Showing 111 of total 111 results (show query)

arsilva87

soilphysics:Soil Physical Analysis

Basic and model-based soil physical analyses.

Maintained by Anderson Rodrigo da Silva. Last updated 3 years ago.

8.1 match 11 stars 4.82 score 12 scripts

mrc-ide

odin2:Next generation odin

Temporary package for rewriting odin.

Maintained by Rich FitzJohn. Last updated 2 months ago.

2.5 match 5 stars 6.32 score 22 scripts

nano-optics

mie:Mie scattering

Numerical implementation of Mie scattering theory for light scattering by spherical particles.

Maintained by Baptiste Auguie. Last updated 2 years ago.

fortran

3.5 match 8 stars 4.26 score 15 scripts

cran

psoptim:Particle Swarm Optimization

Particle swarm optimization - a basic variant.

Maintained by Krzysztof Ciupke. Last updated 9 years ago.

5.9 match 1.00 score

leef-uzh

LEEF.measurement.bemovi:Prepares Movies for Analysis with Bemovi and Extracts Data

Module for the LEEF pipeline to process bemovi data.

Maintained by Rainer M. Krug. Last updated 3 years ago.

3.7 match 1.48 score 1 dependents

calbertsen

caMisc:Different Functions

More about what it does (maybe more than one line)

Maintained by Christoffer Moesgaard Albertsen. Last updated 9 months ago.

cpp

2.0 match 1.70 score

meenakshi-kushwaha

mmaqshiny:Explore Air Quality Mobile-Monitoring Data

Mobile-monitoring or sensors on a mobile platform, is an increasingly popular approach to measure high-resolution pollution data at the street level. Coupled with location data, spatial visualization of air-quality parameters helps detect localized areas of high air pollution, also called hotspots. In this approach, portable sensors are mounted on a vehicle and driven on predetermined routes to collect high frequency data (1 Hz). 'mmaqshiny' is for analysing, visualizing and spatial mapping of high-resolution air-quality data collected by specific devices installed on a moving platform. 1 Hz data of PM2.5 (mass concentrations of particulate matter with size less than 2.5 microns), Black carbon mass concentrations (BC), ultra-fine particle number concentrations, carbon dioxide along with GPS coordinates and relative humidity (RH) data collected by popular portable instruments (TSI DustTrak-8530, Aethlabs microAeth-AE51, TSI CPC3007, LICOR Li-830, Garmin GPSMAP 64s, Omega USB RH probe respectively). It incorporates device specific cleaning and correction algorithms. RH correction is applied to DustTrak PM2.5 following the Chakrabarti et al., (2004) <doi:10.1016/j.atmosenv.2004.03.007>. Provision is given to add linear regression coefficients for correcting the PM2.5 data (if required). BC data will be cleaned for the vibration generated noise, by adopting the statistical procedure as explained in Apte et al., (2011) <doi:10.1016/j.atmosenv.2011.05.028>, followed by a loading correction as suggested by Ban-Weiss et al., (2009) <doi:10.1021/es8021039>. For the number concentration data, provision is given for dilution correction factor (if a diluter is used with CPC3007; default value is 1). The package joins the raw, cleaned and corrected data from the above said instruments and outputs as a downloadable csv file.

Maintained by Adithi R. Upadhya. Last updated 3 years ago.

0.5 match 5 stars 3.70 score 4 scripts

lala-s-riza

metaheuristicOpt:Metaheuristic for Optimization

An implementation of metaheuristic algorithms for continuous optimization. Currently, the package contains the implementations of 21 algorithms, as follows: particle swarm optimization (Kennedy and Eberhart, 1995), ant lion optimizer (Mirjalili, 2015 <doi:10.1016/j.advengsoft.2015.01.010>), grey wolf optimizer (Mirjalili et al., 2014 <doi:10.1016/j.advengsoft.2013.12.007>), dragonfly algorithm (Mirjalili, 2015 <doi:10.1007/s00521-015-1920-1>), firefly algorithm (Yang, 2009 <doi:10.1007/978-3-642-04944-6_14>), genetic algorithm (Holland, 1992, ISBN:978-0262581110), grasshopper optimisation algorithm (Saremi et al., 2017 <doi:10.1016/j.advengsoft.2017.01.004>), harmony search algorithm (Mahdavi et al., 2007 <doi:10.1016/j.amc.2006.11.033>), moth flame optimizer (Mirjalili, 2015 <doi:10.1016/j.knosys.2015.07.006>, sine cosine algorithm (Mirjalili, 2016 <doi:10.1016/j.knosys.2015.12.022>), whale optimization algorithm (Mirjalili and Lewis, 2016 <doi:10.1016/j.advengsoft.2016.01.008>), clonal selection algorithm (Castro, 2002 <doi:10.1109/TEVC.2002.1011539>), differential evolution (Das & Suganthan, 2011), shuffled frog leaping (Eusuff, Landsey & Pasha, 2006), cat swarm optimization (Chu et al., 2006), artificial bee colony algorithm (Karaboga & Akay, 2009), krill-herd algorithm (Gandomi & Alavi, 2012), cuckoo search (Yang & Deb, 2009), bat algorithm (Yang, 2012), gravitational based search (Rashedi et al., 2009) and black hole optimization (Hatamlou, 2013).

Maintained by Lala Septem Riza. Last updated 6 years ago.

0.5 match 4 stars 3.38 score 66 scripts 3 dependents