Title: Interface to the Penn Machine Learning Benchmarks Data
Description: Check available classification and regression data sets from the PMLB repository and download them.
The PMLB repository (<https://github.com/EpistasisLab/pmlbr>) contains a curated collection of data sets for evaluating and comparing machine learning algorithms.
These data sets cover a range of applications, and include binary/multi-class classification problems and
regression problems, as well as combinations of categorical, ordinal, and continuous features.
There are currently over 150 datasets included in the PMLB repository.
person("Trang", "Le", email = "grixor@gmail.com", role = c("aut", "cre"), comment = "https://trang.page/"),
person("makeyourownmaker", email = "makeyourownmaker@gmx.com", role = "aut", comment = "https://github.com/makeyourownmaker"),
person("Jason", "Moore", email = "jhmoore@upenn.edu", role = "aut", comment = "http://www.epistasisblog.org/"),
person("University of Pennsylvania", role = "cph"))
Author: Trang Le [aut, cre] (https://trang.page/),

Title: Deconvolution Density Estimation using Penalized MLE
Description: Given a sample with additive measurement error, the package estimates the deconvolution density - that is, the density of the underlying distribution of the sample without measurement error. The method maximises the log-likelihood of the estimated density, plus a quadratic smoothness penalty. The distribution of the measurement error can be either a known family, or can be estimated from a "pure error" sample. For known error distributions, the package supports Normal, Laplace or Beta distributed error. For unknown error distribution, a pure error sample independent from the data is used.

Authors@R: person(given = "Yun",family = "Cai",role = c("aut", "cre"),email = "Yun.Cai@dal.ca")
Description: Performs penalized MLE deconvolution  estimation.

Package: pmlr
Title: Penalized Multinomial Logistic Regression
Version: 1.0
Date: 2010-03-28
Author: Sarah Colby <colby@lunenfeld.ca>, Sophia Lee, Juan Pablo
Lewinger, Shelley Bull <bull@lunenfeld.ca>
Maintainer: Sarah Colby <colby@lunenfeld.ca>
Description: Extends the approach proposed by Firth (1993) for bias
reduction of MLEs in exponential family models to the
multinomial logistic regression model with general covariate
types.  Modification of the logistic regression score function
to remove first-order bias is equivalent to penalizing the
likelihood by the Jeffreys prior, and yields penalized maximum
likelihood estimates (PLEs) that always exist.  Hypothesis
testing is conducted via likelihood ratio statistics.  Profile
confidence intervals (CI) are constructed for the PLEs.
License: GPL (>= 2)
Packaged: 2010-03-29 02:29:55 UTC; Dao & Sarah
Repository: CRAN
Date/Publication: 2010-04-02 17:08:39