Generalized Additive Models with Flexible Response Functions (FlexGAM)
Standard generalized additive models assume a response function, which induces an assumption on the shape of the distribution of the response. However, miss-specifying the response function results in biased estimates. Therefore in Spiegel et al. (2017) <doi:10.1007/s11222-017-9799-6> we propose to estimate the response function jointly with the covariate effects. This package provides the underlying functions to estimate these generalized additive models with flexible response functions. The estimation is based on an iterative algorithm. In the outer loop the response function is estimated, while in the inner loop the covariate effects are determined. For the response function a strictly monotone P-spline is used while the covariate effects are estimated based on a modified Fisher-Scoring algorithm. Overall the estimation relies on the ‘mgcv’-package.

Tree-Structured Modelling of Varying Coefficients (TSVC)
Fitting tree-structured varying coefficient models (Berger, M., Tutz, G. & Schmid, M. (2018) <doi:10.1007/s11222-018-9804-8>). Simultaneous detection of covariates with varying coefficients and effect modifiers that induce varying coefficients if they are present.

Online Fitting of Time-Adaptive Lasso Vector Auto Regression (onlineVAR)
Functions for recursive online fitting of time-adaptive lasso vector auto regression. A recursive coordinate descent algorithm is used to estimate sparse vector auto regressive models and exponential forgetting is applied to allow model changes. Details can be found in Jakob W. Messner and Pierre Pinson (2018). ‘Online adaptive LASSO estimation in Vector Auto Regressive models for wind power forecasting in high dimension’. International Journal of Forecasting, in press. Preprint: <http://…/MessnerPinson18.pdf>.

A Time Series Toolbox for Official Statistics (tstools)
Plot official statistics’ time series conveniently: automatic legends, highlight windows, stacked bar chars with positive and negative contributions, sum-as-line option, two y-axes with automatic horizontal grids that fit both axes and other popular chart types. ‘tstools’ comes with a plethora of defaults to let you plot without setting an abundance of parameters first, but gives you the flexibility to tweak the defaults. In addition to charts, ‘tstools’ provides a super fast, ‘data.table’ backed time series I/O that allows the user to export / import long format, wide format and transposed wide format data to various file types.

Inferring Directional Conservative Causal Core Gene Networks (Ac3net)
Infers directional conservative causal core (gene) networks. It is an advanced version of the algorithm C3NET by providing directional network. Gokmen Altay (2018) <doi:10.1101/271031>, bioRxiv.