Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure (diffee)
This is an R implementation of Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure (DIFFEE). The DIFFEE algorithm can be used to fast estimate the differential network between two related datasets. For instance, it can identify differential gene network from datasets of case and control. By performing data-driven network inference from two high-dimensional data sets, this tool can help users effectively translate two aggregated data blocks into knowledge of the changes among entities between two Gaussian Graphical Model. Please run demo(diffeeDemo) to learn the basic functions provided by this package. For further details, please read the original paper: Beilun Wang, Arshdeep Sekhon, Yanjun Qi (2018) <arXiv:1710.11223>.

Isolate-Detect Methodology for Multiple Change-Point Detection (IDetect)
Provides efficient implementation of the Isolate-Detect methodology for the consistent estimation of the number and location of multiple change-points in one-dimensional data sequences from the ‘deterministic + noise’ model. For details on the Isolate-Detect methodology, please see Anastasiou and Fryzlewicz (2018) <https://…6a0866c574654163b8255e272bc0001b.pdf>. Currently implemented scenarios are: piecewise-constant signal with Gaussian noise, piecewise-constant signal with heavy-tailed noise, continuous piecewise-linear signal with Gaussian noise, continuous piecewise-linear signal with heavy-tailed noise.

Effortless Exception Logging (loggit)
A very simple and easy-to-use set of suspiciously-familiar functions. ‘loggit’ provides a set of wrappings for base R’s message(), warning(), and stop() functions that maintain identical functionality, but also log the handler message to a ‘JSON’ log file. While mostly automatic, powerful custom logging is available via these handlers’ logging function, loggit(), which is also exported for use. No change in existing code is necessary to use this package.

Nonparametric Models for Longitudinal Data (npmlda)
Support the book: Wu CO and Tian X (2018). Nonparametric Models for Longitudinal Data. Chapman & Hall/CRC (to appear); and provide fit for using global and local smoothing methods for the conditional-mean and conditional-distribution based models with longitudinal Data.

Robust Non-Linear Regression using AIC Scores (nls.multstart)
Non-linear least squares regression with the Levenberg-Marquardt algorithm using multiple starting values for increasing the chance that the minimum found is the global minimum.