Estimates of Standard Errors for Risk and Performance Measures (RPESE)
Estimates of standard errors of popular risk and performance measures for asset or portfolio returns using methods as described in Chen and Martin (2019) <>.

A Dipping Sauce for Data Analysis and Visualizations (dipsaus)
Works as ‘add-ons’ to packages like ‘shiny’, ‘future’, as well as ‘rlang’, and provides utility functions. Just like dipping sauce adding flavors to potato chips or pita bread, ‘dipsaus’ for data analysis and visualizations adds handy functions and enhancements to popular packages. The goal is to provide simple solutions that are frequently asked for online, such as how to synchronize ‘shiny’ inputs without freezing the app, or how to get memory size on ‘Linux’ or ‘MacOS’ system. The enhancements roughly fall into these four categories: 1. ‘shiny’ input widgets; 2. high-performance computing using ‘RcppParallel’ and ‘future’ package; 3. modify R calls and convert among numbers, strings, and other objects. 4. utility functions to get system information such like CPU chipset, memory limit, etc.

Uncertainties of Climate Projections using Smoothing Splines (qualypsoss)
These functions use smoothing-splines, data augmentation and Bayesian techniques for the assessment of single-member and incomplete ensembles of climate projections. – Cheng, C.-I. and P. L. Speckman (2012) <doi:10.1016/j.csda.2012.05.020>. – Evin, G., B. Hingray, J. Blanchet, N. Eckert, S. Morin, and D. Verfaillie. (2019) <doi:10.1175/JCLI-D-18-0606.1>.

Identify Zero-Inflated Distributions (iZID)
Computes bootstrapped Monte Carlo estimate of p value of Kolmogorov-Smirnov (KS) test and likelihood ratio test for zero-inflated count data, based on the work of Aldirawi et al. (2019) <doi:10.1109/BHI.2019.8834661>. With the package, user can also find tools to simulate random deviates from zero inflated or hurdle models and obtain maximum likelihood estimate of unknown parameters in these models.