Showing 4 of total 4 results (show query)
crunch-io
crunch:Crunch.io Data Tools
The Crunch.io service <https://crunch.io/> provides a cloud-based data store and analytic engine, as well as an intuitive web interface. Using this package, analysts can interact with and manipulate Crunch datasets from within R. Importantly, this allows technical researchers to collaborate naturally with team members, managers, and clients who prefer a point-and-click interface.
Maintained by Greg Freedman Ellis. Last updated 8 days ago.
9 stars 10.47 score 200 scripts 2 dependents
mdsr-book
mdsr:Complement to 'Modern Data Science with R'
A complement to all editions of *Modern Data Science with R* (ISBN: 978-0367191498, publisher URL: <https://www.routledge.com/Modern-Data-Science-with-R/Baumer-Kaplan-Horton/p/book/9780367191498>). This package contains data and code to complete exercises and reproduce examples from the text. It also facilitates connections to the SQL database server used in the book. All editions of the book are supported by this package.
Maintained by Benjamin S. Baumer. Last updated 7 months ago.
38 stars 7.06 score 504 scripts
tsuchiya-lab
dsdp:Density Estimation with Semidefinite Programming
The models of probability density functions are Gaussian or exponential distributions with polynomial correction terms. Using a maximum likelihood method, 'dsdp' computes parameters of Gaussian or exponential distributions together with degrees of polynomials by a grid search, and coefficient of polynomials by a variant of semidefinite programming. It adopts Akaike Information Criterion for model selection. See a vignette for a tutorial and more on our 'Github' repository <https://github.com/tsuchiya-lab/dsdp/>.
Maintained by Satoshi Kakihara. Last updated 2 years ago.
density-estimationsemidefinite-programmingfortranopenblas
3.70 score 2 scripts
carmefont
nlMS:Non-Linear Model Selection
Package to select best model among several linear and nonlinear models. The main function uses the gnls() function from the 'nlme' package to fit the data to nine regression models, named: "linear", "quadratic", "cubic", "logistic", "exponential", "power", "monod", "haldane", "logit".
Maintained by Carme Font. Last updated 6 years ago.
1.00 score