R-universe search: weibull

ousuga

RelDists:Estimation for some Reliability Distributions

Parameters estimation and linear regression models for Reliability distributions families reviewed by Almalki & Nadarajah (2014) <doi:10.1016/j.ress.2013.11.010> using Generalized Additive Models for Location, Scale and Shape, aka GAMLSS by Rigby & Stasinopoulos (2005) <doi:10.1111/j.1467-9876.2005.00510.x>.

Maintained by Jaime Mosquera. Last updated 8 days ago.

84.9 match 3 stars 5.76 score 19 scripts

appliedstat

weibullness:Goodness-of-Fit Test for Weibull Distribution (Weibullness)

Conducts a goodness-of-fit test for the Weibull distribution (referred to as the weibullness test) and furnishes parameter estimations for both the two-parameter and three-parameter Weibull distributions. Notably, the threshold parameter is derived through correlation from the Weibull plot. Additionally, this package conducts goodness-of-fit assessments for the exponential, Gumbel, and inverse Weibull distributions, accompanied by parameter estimations. For more details, see Park (2017) <doi:10.23055/ijietap.2017.24.4.2848>, Park (2018) <doi:10.1155/2018/6056975>, and Park (2023) <doi:10.3390/math11143156>. This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korea government (MSIT) (No. 2022R1A2C1091319, RS-2023-00242528).

Maintained by Chanseok Park. Last updated 1 years ago.

control-chart goodness-of-fit r-language weibull

114.6 match 2 stars 3.98 score 32 scripts 1 dependents

alexpghayes

distributions3:Probability Distributions as S3 Objects

Tools to create and manipulate probability distributions using S3. Generics pdf(), cdf(), quantile(), and random() provide replacements for base R's d/p/q/r style functions. Functions and arguments have been named carefully to minimize confusion for students in intro stats courses. The documentation for each distribution contains detailed mathematical notes.

Maintained by Alex Hayes. Last updated 6 months ago.

15.9 match 101 stars 11.31 score 118 scripts 7 dependents

zrmacc

Temporal:Parametric Time to Event Analysis

Performs maximum likelihood based estimation and inference on time to event data, possibly subject to non-informative right censoring. FitParaSurv() provides maximum likelihood estimates of model parameters and distributional characteristics, including the mean, median, variance, and restricted mean. CompParaSurv() compares the mean, median, and restricted mean survival experiences of two treatment groups. Candidate distributions include the exponential, gamma, generalized gamma, log-normal, and Weibull.

Maintained by Zachary McCaw. Last updated 1 years ago.

18.9 match 3 stars 5.96 score 34 scripts 1 dependents

wasquith

lmomco:L-Moments, Censored L-Moments, Trimmed L-Moments, L-Comoments, and Many Distributions

Extensive functions for Lmoments (LMs) and probability-weighted moments (PWMs), distribution parameter estimation, LMs for distributions, LM ratio diagrams, multivariate Lcomoments, and asymmetric (asy) trimmed LMs (TLMs). Maximum likelihood and maximum product spacings estimation are available. Right-tail and left-tail LM censoring by threshold or indicator variable are available. LMs of residual (resid) and reversed (rev) residual life are implemented along with 13 quantile operators for reliability analyses. Exact analytical bootstrap estimates of order statistics, LMs, and LM var-covars are available. Harri-Coble Tau34-squared Normality Test is available. Distributions with L, TL, and added (+) support for right-tail censoring (RC) encompass: Asy Exponential (Exp) Power [L], Asy Triangular [L], Cauchy [TL], Eta-Mu [L], Exp. [L], Gamma [L], Generalized (Gen) Exp Poisson [L], Gen Extreme Value [L], Gen Lambda [L, TL], Gen Logistic [L], Gen Normal [L], Gen Pareto [L+RC, TL], Govindarajulu [L], Gumbel [L], Kappa [L], Kappa-Mu [L], Kumaraswamy [L], Laplace [L], Linear Mean Residual Quantile Function [L], Normal [L], 3p log-Normal [L], Pearson Type III [L], Polynomial Density-Quantile 3 and 4 [L], Rayleigh [L], Rev-Gumbel [L+RC], Rice [L], Singh Maddala [L], Slash [TL], 3p Student t [L], Truncated Exponential [L], Wakeby [L], and Weibull [L].

