Package: iclogcondist 1.0.1

Chaoyu Yuan

iclogcondist: Log-Concave Distribution Estimation with Interval-Censored Data

We consider the non-parametric maximum likelihood estimation of the underlying distribution function, assuming log-concavity, based on mixed-case interval-censored data. The algorithm implemented is base on Chi Wing Chu, Hok Kan Ling and Chaoyu Yuan (2024, <doi:10.48550/arXiv.2411.19878>).

Authors:Chi Wing Chu [aut], Hok Kan Ling [aut], Chaoyu Yuan [aut, cre]

iclogcondist_1.0.1.tar.gz
iclogcondist_1.0.1.zip(r-4.7-x86_64)iclogcondist_1.0.1.zip(r-4.7-arm64)iclogcondist_1.0.1.zip(r-4.6-x86_64)iclogcondist_1.0.1.zip(r-4.6-arm64)
iclogcondist_1.0.1.tar.gz(r-4.7-arm64)iclogcondist_1.0.1.tar.gz(r-4.7-x86_64)iclogcondist_1.0.1.tar.gz(r-4.6-arm64)iclogcondist_1.0.1.tar.gz(r-4.6-x86_64)
iclogcondist_1.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
iclogcondist/json (API)

# Install 'iclogcondist' in R:
install.packages('iclogcondist', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • lgnm - LGNM Data: Case II Interval Censoring Example

On CRAN:

Conda:

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

cpp

1.70 score 2 scripts 445 downloads 15 exports 61 dependencies

Last updated from:0f69c495a7. Checks:10 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK140
linux-devel-x86_64OK167
source / vignettesOK220
linux-release-arm64OK140
linux-release-x86_64OK176
windows-devel-arm64OK245
windows-devel-x86_64OK169
windows-release-arm64OK281
windows-release-x86_64OK212
wasm-releaseOK126

Exports:get_F_at_xic_LCM_UMLEic_LCMLEic_UMLEiclogcondist_visualizationptllogisptlnormptweibullqtllogisqtlnormqtweibullrtllogisrtlnormrtweibullsimulate_ic_data

Dependencies:assertthatbbmlebdsmatrixclicodacodetoolscpp11data.tabledeSolvedplyrfarverfastGHQuadfdrtoolflexsurvforeachgenericsggplot2gluegtableicenRegisobanditeratorslabelinglatticelifecyclelsodamagrittrMASSMatrixmgcvMLEcensmonotonemstatemuhazmvtnormnlmenumDerivpillarpkgconfigpurrrquadprogR6RColorBrewerRcppRcppArmadilloRcppEigenrlangrstpm2S7scalesstatmodstringistringrsurvivaltibbletidyrtidyselectutf8vctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
Construct Case II Interval Censoring Datacase_II_X
Construct Case I Interval Censoring Data (Current Status Data)current_status_X
Prepare Data for Interval-Censored Modeldata_prep
Compute Directional Derivatives for Active Set Algorithmfind_dir_deriv
Compute qsi Matrixfind_qsi
Generic Function to compute F at Xget_F_at_x
Evaluate F(x) for Objects of Class 'iclogcondist'get_F_at_x.iclogcondist
Compute Least Concave Majorant (LCM) of the log of the Unconstrained MLE for Interval-Censored Dataic_LCM_UMLE
Compute the log-concave MLE for Interval-censored Data using an Active Set Algorithmic_LCMLE
Compute Unconstrained Maximum Likelihood Estimate for Interval-Censored Dataic_UMLE
Visualize the Estimated Cumulative Distribution Functionsiclogcondist_visualization
Iterative Convex Minorant (ICM) Subset Algorithmicm_subset_cpp
Initial Values for Estimation under Log-concavity with Interval-Censored Datainitial_values
LGNM Data: Case II Interval Censoring Examplelgnm
Compute the Negative Log-Likelihood for the Interval-Censored Modelneg_log_like
Plot Method for iclogcondist_plot Objectsplot.iclogcondist
Cumulative Distribution Function of a Truncated Log-Logistic Distributionptllogis
Cumulative Distribution Function of a Truncated Log-Normal Distributionptlnorm
Cumulative Distribution Function of a Truncated Weibull Distributionptweibull
Quantile Function of a Truncated Log-Logistic Distributionqtllogis
Quantile Function of a Truncated Log-Normal Distributionqtlnorm
Quantile Function of a Truncated Weibull Distributionqtweibull
Simulate from a Truncated Log-Logistic Distributionrtllogis
Simulate from a Truncated Log-Normal Distributionrtlnorm
Simulate from a Truncated Weibull Distributionrtweibull
Simulate Interval-Censored Datasimulate_ic_data
Find Unique Rows in a Matrix and Their Weightsunique_X_weight