Density estimation is an important statistical tool, and within R there are over 20 packages that implement it: so many that it is often difficult to know which to use. This paper presents a brief outline of the theory underlying each package, as well as an overview of the code and comparison of speed and accuracy. We focus on univariate methods, but include pointers to other more specialised p...

MOTIVATION spacodiR is a cross-platform package, written for the R environment, for studying partitioning of diversity among natural communities in space and time. Complementing and extending existing software, spacodiR allows for hypothesis testing and parameter estimation in studying spatial structuring of species-, phylogenetic- and trait diversities. AVAILABILITY Integrated with other sof...

We present the RWiener package that provides R functions for the Wiener diffusion model. The core of the package are the four distribution functions dwiener, pwiener, qwiener and rwiener, which use up-to-date methods, implemented in C, and provide fast and accurate computation of the density, distribution, and quantile function, as well as a random number generator for the Wiener diffusion mode...

this study focuses on the forecasting of energy demands of residential and commercial sectors using linear and exponential functions. the coefficients were obtained from genetic and particle swarm optimization (pso) algorithms. totally, 72 different scenarios with various inputs were investigated. consumption data in respect of residential and commercial sectors in iran were collected from the ...

Variable selection via penalized estimation is appealing for dimension reduction. For penalized linear regression, Efron, et al. (2004) introduced the LARS algorithm. Recently, the coordinate descent (CD) algorithm was developed by Friedman, et al. (2007) for penalized linear regression and penalized logistic regression and was shown to gain computational superiority. This paper explores...

In this paper the problem of parameter identification for the Monod model is considered. As known for a long time, noisy batch measurements do not allow unique and accurate estimation of the kinetic parameters of the Monod model. Techniques of optimal experiment design are, therefore, addressed to design informative experiments and improve the parameter estimation accuracy. During the design pr...

2017 1. Using information given in appendix A, identify with the periodogram the period of the main seasonal pattern in the time series beer 1. 2. Using information given in appendix B, identify with the periodogram the period of the main seasonal pattern in the time series airpass. 3. Explain R code in appendices A and B. 1 time series are taken from the R package fma, and R outputs are shown ...

this paper presents a probabilistic confidence-interval based method for optimal placement of shunt capacitors in distribution networks by considering probabilistic characteristics of loads. the main objective function are reducing loss and improving the voltage profile. backward forward sweep method has been employed to obtain the power flow results in distribution system. in addition, integer...

We revisit the problem of (1±ε)-approximating the Lp norm, 0 ≤ p ≤ 2, of an n-dimensional vector updated in a stream of length m with positive and negative updates to its coordinates. We give several new upper and lower bounds, some of which are optimal. LOWER BOUNDS.We show that for the interesting range of parameters, Ω(ε log(nm)) bits of space are necessary for estimating Lp in one pass for ...

Random number generators are critically important for computational statistics. Simulation methods are becoming ever more common for estimation; Monte Carlo Markov Chain is but one approach. Also, simulation methods such as the Bootstrap have long been used in inference and are becoming a standard part of a rigorous analysis. As random number generators are at the heart of the simulation-based ...