Package: logcondens 2.1.8

logcondens: Estimate a Log-Concave Probability Density from Iid Observations

Given independent and identically distributed observations X(1), ..., X(n), compute the maximum likelihood estimator (MLE) of a density as well as a smoothed version of it under the assumption that the density is log-concave, see Rufibach (2007) and Duembgen and Rufibach (2009). The main function of the package is 'logConDens' that allows computation of the log-concave MLE and its smoothed version. In addition, we provide functions to compute (1) the value of the density and distribution function estimates (MLE and smoothed) at a given point (2) the characterizing functions of the estimator, (3) to sample from the estimated distribution, (5) to compute a two-sample permutation test based on log-concave densities, (6) the ROC curve based on log-concave estimates within cases and controls, including confidence intervals for given values of false positive fractions (7) computation of a confidence interval for the value of the true density at a fixed point. Finally, three datasets that have been used to illustrate log-concave density estimation are made available.

Authors:Kaspar Rufibach <[email protected]> and Lutz Duembgen <[email protected]>

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NEWS

# Install 'logcondens' in R:
install.packages('logcondens', repos = c('https://numbersman77.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • brightstar - Bright star dataset used to illustrate log-concave density estimation
  • pancreas - Data from pancreatic cancer serum biomarker study
  • reliability - Reliability dataset used to illustrate log-concave density estimation

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

41 exports 0.36 score 13 dependencies 1 dependents 31 scripts 1.7k downloads

Last updated 1 years agofrom:d41fae65c8. Checks:OK: 7. Indexed: yes.

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Doc / VignettesOKSep 15 2024
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R-4.5-linuxOKSep 15 2024
R-4.4-winOKSep 15 2024
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Exports:activeSetLogConc2hatconfIntBootLogConROC_t0etaphievaluateLogConDensftildeicmaLogConintECDFintFisoMeanJ00J10J11J20Lhat_etaLocal_LLLocal_LL_allLocalCoarsenLocalConvexityLocalExtendLocalFLocalMLELocalNormalizeLocalVariancelogConCIlogConDenslogConROClogconTwoSamplemaxDiffCDFMLEphietaplot.dlcpreProcessQ00qloglinquadDerivquantilesLogConDensrlogconrobustROCxsummary.dlc

Dependencies:FNNkernlabKernSmoothkslatticeMatrixmclustmgcvmulticoolmvtnormnlmepracmaRcpp

logcondens: Computations Related to Univariate Log-Concave Density Estimation (Duembgen and Rufibach, 2011, Journal of Statistical Software, 39(6), 1-28.)

Rendered fromlogcondens.Rnwusingutils::Sweaveon Sep 15 2024.

Last update: 2023-08-22
Started: 2013-12-14

Readme and manuals

Help Manual

Help pageTopics
Estimate a Log-Concave Probability Density from iid Observationslogcondens-package log-concave logcon logcondens
Computes a Log-Concave Probability Density Estimate via an Active Set AlgorithmactiveSet activeSetLogCon
Auxiliary Numerical Routines for the Function activeSetLogConactiveSetRoutines LocalCoarsen LocalConvexity LocalExtend LocalF LocalMLE LocalNormalize LocalVariance
Bright star dataset used to illustrate log-concave density estimationbrightstar
Function to compute a bootstrap confidence interval for the ROC curve at a given t, based on the log-concave ROC curveconfIntBootLogConROC_t0
Evaluates the Log-Density MLE and Smoothed Estimator at Arbitrary Real Numbers xsevaluateLogConDens
Computes a Log-Concave Probability Density Estimate via an Iterative Convex Minorant AlgorithmicmaLogCon
Computes the Integrated Empirical Distribution Function at Arbitrary Real Numbers in sintECDF
Computes the Integral of the estimated CDF at Arbitrary Real Numbers in sintF
Pool-Adjacent Violaters Algorithm: Least Square Fit under Monotonicity ConstraintisoMean
Numerical Routine J and Some DerivativesJ00 J10 J11 J20 Jfunctions
Value of the Log-Likelihood Function L, where Input is in Eta-ParametrizationLhat_eta
Value of the Log-Likelihood Function L, where Input is in Phi-ParametrizationLocal_LL
Log-likelihood, New Candidate and Directional Derivative for LLocal_LL_all
Compute pointwise confidence interval for a density assuming log-concavitylogConCI
Functions that are used by logConCIc2hat ftilde logConCIfunctions rLCD
Compute log-concave density estimator and related quantitieslogConDens
Compute ROC curve based on log-concave estimates for the constituent distributionslogConROC
Compute p-values for two-sample test based on log-concave CDF estimateslogconTwoSample
Compute maximal difference between CDFs corresponding to log-concave estimatesmaxDiffCDF
Unconstrained piecewise linear MLEMLE
Data from pancreatic cancer serum biomarker studypancreas
Standard plots for a dlc objectplot.dlc
Compute a weighted sample from initial observationspreProcess
Numerical Routine QQ00
Quantile Function In a Simple Log-Linear modelqloglin
Gradient and Diagonal of Hesse Matrix of Quadratic Approximation to Log-Likelihood Function LquadDeriv
Function to compute Quantiles of FhatquantilesLogConDens
Reliability dataset used to illustrate log-concave density estimationreliability
Changes Between Parametrizationsetaphi phieta reparametrizations
Generate random sample from the log-concave and the smoothed log-concave density estimatorrlogcon
Robustification and Hermite Interpolation for ICMArobust
Compute ROC curve at a given x based on log-concave estimates for the constituent distributionsROCx
Summarizing log-concave density estimationsummary.dlc