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Variable Selection for Model-Based Clustering of Mixed-Type Data Set with Missing Values
Full model selection (detection of the relevant features and estimation of the number of clusters) for model-based clustering (see reference here
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.
Data for 'GAMs: An Introduction with R'
Data sets and scripts used in the book 'Generalized Additive Models: An Introduction with R', Wood (2006,2017) CRC.
Data Analysis and Graphics Data and Functions
Functions and data sets used in examples and exercises in the text Maindonald, J.H. and Braun, W.J. (2003, 2007, 2010) "Data Analysis and Graphics Using R", and in an upcoming Maindonald, Braun, and Andrews text that builds on this earlier text.
A Multi-Process 'dplyr' Backend
Partition a data frame across multiple worker processes to provide simple multicore parallelism.
Nonlinear Time Series Models with Regime Switching
Implements nonlinear autoregressive (AR) time series models. For univariate series, a non-parametric approach is available through additive nonlinear AR. Parametric modeling and testing for regime switching dynamics is available when the transition is either direct (TAR: threshold AR) or smooth (STAR: smooth transition AR, LSTAR). For multivariate series, one can estimate a range of TVAR or threshold cointegration TVECM models with two or three regimes. Tests can be conducted for TVAR as well as for TVECM (Hansen and Seo 2002 and Seo 2006).
Doubly Robust Distribution Balancing Weighting Estimation
Implements the doubly robust distribution balancing weighting proposed by Katsumata (2024)
Maximum Likelihood Shrinkage using Generalized Ridge or Least Angle Regression
Functions are provided to calculate and display ridge TRACE Diagnostics for a
variety of alternative Shrinkage Paths. While all methods focus on Maximum Likelihood
estimation of unknown true effects under normal distribution-theory, some estimates are
modified to be Unbiased or to have "Correct Range" when estimating either [1] the noncentrality
of the F-ratio for testing that true Beta coefficients are Zeros or [2] the "relative" MSE
Risk (i.e. MSE divided by true sigma-square, where the "relative" variance of OLS is known.)
The eff.ridge() function implements the "Efficient Shrinkage Path" introduced in Obenchain
(2022)
Modeling Animal Movement with Continuous-Time Discrete-Space Markov Chains
Software to facilitates taking movement data in xyt format and pairing it with raster covariates within a continuous time Markov chain (CTMC) framework. As described in Hanks et al. (2015)
A LazyData Facility
Supplies a LazyData facility for packages which have data sets but do not provide LazyData: true. A single function is is included, requireData, which is a drop-in replacement for base::require, but carrying the additional functionality. By default, it suppresses package startup messages as well. See argument 'reallyQuitely'.