penalised with adaptive multi-group ridge penalties. The multi-group penalties correspond to groups of covariates defined by (multiple) co-data sources. Group hyperparameters are estimated with an empirical Bayes method of moments, penalised with an extra level of hyper shrinkage. Various types of hyper shrinkage may be used for various co-data. Co-data may be continuous or categorical. The method accommodates inclusion of unpenalised covariates, posterior selection of covariates and multiple data types. The model fit is used to predict for new samples. The name 'ecpc' stands for Empirical Bayes, Co-data learnt, Prediction and Covariate selection. See Van Nee et al. (2020) .