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Flexible Mixture Modeling
A general framework for finite mixtures of regression models using the EM algorithm is implemented. The E-step and all data handling are provided, while the M-step can be supplied by the user to easily define new models. Existing drivers implement mixtures of standard linear models, generalized linear models and model-based clustering.
Regression with Functional Data
Methods for regression for functional data, including function-on-scalar, scalar-on-function, and function-on-function regression. Some of the functions are applicable to image data.
Tour Methods for Multivariate Data Visualisation
Implements geodesic interpolation and basis generation functions that allow you to create new tour methods from R.
Indices of Effect Size
Provide utilities to work with indices of effect size for a wide
variety of models and hypothesis tests (see list of supported models using
the function 'insight::supported_models()'), allowing computation of and
conversion between indices such as Cohen's d, r, odds, etc.
References: Ben-Shachar et al. (2020)
Tidying Methods for Mixed Models
Convert fitted objects from various R mixed-model packages into tidy data frames along the lines of the 'broom' package. The package provides three S3 generics for each model: tidy(), which summarizes a model's statistical findings such as coefficients of a regression; augment(), which adds columns to the original data such as predictions, residuals and cluster assignments; and glance(), which provides a one-row summary of model-level statistics.
Forecasting Mortality, Fertility, Migration and Population Data
Functions for demographic analysis including lifetable calculations; Lee-Carter modelling; functional data analysis of mortality rates, fertility rates, net migration numbers; and stochastic population forecasting.
Convert Statistical Objects into Tidy Tibbles
Summarizes key information about statistical objects in tidy tibbles. This makes it easy to report results, create plots and consistently work with large numbers of models at once. Broom provides three verbs that each provide different types of information about a model. tidy() summarizes information about model components such as coefficients of a regression. glance() reports information about an entire model, such as goodness of fit measures like AIC and BIC. augment() adds information about individual observations to a dataset, such as fitted values or influence measures.
SIMEX- And MCSIMEX-Algorithm for Measurement Error Models
Implementation of the SIMEX-Algorithm by Cook & Stefanski (1994)
Time Series Analysis
Contains R functions and datasets detailed in the book "Time Series Analysis with Applications in R (second edition)" by Jonathan Cryer and Kung-Sik Chan.
Risk Regression Models and Prediction Scores for Survival Analysis with Competing Risks
Implementation of the following methods for event history analysis. Risk regression models for survival endpoints also in the presence of competing risks are fitted using binomial regression based on a time sequence of binary event status variables. A formula interface for the Fine-Gray regression model and an interface for the combination of cause-specific Cox regression models. A toolbox for assessing and comparing performance of risk predictions (risk markers and risk prediction models). Prediction performance is measured by the Brier score and the area under the ROC curve for binary possibly time-dependent outcome. Inverse probability of censoring weighting and pseudo values are used to deal with right censored data. Lists of risk markers and lists of risk models are assessed simultaneously. Cross-validation repeatedly splits the data, trains the risk prediction models on one part of each split and then summarizes and compares the performance across splits.