Title: Standardized high-throughput evaluation of cell-based compound
Description: Calculation of IC50 values, automatic drawing of
dose-response curves and validation of compound screens on 96-

Title: A Modified Random Survival Forest Algorithm
Description: Implements a modification to the Random Survival Forests algorithm for obtaining variable importance in high dimensional datasets. The proposed algorithm is appropriate for settings in which a silent event is observed through sequentially administered, error-prone self-reports or laboratory based diagnostic tests.  The modified algorithm incorporates a formal likelihood framework that accommodates sequentially administered, error-prone self-reports or laboratory based diagnostic tests. The original Random Survival Forests algorithm is modified by the introduction of a new splitting criterion based on a likelihood ratio test statistic.
Imports: Rcpp (>= 0.11.3), icensmis, parallel

Imports: Rcpp (>= 0.11.3), icensmis, parallel, stats

Description: Independent Component Analysis (ICA) using various algorithms: FastICA, Information-Maximization (Infomax), and Joint Approximate Diagonalization of Eigenmatrices (JADE).

Title: Independent Components Analysis (ICA) based eye-movement
Description: Removes eye-movements artifacts as well as (a portion of)
of other noise using the fastICA package. The correction
methods is based on Flexer, Bauer, Pripfl, and Dorffner (2005).

Description: Removes eye-movements artifacts as well as (a portion of) of other noise using the fastICA package. The correction methods is based on Flexer, Bauer, Pripfl, and Dorffner (2005).

Author: Antoine Tremblay, NeuroCognitive Imaging Lab, Dalhousie University
Description: Removes eye-movement and other types of known (i.e., recorded) or unknown (i.e., not recorded) artifacts using the fastICA package. The correction method proposed in this package is largely based on the method described in on Flexer, Bauer, Pripfl, and Dorffner (2005). The process of correcting electro- and magneto-encephalographic data (EEG/MEG) begins by running function ``icac'', which first performs independent components analysis (ICA) to decompose the data frame into independent components (ICs) using function ``fastICA'' from the package of the same name. It then calculates for each trial the correlation between each IC and each one of the noise signals -- there can be one or more, e.g., vertical and horizontal electro-oculograms (VEOG and HEOG), electro-myograms (EMG), electro-cardiograms (ECG), galvanic skin responses (GSR), and other noise signals. Subsequently, portions of an IC corresponding to trials at which the correlation between it and a noise signal was at or above threshold (set to 0.4 by default; Flexer et al., 2005, p. 1001) are zeroed-out in the source matrix, ``S''. The user can then identify which ICs correlate with the noise signals the most by looking at the summary of the ``icac'' object (using function ``summary.icac''), the scalp topography of the ICs (using function ``topo_ic''), the time courses of the ICs (using functions ``plot_tric'' and ``plot_nic''), and other diagnostic plots. Once these ICs have been identified, they can be completely zeroed-out using function ``update.icac'' and the resulting correction checked using functions ``plot_avgba'' and ``plot_trba''. Some worked-out examples with R code are provided in the package vignette.