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Nelson-Aalen Estimator of the Cumulative Hazard in Multistate Models
Computes the Nelson-Aalen estimator of the cumulative transition hazard for arbitrary Markov multistate models
Complete Environment for Bayesian Inference
Provides a complete environment for Bayesian inference using a variety of different samplers (see ?LaplacesDemon for an overview). The README describes the history of the package development process.
Bayesian Latent Class Analysis
Bayesian Latent Class Analysis using several different methods.
Empirical Transition Matrix
The etm (empirical transition matrix) package permits to estimate the matrix of transition probabilities for any time-inhomogeneous multistate model with finite state space using the Aalen-Johansen estimator. Functions for data preparation and for displaying are also included (Allignol et al., 2011
Regularized and Sparse Generalized Canonical Correlation Analysis for Multiblock Data
Multiblock data analysis concerns the analysis of several sets of variables (blocks) observed on the same group of individuals. The main aims of the RGCCA package are: (i) to study the relationships between blocks and (ii) to identify subsets of variables of each block which are active in their relationships with the other blocks.
The Conditional Probability Function of a Competing Event
Permits to estimate the conditional probability function of a competing event, and to fit, using the temporal process regression or the pseudo-value approach, a proportional-odds model to the conditional probability function (or other models by specifying another link function). See
Kaplan-Meier Multiple Imputation for the Analysis of Cumulative Incidence Functions in the Competing Risks Setting
Performs a Kaplan-Meier multiple imputation to recover the missing potential censoring information from competing risks events, so that standard right-censored methods could be applied to the imputed data sets to perform analyses of the cumulative incidence functions (Allignol and Beyersmann, 2010
Communication Between Editor and Viewer for Literate Programs
This utility eases the debugging of literate documents ('noweb' files) by patching the synchronization information (the '.synctex(.gz)' file) produced by 'pdflatex' with concordance information produced by 'Sweave' or 'knitr' and 'Sweave' or 'knitr' ; this allows for bilateral communication between a text editor (visualizing the 'noweb' source) and a viewer (visualizing the resultant 'PDF'), thus bypassing the intermediate 'TeX' file.
A Set of Methods for Longitudinal Data Objects
A very simple implementation of a class for longitudinal data.
Accurate Parentage Analysis in the Absence of Guiding Information
Performs parentage analysis based on a test of genetic identity between expected progeny (EPij), built using Single Nucleotide Polymorphism (SNP) homozygous loci from all pairs of possible parents (i and j), and all potential offspring (POk). Using the Gower Dissimilarity metric (GD), genetic identity between EPij and POk is taken as evidence that individuals i and j are the true parents of offspring k. Evaluation of triad (two parents + offspring) significance is based on the distribution of all GD (EPij|k) values. Specifically, a Dixon test is used to identify a gap-based threshold that separates true triads and from spurious associations. For any offspring not successfully assigned to a pair of parents, perhaps due to the absence of one parent from the test population, a non-mandatory Dyad analysis can be employed to identify a likely single parent for a given offspring. In this analysis, a two-stage test is applied to discriminate an offspring's true parent from its other close relatives (e.g. siblings) that may also be present in the population. In the first stage, 'apparent' calculates the mean GD (GDM) between a POk and all expected progeny arising from the j possible triads involving potential parent i. In the second stage, it calculates a coefficient of variation (GDCV) among the pairwise GD's between POk and each expected progeny arising from the j triads involving potential parent i. An individual that is simultaneously a low outlier in the first test and a high outlier in the second is identified as a likely parent of POk. In an effort to facilitate interpretation, results of both the triad and optional dyad analyses are presented in tabular and graphical form.