Calculation of IC50 values, automatic drawing of kill-curves and validation of compound screens on 96- and 384-well plates.

Standardized high-throughput evaluation of compound screens

Calculation of IC50 values, automatic drawing of dose-response curves and validation of compound screens on 96- and 384-well plates.

Standardized high-throughput evaluation of cell-based compound screens

Standardized high-throughput evaluation of cell-based compound
screens
Calculation of IC50 values, automatic drawing of
dose-response curves and validation of compound screens on 96-
and 384-well plates.

A Modified Random Survival Forest Algorithm
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.

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

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

Independent Components Analysis (ICA) based eye-movement
correction.
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).

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).

Independent Components Analysis (ICA) based artifact correction.
Antoine Tremblay, NeuroCognitive Imaging Lab, Dalhousie
University
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 either zeroed-out in
the source matrix, ``S'', or subtracted from the data that was
passed to function ``icac''. 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 or subtracted 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 following sections.
Please contact the package maintainer to obtain the data to run
the examples.