takeshiemura1.r-universe.dev

[takeshiemura1] Copula.surv 3.1

takeshiemura@gmail.com (Takeshi Emura) — Mon, 25 May 2026 04:57:41 GMT

Simulating bivariate survival data from various copula models. Estimating bivariate copula models with semiparametric or Weibull margins under various copulas. Two different ways to estimate the association parameter in copula models are implemented. A goodness-of-fit test for the Gumbel and Clayton copulas is also implemented for semiparametric models. See Emura, Lin and Wang (2010) for details.

[takeshiemura1] compound.Cox 3.33

takeshiemura@gmail.com (Takeshi Emura) — Mon, 16 Jun 2025 04:50:02 GMT

Univariate feature selection and compound covariate methods under the Cox model with high-dimensional features (e.g., gene expressions). Available are survival data for non-small-cell lung cancer patients with gene expressions (Chen et al 2007 New Engl J Med) , statistical methods in Emura et al (2012 PLoS ONE) , Emura & Chen (2016 Stat Methods Med Res) , and Emura et al (2019). Algorithms for generating correlated gene expressions are also available. Estimation of survival functions via copula-graphic (CG) estimators is also implemented, which is useful for sensitivity analyses under dependent censoring (Yeh et al 2023 Biomedicines) and factorial survival analyses (Emura et al 2024 Stat Methods Med Res) .

[takeshiemura1] double.truncation 1.8

takeshiemura@gmail.com (Takeshi Emura) — Thu, 05 Dec 2024 05:30:02 GMT

Likelihood-based inference methods with doubly-truncated data are developed under various models. Nonparametric models are based on Efron and Petrosian (1999) and Emura, Konno, and Michimae (2015) . Parametric models from the special exponential family (SEF) are based on Hu and Emura (2015) and Emura, Hu and Konno (2017) . The parametric location-scale models are based on Dorre et al. (2021) .

[takeshiemura1] g.ridge 1.0

takeshiemura@gmail.com (Takeshi Emura) — Fri, 08 Dec 2023 02:41:02 GMT

Ridge regression due to Hoerl and Kennard (1970) and generalized ridge regression due to Yang and Emura (2017) with optimized tuning parameters. These ridge regression estimators (the HK estimator and the YE estimator) are computed by minimizing the cross-validated mean squared errors. Both the ridge and generalized ridge estimators are applicable for high-dimensional regressors (p>n), where p is the number of regressors, and n is the sample size.

[takeshiemura1] joint.Cox 3.16

takeshiemura@gmail.com (Takeshi Emura) — Fri, 04 Feb 2022 09:10:10 GMT

Fit survival data and perform dynamic prediction under joint frailty-copula models for tumour progression and death. Likelihood-based methods are employed for estimating model parameters, where the baseline hazard functions are modeled by the cubic M-spline or the Weibull model. The methods are applicable for meta-analytic data containing individual-patient information from several studies. Survival outcomes need information on both terminal event time (e.g., time-to-death) and non-terminal event time (e.g., time-to-tumour progression). Methodologies were published in Emura et al. (2017) , Emura et al. (2018) , Emura et al. (2020) , Shinohara et al. (2020) , Wu et al. (2020) , and Emura et al. (2021) . See also the book of Emura et al. (2019) . Survival data from ovarian cancer patients are also available.

[takeshiemura1] Copula.Markov 2.9

takeshiemura@gmail.com (Takeshi Emura) — Mon, 29 Nov 2021 04:40:13 GMT

Estimation and statistical process control are performed under copula-based time-series models. Available are statistical methods in Long and Emura (2014 JCSA), Emura et al. (2017 Commun Stat-Simul) , Huang and Emura (2021 Commun Stat-Simul) , Lin et al. (2021 Comm Stat-Simul) , Sun et al. (2020 JSS Series in Statistics), and Huang and Emura (2021, in revision).

[takeshiemura1] uni.survival.tree 1.5

takeshiemura@gmail.com (Takeshi Emura) — Mon, 22 Mar 2021 05:40:02 GMT

A classification (decision) tree is constructed from survival data with high-dimensional covariates. The method is a robust version of the logrank tree, where the variance is stabilized. The main function "uni.tree" returns a classification tree for a given survival dataset. The inner nodes (splitting criterion) are selected by minimizing the P-value of the two-sample the score tests. The decision of declaring terminal nodes (stopping criterion) is the P-value threshold given by an argument (specified by user). This tree construction algorithm is proposed by Emura et al. (2021, in review).

[takeshiemura1] depend.truncation 3.0

takeshiemura@gmail.com (Takeshi Emura) — Tue, 27 Feb 2018 11:43:41 GMT

Estimation and testing methods for dependently truncated data. Semi-parametric methods are based on Emura et al. (2011), Emura & Wang (2012), and Emura & Murotani (2015). Parametric approaches are based on Emura & Konno (2012) and Emura & Pan (2017). A regression approach is based on Emura & Wang (2016). Quasi-independence tests are based on Emura & Wang (2010). Right-truncated data for Japanese male centenarians are given by Emura & Murotani (2015).