Maintained by William Asquith. Last updated 1 months ago.

flood-frequency-analysis l-moments mle-estimation mps-estimation probability-distribution rainfall-frequency-analysis reliability-analysis risk-analysis survival-analysis

13.3 match 2 stars 8.06 score 458 scripts 38 dependents

tim-tu

weibulltools:Statistical Methods for Life Data Analysis

Provides statistical methods and visualizations that are often used in reliability engineering. Comprises a compact and easily accessible set of methods and visualization tools that make the examination and adjustment as well as the analysis and interpretation of field data (and bench tests) as simple as possible. Non-parametric estimators like Median Ranks, Kaplan-Meier (Abernethy, 2006, <ISBN:978-0-9653062-3-2>), Johnson (Johnson, 1964, <ISBN:978-0444403223>), and Nelson-Aalen for failure probability estimation within samples that contain failures as well as censored data are included. The package supports methods like Maximum Likelihood and Rank Regression, (Genschel and Meeker, 2010, <DOI:10.1080/08982112.2010.503447>) for the estimation of multiple parametric lifetime distributions, as well as the computation of confidence intervals of quantiles and probabilities using the delta method related to Fisher's confidence intervals (Meeker and Escobar, 1998, <ISBN:9780471673279>) and the beta-binomial confidence bounds. If desired, mixture model analysis can be done with segmented regression and the EM algorithm. Besides the well-known Weibull analysis, the package also contains Monte Carlo methods for the correction and completion of imprecisely recorded or unknown lifetime characteristics. (Verband der Automobilindustrie e.V. (VDA), 2016, <ISSN:0943-9412>). Plots are created statically ('ggplot2') or interactively ('plotly') and can be customized with functions of the respective visualization package. The graphical technique of probability plotting as well as the addition of regression lines and confidence bounds to existing plots are supported.

Maintained by Tim-Gunnar Hensel. Last updated 2 years ago.

field-data-analysis interactive-visualizations plotly reliability-analysis weibull-analysis weibulltools openblas cpp

16.7 match 13 stars 6.15 score 54 scripts

martinbladt

matrixdist:Statistics for Matrix Distributions

Tools for phase-type distributions including the following variants: continuous, discrete, multivariate, in-homogeneous, right-censored, and regression. Methods for functional evaluation, simulation and estimation using the expectation-maximization (EM) algorithm are provided for all models. The methods of this package are based on the following references. Asmussen, S., Nerman, O., & Olsson, M. (1996). Fitting phase-type distributions via the EM algorithm, Olsson, M. (1996). Estimation of phase-type distributions from censored data, Albrecher, H., & Bladt, M. (2019) <doi:10.1017/jpr.2019.60>, Albrecher, H., Bladt, M., & Yslas, J. (2022) <doi:10.1111/sjos.12505>, Albrecher, H., Bladt, M., Bladt, M., & Yslas, J. (2022) <doi:10.1016/j.insmatheco.2022.08.001>, Bladt, M., & Yslas, J. (2022) <doi:10.1080/03461238.2022.2097019>, Bladt, M. (2022) <doi:10.1017/asb.2021.40>, Bladt, M. (2023) <doi:10.1080/10920277.2023.2167833>, Albrecher, H., Bladt, M., & Mueller, A. (2023) <doi:10.1515/demo-2022-0153>, Bladt, M. & Yslas, J. (2023) <doi:10.1016/j.insmatheco.2023.02.008>.

Maintained by Martin Bladt. Last updated 17 days ago.

openblas cpp openmp

24.7 match 1 stars 4.00 score 2 scripts

goranbrostrom

eha:Event History Analysis

Parametric proportional hazards fitting with left truncation and right censoring for common families of distributions, piecewise constant hazards, and discrete models. Parametric accelerated failure time models for left truncated and right censored data. Proportional hazards models for tabular and register data. Sampling of risk sets in Cox regression, selections in the Lexis diagram, bootstrapping. Broström (2022) <doi:10.1201/9780429503764>.

Maintained by Göran Broström. Last updated 9 months ago.

fortran openblas

10.1 match 7 stars 9.76 score 308 scripts 10 dependents

spsanderson

TidyDensity:Functions for Tidy Analysis and Generation of Random Data

To make it easy to generate random numbers based upon the underlying stats distribution functions. All data is returned in a tidy and structured format making working with the data simple and straight forward. Given that the data is returned in a tidy 'tibble' it lends itself to working with the rest of the 'tidyverse'.

