Showing 10 of total 10 results (show query)
bioc
MSnbase:Base Functions and Classes for Mass Spectrometry and Proteomics
MSnbase provides infrastructure for manipulation, processing and visualisation of mass spectrometry and proteomics data, ranging from raw to quantitative and annotated data.
Maintained by Laurent Gatto. Last updated 15 days ago.
immunooncologyinfrastructureproteomicsmassspectrometryqualitycontroldataimportbioconductorbioinformaticsmass-spectrometryproteomics-datavisualisationcpp
131 stars 12.76 score 772 scripts 36 dependents
acguidoum
Sim.DiffProc:Simulation of Diffusion Processes
It provides users with a wide range of tools to simulate, estimate, analyze, and visualize the dynamics of stochastic differential systems in both forms Ito and Stratonovich. Statistical analysis with parallel Monte Carlo and moment equations methods of SDEs <doi:10.18637/jss.v096.i02>. Enabled many searchers in different domains to use these equations to modeling practical problems in financial and actuarial modeling and other areas of application, e.g., modeling and simulate of first passage time problem in shallow water using the attractive center (Boukhetala K, 1996) ISBN:1-56252-342-2.
Maintained by Arsalane Chouaib Guidoum. Last updated 1 years ago.
dynamic-systemmoment-equationsmonte-carlo-simulationparallel-computingstochastic-calculusstochastic-differential-equationtransition-density
13 stars 7.69 score 86 scripts 4 dependents
snoweye
EMCluster:EM Algorithm for Model-Based Clustering of Finite Mixture Gaussian Distribution
EM algorithms and several efficient initialization methods for model-based clustering of finite mixture Gaussian distribution with unstructured dispersion in both of unsupervised and semi-supervised learning.
Maintained by Wei-Chen Chen. Last updated 7 months ago.
18 stars 7.53 score 123 scripts 2 dependents
freezenik
bamlss:Bayesian Additive Models for Location, Scale, and Shape (and Beyond)
Infrastructure for estimating probabilistic distributional regression models in a Bayesian framework. The distribution parameters may capture location, scale, shape, etc. and every parameter may depend on complex additive terms (fixed, random, smooth, spatial, etc.) similar to a generalized additive model. The conceptual and computational framework is introduced in Umlauf, Klein, Zeileis (2019) <doi:10.1080/10618600.2017.1407325> and the R package in Umlauf, Klein, Simon, Zeileis (2021) <doi:10.18637/jss.v100.i04>.
Maintained by Nikolaus Umlauf. Last updated 6 months ago.
1 stars 5.76 score 239 scripts 5 dependents
munterfi
eRTG3D:Empirically Informed Random Trajectory Generation in 3-D
Creates realistic random trajectories in a 3-D space between two given fix points, so-called conditional empirical random walks (CERWs). The trajectory generation is based on empirical distribution functions extracted from observed trajectories (training data) and thus reflects the geometrical movement characteristics of the mover. A digital elevation model (DEM), representing the Earth's surface, and a background layer of probabilities (e.g. food sources, uplift potential, waterbodies, etc.) can be used to influence the trajectories. Unterfinger M (2018). "3-D Trajectory Simulation in Movement Ecology: Conditional Empirical Random Walk". Master's thesis, University of Zurich. <https://www.geo.uzh.ch/dam/jcr:6194e41e-055c-4635-9807-53c5a54a3be7/MasterThesis_Unterfinger_2018.pdf>. Technitis G, Weibel R, Kranstauber B, Safi K (2016). "An algorithm for empirically informed random trajectory generation between two endpoints". GIScience 2016: Ninth International Conference on Geographic Information Science, 9, online. <doi:10.5167/uzh-130652>.
Maintained by Merlin Unterfinger. Last updated 3 years ago.
3dbirdsconditional-empirical-random-walkgliding-and-soaringmachine-learningmovement-ecologyrandom-trajectory-generatorrandom-walksimulationtrajectory-generation
6 stars 5.71 score 19 scripts
trn000
norMmix:Direct MLE for Multivariate Normal Mixture Distributions
Multivariate Normal (i.e. Gaussian) Mixture Models (S3) Classes. Fitting models to data using 'MLE' (maximum likelihood estimation) for multivariate normal mixtures via smart parametrization using the 'LDL' (Cholesky) decomposition, see McLachlan and Peel (2000, ISBN:9780471006268), Celeux and Govaert (1995) <doi:10.1016/0031-3203(94)00125-6>.
Maintained by Nicolas Trutmann. Last updated 7 months ago.
gaussian-mixture-modelsmaximum-likelihood-estimationr-language
4.18 score 3 scriptsfreezenik
R2BayesX:Estimate Structured Additive Regression Models with 'BayesX'
An R interface to estimate structured additive regression (STAR) models with 'BayesX'.
Maintained by Nikolaus Umlauf. Last updated 4 days ago.
1 stars 3.55 score 118 scripts 1 dependents
intercapackage
interca:Multiple Correspondence Analysis Based on Interpretive Coordinates
Various functions and a Shiny app to enrich the results of Multiple Correspondence Analysis with interpretive axes and planes (see Moschidis, Markos, and Thanopoulos, 2022; <doi:10.1108/ACI-07-2022-0191>).
Maintained by Stratos Moschidis. Last updated 2 years ago.
1.70 score 1 scripts
cran
WPKDE:Weighted Piecewise Kernel Density Estimation
Weighted Piecewise Kernel Density Estimation for large data.
Maintained by Kunyu Ye. Last updated 8 years ago.
1.00 score
jennyfarmer
PDFEstimator:Multivariate Nonparametric Probability Density Estimator
Farmer, J., D. Jacobs (2108) <DOI:10.1371/journal.pone.0196937>. A multivariate nonparametric density estimator based on the maximum-entropy method. Accurately predicts a probability density function (PDF) for random data using a novel iterative scoring function to determine the best fit without overfitting to the sample.
Maintained by Jenny Farmer. Last updated 2 years ago.
1.00 score 2 scripts