Graphviz Pipeline Plot Based on Grids (grPipe: Graphviz
Pipeline)
person("Daniel", "Gaspar Gonçalves", email = "daniel.gaspar.goncalves@gmail.com", role = c("aut", "cre"))
Create a grid-based graphviz using the following functions:
1 - Creating the data.frame where the nodes are;
2 - Adding and editing nodes;
3 - Plotting these nodes.
DiagrammeR, DiagrammeRsvg, dplyr, magrittr, rsvg, png, grid

Stochastic Quasi-Gradient Differential Evolution Optimization
An optim-style implementation of the Stochastic Quasi-Gradient Differential Evolution (SQG-DE) optimization algorithm first published by Sala, Baldanzini, and Pierini (2018; <doi:10.1007/978-3-319-72926-8_27>). This optimization algorithm fuses the robustness of the population-based global optimization algorithm "Differential Evolution" with the efficiency of gradient-based optimization. The derivative-free algorithm uses population members to build stochastic gradient estimates, without any additional objective function evaluations. Sala, Baldanzini, and Pierini argue this algorithm is useful for 'difficult optimization problems under a tight function evaluation budget.' This package can run SQG-DE in parallel and sequentially.

Probability of Detection for Grab Sample Selection
c(
person("Mayooran", "Thevaraja", email = "mayooran@eng.jfn.ac.lk", role = c("aut", "cre")),
person("Kondaswamy", "Govindaraju", email = "k.govindaraju@massey.ac.nz", role = "aut"),
person("Mark", "Bebbington", email = "m.bebbington@massey.ac.nz", role = "aut")
)
Functions for obtaining the probability of detection, for grab samples selection by using two different methods such as systematic or random based on two-state Markov chain model. For detection probability calculation, we used results from Bhat, U. and Lal, R. (1988) <doi:10.2307/1427041>.
extraDistr, ggplot2, ggthemes, plyr, reshape2, stats
Mayooran Thevaraja [aut, cre],
Kondaswamy Govindaraju [aut],
Mark Bebbington [aut]

Dendi Handian, Imam Fachmi Nasrulloh, Lala Septem Riza
An implementation of various learning algorithms based on Gradient Descent for dealing with regression tasks.
The variants of gradient descent algorithm are :
Mini-Batch Gradient Descent (MBGD), which is an optimization to use training data partially to reduce the computation load.
Stochastic Gradient Descent (SGD), which is an optimization to use a random data in learning to reduce the computation load drastically.
Stochastic Average Gradient (SAG), which is a SGD-based algorithm to minimize stochastic step to average.
Momentum Gradient Descent (MGD), which is an optimization to speed-up gradient descent learning.
Accelerated Gradient Descent (AGD), which is an optimization to accelerate gradient descent learning.
Adagrad, which is a gradient-descent-based algorithm that accumulate previous cost to do adaptive learning.
Adadelta, which is a gradient-descent-based algorithm that use hessian approximation to do adaptive learning.
RMSprop, which is a gradient-descent-based algorithm that combine Adagrad and Adadelta adaptive learning ability.
Adam, which is a gradient-descent-based algorithm that mean and variance moment to do adaptive learning.