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R / Bioconductor Packages

A Package for Computation of Confidence Intervals for Variance Components of Mixed Models in R. varcompci computes these confidence intervals (Burdick and Graybill, 1992) for any balanced mixed effects saturated (main effect and interaction terms of all orders) analysis of variance (ANOVA) model (type III), involving five or fewer factors. The methods in this paper can also be applied to data with unbalanced design, but we have not evaluated the performance of these methods for unbalanced data.
Description: dcens is a library for the estimation of the survival function for doubly censored data. A double censorship scheme appears when in addition to the usual right censoring also left censoring exists. If T denotes the time of interest, its exact value is only observed when it is in the time window [L, R], where L < R are positive random variables. So, the observable sample is integrated by the pairs (U, d) where U = min{R, max{T, L}} and d an indicator variable (d = 0 for exact values, d = 1 for right censored values and d = -1 for left censored values). The library allows the non parametric and simultaneous estimation of the marginal survival functions ST, SL and SR, for T, L and R, respectively by using an inverse probability of censoring weighted procedure. (Julià, O. and Gómez, G. (2011) Simultaneous marginal survival estimators when doubly censored data is present. Lifetime Data Analysis, 17, 347-372).
The library bwsurvival is designed for situations in which there are two consecutive events of interest, E1 and E2, when the scientific goal is to infer on the time T2 until E2 given the time T1 till E1. The methodology is based on non-parametric estimation of the conditional survival function T2|T1 on a partition of different intervals of time of scientific interest (1 week, one quarter, one year, two years ,...) entered by the user. The proposed estimator takes into account the selection bias and the heterogeneity due to the dependent censorship, by using a weighted method on the observations of T1. The library allows the use of other weights defined by the user as well as the stratification of the survival function by a categorical variable. (Gómez, G. and Serrat, C. (2014) Correcting the bias due to dependent censoring of the survival estimator by conditioning. Statistics, 48 (2), 295 - 314).
ICGE is a user-friendly R package which provides many functions related to: identify the number of clusters using mixed variables, usually found by applied biomedical researchers; detect whether the data have a cluster structure; identify whether a new unit belongs to one of the pre-identified clusters or to a novel group, and classify new units into the corresponding cluster. The functions in the ICGE package are accompanied by help files and easy examples to facilitate its use.
Tests for Right and Interval-Censored Survival Data Based on the Fleming-Harrington Class.
The package implements methods to compare lists of genes based on comparing the corresponding 'functional profiles'.
Statistical tests for label-free LC-MS/MS data by spectral counts, to discover differentially expressed proteins between two biological conditions. Three tests are available: Poisson GLM regression, quasi-likelihood GLM regression, and the negative binomial of the edgeR package.The three models admit blocking factors to control for nuissance variables.To assure a good level of reproducibility a post-test filter is available, where we may set the minimum effect size considered biologicaly relevant, and the minimum expression of the most abundant condition.
Exploratory data analysis to assess the quality of a set of LC-MS/MS experiments, and visualize de influence of the involved factors.