Measuring Stakeholder Influence (StakeholderAnalysis)
Proposes an original instrument for measuring stakeholder influence on the development of an infrastructure project that is carried through by a municipality. Hester, P.T., & Adams, K.M. (2013) <doi:10.1016/j.procs.2013.09.282> Hester, P.T., Bradley, J.M., MacGregor K.A. (2012) <doi:10.1504/IJSSE.2012.052687>.

Forecasting Data using Alpha-Sutte Indicator (sutteForecastR)
The alpha-Sutte indicator (alpha-Sutte) was originally from developed of Sutte indicator. Sutte indicator can using to predict the movement of stocks. As the development of science, then Sutte indicator developed to predict not only the movement of stocks but also can forecast data on financial, insurance, and others time series data. Ahmar, A.S. (2017) <doi:10.17605/>.

Exploring Heterogeneity in Meta-Analysis using Random Forests (metaforest)
A requirement of classic meta-analysis is that the studies being aggregated are conceptually similar, and ideally, close replications. However, in many fields, there is substantial heterogeneity between studies on the same topic. Similar research questions are studied in different laboratories, using different methods, instruments, and samples. Classic meta-analysis lacks the power to assess more than a handful of univariate moderators, or to investigate interactions between moderators, and non-linear effects. MetaForest, by contrast, has substantial power to explore heterogeneity in meta-analysis. It can identify important moderators from a larger set of potential candidates, even with as little as 20 studies (Van Lissa, in preparation). This is an appealing quality, because many meta-analyses have small sample sizes. Moreover, MetaForest yields a measure of variable importance which can be used to identify important moderators, and offers partial prediction plots to explore the shape of the marginal relationship between moderators and effect size.

Calculate Gross Primary Production (GPP) from O2 Time Series (GPPFourier)
Implementation of the Fourier method to estimate aquatic gross primary production from high frequency oxygen data, described in Cox, et al (2015) <doi:10.1002/lom3.10046> and Cox, et al (2017) <doi:10.5194/bg-2017-81>.

Clustering and Feature Screening using L1 Fusion Penalty (fusionclust)
Provides the Big Merge Tracker and COSCI algorithms for convex clustering and feature screening using L1 fusion penalty. See Radchenko, P. and Mukherjee, G. (2017) <doi:10.1111/rssb.12226> and T.Banerjee et al. (2017) <doi:10.1016/j.jmva.2017.08.001> for more details.

Reinforcement Learning using the Q Learning Algorithm (QLearning)
Implements Q-Learning, a model-free form of reinforcement learning, described in work by Strehl, Li, Wiewiora, Langford & Littman (2006) <doi:10.1145/1143844.1143955>.