Maintained by Steven Sanderson. Last updated 5 months ago.

bootstrap density distributions ggplot2 probability r-language simulation statistics tibble tidy

10.7 match 34 stars 7.78 score 66 scripts 1 dependents

stanislashubeaux

SurvRegCensCov:Weibull Regression for a Right-Censored Endpoint with Interval-Censored Covariate

The function SurvRegCens() of this package allows estimation of a Weibull Regression for a right-censored endpoint, one interval-censored covariate, and an arbitrary number of non-censored covariates. Additional functions allow to switch between different parametrizations of Weibull regression used by different R functions, inference for the mean difference of two arbitrarily censored Normal samples, and estimation of canonical parameters from censored samples for several distributional assumptions. Hubeaux, S. and Rufibach, K. (2014) <arXiv:1402.0432>.

Maintained by Stanislas Hubeaux. Last updated 1 years ago.

26.0 match 1 stars 3.18 score 50 scripts

tyee001

VGAM:Vector Generalized Linear and Additive Models

An implementation of about 6 major classes of statistical regression models. The central algorithm is Fisher scoring and iterative reweighted least squares. At the heart of this package are the vector generalized linear and additive model (VGLM/VGAM) classes. VGLMs can be loosely thought of as multivariate GLMs. VGAMs are data-driven VGLMs that use smoothing. The book "Vector Generalized Linear and Additive Models: With an Implementation in R" (Yee, 2015) <DOI:10.1007/978-1-4939-2818-7> gives details of the statistical framework and the package. Currently only fixed-effects models are implemented. Many (100+) models and distributions are estimated by maximum likelihood estimation (MLE) or penalized MLE. The other classes are RR-VGLMs (reduced-rank VGLMs), quadratic RR-VGLMs, doubly constrained RR-VGLMs, quadratic RR-VGLMs, reduced-rank VGAMs, RCIMs (row-column interaction models)---these classes perform constrained and unconstrained quadratic ordination (CQO/UQO) models in ecology, as well as constrained additive ordination (CAO). Hauck-Donner effect detection is implemented. Note that these functions are subject to change; see the NEWS and ChangeLog files for latest changes.

Maintained by Thomas Yee. Last updated 1 months ago.

fortran

7.7 match 10 stars 10.67 score 3.6k scripts 169 dependents

jto888

WeibullR:Weibull Analysis for Reliability Engineering

Life data analysis in the graphical tradition of Waloddi Weibull. Methods derived from Robert B. Abernethy (2008, ISBN 0-965306-3-2), Wayne Nelson (1982, ISBN: 9780471094586), William Q. Meeker and Lois A. Escobar (1998, ISBN: 1-471-14328-6), John I. McCool, (2012, ISBN: 9781118217986).

Maintained by Jacob Ormerod. Last updated 3 years ago.

openblas cpp

23.8 match 3.42 score 44 scripts 4 dependents

dsy109

mixtools:Tools for Analyzing Finite Mixture Models

Analyzes finite mixture models for various parametric and semiparametric settings. This includes mixtures of parametric distributions (normal, multivariate normal, multinomial, gamma), various Reliability Mixture Models (RMMs), mixtures-of-regressions settings (linear regression, logistic regression, Poisson regression, linear regression with changepoints, predictor-dependent mixing proportions, random effects regressions, hierarchical mixtures-of-experts), and tools for selecting the number of components (bootstrapping the likelihood ratio test statistic, mixturegrams, and model selection criteria). Bayesian estimation of mixtures-of-linear-regressions models is available as well as a novel data depth method for obtaining credible bands. This package is based upon work supported by the National Science Foundation under Grant No. SES-0518772 and the Chan Zuckerberg Initiative: Essential Open Source Software for Science (Grant No. 2020-255193).

Maintained by Derek Young. Last updated 9 months ago.

mixture-models mixture-of-experts semiparametric-regression

6.9 match 20 stars 11.34 score 1.4k scripts 56 dependents

rfastofficial

Rfast2:A Collection of Efficient and Extremely Fast R Functions II

A collection of fast statistical and utility functions for data analysis. Functions for regression, maximum likelihood, column-wise statistics and many more have been included. C++ has been utilized to speed up the functions. References: Tsagris M., Papadakis M. (2018). Taking R to its limits: 70+ tips. PeerJ Preprints 6:e26605v1 <doi:10.7287/peerj.preprints.26605v1>.

Maintained by Manos Papadakis. Last updated 1 years ago.

openblas cpp openmp

9.6 match 38 stars 8.09 score 75 scripts 26 dependents

cran

drc:Analysis of Dose-Response Curves

Analysis of dose-response data is made available through a suite of flexible and versatile model fitting and after-fitting functions.

