Showing 57 of total 57 results (show query)

steve-the-bayesian

bsts:Bayesian Structural Time Series

Time series regression using dynamic linear models fit using MCMC. See Scott and Varian (2014) <DOI:10.1504/IJMMNO.2014.059942>, among many other sources.

Maintained by Steven L. Scott. Last updated 1 years ago.

cpp

5.3 match 33 stars 6.54 score 338 scripts 3 dependents

berndbischl

BBmisc:Miscellaneous Helper Functions for B. Bischl

Miscellaneous helper functions for and from B. Bischl and some other guys, mainly for package development.

Maintained by Bernd Bischl. Last updated 2 years ago.

1.9 match 20 stars 10.59 score 980 scripts 69 dependents

geoffjentry

twitteR:R Based Twitter Client

Provides an interface to the Twitter web API.

Maintained by Jeff Gentry. Last updated 9 years ago.

1.9 match 254 stars 10.18 score 2.0k scripts 1 dependents

repboxr

repboxUtils:Utility functions shared by several repbox packages

Utility functions shared by several repbox packages

Maintained by Sebastian Kranz. Last updated 30 days ago.

4.5 match 4.21 score 9 dependents

pik-piam

mip:Comparison of multi-model runs

Package contains generic functions to produce comparison plots of multi-model runs.

Maintained by David Klein. Last updated 26 days ago.

1.8 match 1 stars 8.08 score 70 scripts 20 dependents

beckerbenj

eatGADS:Data Management of Large Hierarchical Data

Import 'SPSS' data, handle and change 'SPSS' meta data, store and access large hierarchical data in 'SQLite' data bases.

Maintained by Benjamin Becker. Last updated 23 days ago.

1.7 match 1 stars 7.36 score 34 scripts 1 dependents

mthrun

DatabionicSwarm:Swarm Intelligence for Self-Organized Clustering

Algorithms implementing populations of agents that interact with one another and sense their environment may exhibit emergent behavior such as self-organization and swarm intelligence. Here, a swarm system called Databionic swarm (DBS) is introduced which was published in Thrun, M.C., Ultsch A.: "Swarm Intelligence for Self-Organized Clustering" (2020), Artificial Intelligence, <DOI:10.1016/j.artint.2020.103237>. DBS is able to adapt itself to structures of high-dimensional data such as natural clusters characterized by distance and/or density based structures in the data space. The first module is the parameter-free projection method called Pswarm (Pswarm()), which exploits the concepts of self-organization and emergence, game theory, swarm intelligence and symmetry considerations. The second module is the parameter-free high-dimensional data visualization technique, which generates projected points on the topographic map with hypsometric tints defined by the generalized U-matrix (GeneratePswarmVisualization()). The third module is the clustering method itself with non-critical parameters (DBSclustering()). Clustering can be verified by the visualization and vice versa. The term DBS refers to the method as a whole. It enables even a non-professional in the field of data mining to apply its algorithms for visualization and/or clustering to data sets with completely different structures drawn from diverse research fields. The comparison to common projection methods can be found in the book of Thrun, M.C.: "Projection Based Clustering through Self-Organization and Swarm Intelligence" (2018) <DOI:10.1007/978-3-658-20540-9>.

Maintained by Michael Thrun. Last updated 1 years ago.

openblascpp

1.8 match 12 stars 6.16 score 27 scripts 1 dependents

repboxr

repboxStata:Repbox analysis of stata scripts in reproduction packages

Repbox analysis of stata scripts in reproduction packages

Maintained by Sebastian Kranz. Last updated 30 days ago.

2.3 match 2.73 score 4 scripts 2 dependents

repboxr

repboxRfun:Repbox functions called by code injections into R scripts

Try to use as little special dependencies as reasonably possible

Maintained by Sebastian Kranz. Last updated 1 years ago.

2.3 match 2.18 score 1 dependents

wernerstahel

plgraphics:User Oriented Plotting Functions

Plots with high flexibility and easy handling, including informative regression diagnostics for many models.

Maintained by Werner A. Stahel. Last updated 1 years ago.

2.3 match 2.08 score 12 scripts

cran

BivRegBLS:Tolerance Interval and EIV Regression - Method Comparison Studies

Assess the agreement in method comparison studies by tolerance intervals and errors-in-variables (EIV) regressions. The Ordinary Least Square regressions (OLSv and OLSh), the Deming Regression (DR), and the (Correlated)-Bivariate Least Square regressions (BLS and CBLS) can be used with unreplicated or replicated data. The BLS() and CBLS() are the two main functions to estimate a regression line, while XY.plot() and MD.plot() are the two main graphical functions to display, respectively an (X,Y) plot or (M,D) plot with the BLS or CBLS results. Four hyperbolic statistical intervals are provided: the Confidence Interval (CI), the Confidence Bands (CB), the Prediction Interval and the Generalized prediction Interval. Assuming no proportional bias, the (M,D) plot (Band-Altman plot) may be simplified by calculating univariate tolerance intervals (beta-expectation (type I) or beta-gamma content (type II)). Major updates from last version 1.0.0 are: title shortened, include the new functions BLS.fit() and CBLS.fit() as shortcut of the, respectively, functions BLS() and CBLS(). References: B.G. Francq, B. Govaerts (2016) <doi:10.1002/sim.6872>, B.G. Francq, B. Govaerts (2014) <doi:10.1016/j.chemolab.2014.03.006>, B.G. Francq, B. Govaerts (2014) <http://publications-sfds.fr/index.php/J-SFdS/article/view/262>, B.G. Francq (2013), PhD Thesis, UCLouvain, Errors-in-variables regressions to assess equivalence in method comparison studies, <https://dial.uclouvain.be/pr/boreal/object/boreal%3A135862/datastream/PDF_01/view>.

Maintained by Bernard G Francq. Last updated 5 years ago.

0.5 match 1.63 score 43 scripts