Showing 7 of total 7 results (show query)
boost-r
mboost:Model-Based Boosting
Functional gradient descent algorithm (boosting) for optimizing general risk functions utilizing component-wise (penalised) least squares estimates or regression trees as base-learners for fitting generalized linear, additive and interaction models to potentially high-dimensional data. Models and algorithms are described in <doi:10.1214/07-STS242>, a hands-on tutorial is available from <doi:10.1007/s00180-012-0382-5>. The package allows user-specified loss functions and base-learners.
Maintained by Torsten Hothorn. Last updated 5 months ago.
boosting-algorithmsgamglmmachine-learningmboostmodellingr-languagetutorialsvariable-selectionopenblas
72 stars 12.70 score 540 scripts 27 dependents
collinerickson
GauPro:Gaussian Process Fitting
Fits a Gaussian process model to data. Gaussian processes are commonly used in computer experiments to fit an interpolating model. The model is stored as an 'R6' object and can be easily updated with new data. There are options to run in parallel, and 'Rcpp' has been used to speed up calculations. For more info about Gaussian process software, see Erickson et al. (2018) <doi:10.1016/j.ejor.2017.10.002>.
Maintained by Collin Erickson. Last updated 14 days ago.
16 stars 8.44 score 104 scripts 1 dependents
gavinsimpson
coenocliner:Coenocline Simulation
Simulate species occurrence and abundances (counts) along gradients.
Maintained by Gavin L. Simpson. Last updated 4 years ago.
12 stars 6.03 score 15 scripts 1 dependents
fcheysson
hawkesbow:Estimation of Hawkes Processes from Binned Observations
Implements an estimation method for Hawkes processes when count data are only observed in discrete time, using a spectral approach derived from the Bartlett spectrum, see Cheysson and Lang (2020) <arXiv:2003.04314>. Some general use functions for Hawkes processes are also included: simulation of (in)homogeneous Hawkes process, maximum likelihood estimation, residual analysis, etc.
Maintained by Felix Cheysson. Last updated 1 years ago.
7 stars 4.54 score 4 scripts
glandfried
TrueSkillThroughTime:Skill Estimation Based on a Single Bayesian Network
Most estimators implemented by the video game industry cannot obtain reliable initial estimates nor guarantee comparability between distant estimates. TrueSkill Through Time solves all these problems by modeling the entire history of activities using a single Bayesian network allowing the information to propagate correctly throughout the system. This algorithm requires only a few iterations to converge, allowing millions of observations to be analyzed using any low-end computer. The core ideas implemented in this project were developed by Dangauthier P, Herbrich R, Minka T, Graepel T (2007). "Trueskill through time: Revisiting the history of chess." <https://dl.acm.org/doi/10.5555/2981562.2981605>.
Maintained by Gustavo Landfried. Last updated 5 months ago.
5 stars 3.40 score 5 scripts
cran
L2DensityGoFtest:Density Goodness-of-Fit Test
Provides functions for the implementation of a density goodness-of-fit test, based on piecewise approximation of the L2 distance.
Maintained by Dimitrios Bagkavos. Last updated 2 years ago.
1.00 scorecran
NPHazardRate:Nonparametric Hazard Rate Estimation
Provides functions and examples for histogram, kernel (classical, variable bandwidth and transformations based), discrete and semiparametric hazard rate estimators.
Maintained by Dimitrios Bagkavos. Last updated 6 years ago.
1.00 score