Maintained by Christian Ritz. Last updated 9 years ago.

7.6 match 8 stars 8.39 score 1.4k scripts 28 dependents

lbelzile

VaRES:Computes Value at Risk and Expected Shortfall for over 100 Parametric Distributions

Computes Value at risk and expected shortfall, two most popular measures of financial risk, for over one hundred parametric distributions, including all commonly known distributions. Also computed are the corresponding probability density function and cumulative distribution function. See Chan, Nadarajah and Afuecheta (2015) <doi:10.1080/03610918.2014.944658> for more details.

Maintained by Leo Belzile. Last updated 2 years ago.

14.0 match 1 stars 4.57 score 123 scripts 2 dependents

mkrit

EWGoF:Goodness-of-Fit Tests for the Exponential and Two-Parameter Weibull Distributions

Contains a large number of the goodness-of-fit tests for the Exponential and Weibull distributions classified into families: the tests based on the empirical distribution function, the tests based on the probability plot, the tests based on the normalized spacings, the tests based on the Laplace transform and the likelihood based tests.

Maintained by Meryam Krit. Last updated 6 years ago.

cpp

21.6 match 2.85 score 14 scripts

gamlss-dev

gamlss.dist:Distributions for Generalized Additive Models for Location Scale and Shape

A set of distributions which can be used for modelling the response variables in Generalized Additive Models for Location Scale and Shape, Rigby and Stasinopoulos (2005), <doi:10.1111/j.1467-9876.2005.00510.x>. The distributions can be continuous, discrete or mixed distributions. Extra distributions can be created, by transforming, any continuous distribution defined on the real line, to a distribution defined on ranges 0 to infinity or 0 to 1, by using a 'log' or a 'logit' transformation respectively.

Maintained by Mikis Stasinopoulos. Last updated 21 days ago.

5.4 match 4 stars 10.50 score 346 scripts 71 dependents

treynkens

ReIns:Functions from "Reinsurance: Actuarial and Statistical Aspects"

Functions from the book "Reinsurance: Actuarial and Statistical Aspects" (2017) by Hansjoerg Albrecher, Jan Beirlant and Jef Teugels <https://www.wiley.com/en-us/Reinsurance%3A+Actuarial+and+Statistical+Aspects-p-9780470772683>.

Maintained by Tom Reynkens. Last updated 4 months ago.

extremes reinsurance risk-analysis cpp

8.9 match 22 stars 6.31 score 186 scripts

epinowcast

primarycensored:Primary Event Censored Distributions

Provides functions for working with primary event censored distributions and 'Stan' implementations for use in Bayesian modeling. Primary event censored distributions are useful for modeling delayed reporting scenarios in epidemiology and other fields (Charniga et al. (2024) <doi:10.48550/arXiv.2405.08841>). It also provides support for arbitrary delay distributions, a range of common primary distributions, and allows for truncation and secondary event censoring to be accounted for (Park et al. (2024) <doi:10.1101/2024.01.12.24301247>). A subset of common distributions also have analytical solutions implemented, allowing for faster computation. In addition, it provides multiple methods for fitting primary event censored distributions to data via optional dependencies.

Maintained by Sam Abbott. Last updated 1 months ago.

censoring distributions mc-stan truncation

7.0 match 8 stars 7.69 score 16 scripts 1 dependents

rfastofficial

Rfast:A Collection of Efficient and Extremely Fast R Functions

A collection of fast (utility) functions for data analysis. Column and row wise means, medians, variances, minimums, maximums, many t, F and G-square tests, many regressions (normal, logistic, Poisson), are some of the many fast functions. References: a) Tsagris M., Papadakis M. (2018). Taking R to its limits: 70+ tips. PeerJ Preprints 6:e26605v1 <doi:10.7287/peerj.preprints.26605v1>. b) Tsagris M. and Papadakis M. (2018). Forward regression in R: from the extreme slow to the extreme fast. Journal of Data Science, 16(4): 771--780. <doi:10.6339/JDS.201810_16(4).00006>. c) Chatzipantsiou C., Dimitriadis M., Papadakis M. and Tsagris M. (2020). Extremely Efficient Permutation and Bootstrap Hypothesis Tests Using Hypothesis Tests Using R. Journal of Modern Applied Statistical Methods, 18(2), eP2898. <doi:10.48550/arXiv.1806.10947>. d) Tsagris M., Papadakis M., Alenazi A. and Alzeley O. (2024). Computationally Efficient Outlier Detection for High-Dimensional Data Using the MDP Algorithm. Computation, 12(9): 185. <doi:10.3390/computation12090185>. e) Tsagris M. and Papadakis M. (2025). Fast and light-weight energy statistics using the R package Rfast. <doi:10.48550/arXiv.2501.02849>.

