Description: Computes exact p-values for multinomial goodness-of-fit tests based on multiple test statistics, namely, Pearson's chi-square, the log-likelihood ratio and the probability mass statistic. Implements the algorithm detailed in Resin (2020) <arXiv:2008.12682>. Estimates based on the classical asymptotic chi-square approximation or Monte-Carlo simulation can also be computed.

Package: ExactNumCI
Type: Package
Title: Exact Confidence Interval for binomial proportions
Version: 1.0.0
Date: 2012-12-14
Author: Deqiang Sun, Hyun Jung Park
Maintainer: Deqiang Sun <deqiangs@bcm.edu>
Description: This package calculates the Confidence Interval (CI) for
single binomial proportion, CI for difference of two binomial
proportions, and CI for difference of difference.  There are 4
major functions available: singleCI(k,n) gives the CI for
binomial proportion given (k,n), pdiff(k1,n1,k2,n2, d) gives
the probability that the difference of p1 from p2 is greater
than d, and pdiffCI(k1,n1, k2,n2) gives the CI for difference
of two binomial proportions given (k1,n1, k2, n2).
numCI(k1,n1, k2,n2, K1, N1,K2,N2) gives the CI for difference
of difference (p1-p2) - (P1-P2).  At default, a uniform priori
distribution of p is assumed.  You may also specify the priori
distribution parameter a0, b0 by assuming p follows a
nonuniform beta distribution beta(a0, b0).  Note that boost
header files are needed to build the package.
License: GPL
Depends: Rcpp
Imports: Rcpp
LinkingTo: Rcpp
SystemRequirements: Boost
Packaged: 2013-02-08 06:13:55 UTC; deqiangs
Repository: CRAN
Date/Publication: 2013-02-09 15:27:12

Title: Exact solution paths for regularized LASSO regressions with L_1
Description: ExactPath implements an algorithm for exact LASSO
solution. Two methods are provided to print and visualize the
whole solution paths. Use ?ExactPath to see an introduction.
Packages ncvreg and lars are required so that their data sets

Title: ExactSampling: risk evaluation using exact resampling methods
Description: ExactSampling contains functions that estimate the risk
associated to a kNN prediction function (either in regression
or binary classification) based on resampling method
(leave-p-out cross-validation or bootstrap). It also contain a
function knn.search to obtain the list of k nearest neighbors
for each point in the sample, and a function to compute the
empirical risk of a kNN prediction function.