Seamless ‘D3Plus’ Integration (d3plus)
Provides functions that offer seamless ‘D3Plus’ integration. The examples provided here are taken from the official ‘D3Plus’ website <http://d3plus.org>.

Time Series Prediction (predtoolsTS)
Makes the time series prediction easier by automatizing this process using four main functions: prep(), modl(), pred() and postp(). Features different preprocessing methods to homogenize variance and to remove trend and seasonality. Also has the potential to bring together different predictive models to make comparatives. Features ARIMA and Data Mining Regression models (using caret).

Propensity Score Weighting Methods for Dichotomous Treatments (PSW)
Provides propensity score weighting methods to control for confounding in causal inference with dichotomous treatments and continuous/binary outcomes. It includes the following functional modules: (1) visualization of the propensity score distribution in both treatment groups with mirror histogram, (2) covariate balance diagnosis, (3) propensity score model specification test, (4) weighted estimation of treatment effect, and (5) doubly robust estimation of treatment effect. The weighting methods include the inverse probability weight (IPW) for estimating the average treatment effect (ATE), the IPW for average treatment effect of the treated (ATT), the IPW for the average treatment effect of the controls (ATC), the matching weight (MW), the overlap weight (OVERLAP), and the trapezoidal weight (TRAPEZOIDAL). Sandwich variance estimation is provided to adjust for the sampling variability of the estimated propensity score. These methods are discussed by Hirano et al (2003) <DOI:10.1111/1468-0262.00442>, Li and Greene (2013) <DOI:10.1515/ijb-2012-0030>, and Li et al (2016) <DOI:10.1080/01621459.2016.1260466>.

Dimension Reduction and Estimation Methods (Rdimtools)
We provide a rich collection of linear and nonlinear dimension reduction techniques implemented using ‘RcppArmadillo’. The question on what we should use as the target dimension is addressed by intrinsic dimension estimation methods introduced as well. For more details on dimensionality techniques, see the paper by Ma and Zhu (2013) <doi:10.1111/j.1751-5823.2012.00182.x> if you are interested in statistical approach, or Engel, Huttenberger, and Hamann (2012) <doi:10.4230/OASIcs.VLUDS.2011.135> for a broader cross-disciplinary overview.

High Performance Algorithms for Vine Copula Modeling (rvinecopulib)
Provides an interface to ‘vinecopulib’, a high performance C++ library based on ‘Boost’, ‘Eigen’ and ‘NLopt’. It provides high-performance implementations of the core features of the popular ‘VineCopula’ package, in particular inference algorithms for both vine copula and bivariate copula models. Advantages over VineCopula are a sleaker and more modern API, shorter runtimes, especially in high dimensions, nonparametric and multi-parameter families.

Tools to Complete Euclidean Distance Matrices (edmcr)
Implements the Euclidean distance matrix completion algorithms of Alfakih, Khandani, and Wolkowicz (1999) <doi:10.1023/A:1008655427845>, Trosset (2000) <doi:10.1023/A:1008722907820>, Fang and O’Leary (2012) <doi:10.1080/10556788.2011.643888>, and Rahman and Oldford (2017) <arXiv:1610.06599> the Sensor Network Localization Algorithm of Krislock and Wolkowicz (2010) <doi:10.1137/090759392>, and the molecular reconstruction algorithm of Alipanahi (2011).

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