Tools for fluctuations analysis of mutant cells counts. Main reference is A. Mazoyer, R. Drouilhet, S. Despreaux and B. Ycart (2017)  <doi:10.32614/RJ-2017-029>.

A set of methods to simulate from and fit computational models of
    attentional selectivity. The package implements the dual-stage two-phase
    (DSTP) model of Hübner et al. (2010) <doi:10.1037/a0019471>, and the
    shrinking spotlight (SSP) model of White et al. (2011)
    <doi:10.1016/j.cogpsych.2011.08.001>.

The Forecast Linear Augmented Projection (flap) method reduces 
    forecast variance by adjusting the forecasts of multivariate time series to 
    be consistent with the forecasts of linear combinations (components) of the 
    series by projecting all forecasts onto the space where the linear 
    constraints are satisfied. The forecast variance can be reduced 
    monotonically by including more components. For a given number of 
    components, the flap method achieves maximum forecast variance reduction 
    among linear projections.

Provide the implementation of a family of Lasso variants including Dantzig Selector, LAD Lasso, SQRT Lasso, Lq Lasso for estimating high dimensional sparse linear model. We adopt the alternating direction method of multipliers and convert the original optimization problem into a sequential L1 penalized least square minimization problem, which can be efficiently solved by linearization algorithm. A multi-stage screening approach is adopted for further acceleration. Besides the sparse linear model estimation, we also provide the extension of these Lasso variants to sparse Gaussian graphical model estimation including TIGER and CLIME using either L1 or adaptive penalty. Missing values can be tolerated for Dantzig selector and CLIME. The computation is memory-optimized using the sparse matrix output. For more information, please refer to <https://www.jmlr.org/papers/volume16/li15a/li15a.pdf>.

Create a flip over style Flash Card with desired data frame for Shiny application.