Maintained by Manos Papadakis. Last updated 17 days ago.

openblas cpp openmp

4.3 match 147 stars 12.54 score 1.2k scripts 166 dependents

paul-buerkner

brms:Bayesian Regression Models using 'Stan'

Fit Bayesian generalized (non-)linear multivariate multilevel models using 'Stan' for full Bayesian inference. A wide range of distributions and link functions are supported, allowing users to fit -- among others -- linear, robust linear, count data, survival, response times, ordinal, zero-inflated, hurdle, and even self-defined mixture models all in a multilevel context. Further modeling options include both theory-driven and data-driven non-linear terms, auto-correlation structures, censoring and truncation, meta-analytic standard errors, and quite a few more. In addition, all parameters of the response distribution can be predicted in order to perform distributional regression. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their prior knowledge. Models can easily be evaluated and compared using several methods assessing posterior or prior predictions. References: Bürkner (2017) <doi:10.18637/jss.v080.i01>; Bürkner (2018) <doi:10.32614/RJ-2018-017>; Bürkner (2021) <doi:10.18637/jss.v100.i05>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.

Maintained by Paul-Christian Bürkner. Last updated 2 days ago.

bayesian-inference brms multilevel-models stan statistical-models

3.0 match 1.3k stars 16.61 score 13k scripts 34 dependents

oucru-modelling

serosv:Model Infectious Disease Parameters from Serosurveys

An easy-to-use and efficient tool to estimate infectious diseases parameters using serological data. Implemented models include SIR models (basic_sir_model(), static_sir_model(), mseir_model(), sir_subpops_model()), parametric models (polynomial_model(), fp_model()), nonparametric models (lp_model()), semiparametric models (penalized_splines_model()), hierarchical models (hierarchical_bayesian_model()). The package is based on the book "Modeling Infectious Disease Parameters Based on Serological and Social Contact Data: A Modern Statistical Perspective" (Hens, Niel & Shkedy, Ziv & Aerts, Marc & Faes, Christel & Damme, Pierre & Beutels, Philippe., 2013) <doi:10.1007/978-1-4614-4072-7>.

Maintained by Anh Phan Truong Quynh. Last updated 1 months ago.

cpp

7.4 match 6.58 score 24 scripts

paulgovan

WeibullR.plotly:Interactive Weibull Probability Plots

Build interactive Weibull Probability Plots with 'WeibullR' by David Silkworth and Jurgen Symynck (2022) <https://CRAN.R-project.org/package=WeibullR>, an R package for Weibull analysis, and 'plotly' by Carson Sievert (2020) <https://plotly-r.com>, an interactive web-based graphing library.

Maintained by Paul Govan. Last updated 4 months ago.

life-data-analysis plotly weibull-analysis

11.4 match 4.26 score 12 scripts 1 dependents

alexkowa

EnvStats:Package for Environmental Statistics, Including US EPA Guidance

Graphical and statistical analyses of environmental data, with focus on analyzing chemical concentrations and physical parameters, usually in the context of mandated environmental monitoring. Major environmental statistical methods found in the literature and regulatory guidance documents, with extensive help that explains what these methods do, how to use them, and where to find them in the literature. Numerous built-in data sets from regulatory guidance documents and environmental statistics literature. Includes scripts reproducing analyses presented in the book "EnvStats: An R Package for Environmental Statistics" (Millard, 2013, Springer, ISBN 978-1-4614-8455-4, <doi:10.1007/978-1-4614-8456-1>).

Maintained by Alexander Kowarik. Last updated 16 days ago.

3.8 match 26 stars 12.80 score 2.4k scripts 46 dependents

albgarre

bioinactivation:Mathematical Modelling of (Dynamic) Microbial Inactivation

Functions for modelling microbial inactivation under isothermal or dynamic conditions. The calculations are based on several mathematical models broadly used by the scientific community and industry. Functions enable to make predictions for cases where the kinetic parameters are known. It also implements functions for parameter estimation for isothermal and dynamic conditions. The model fitting capabilities include an Adaptive Monte Carlo method for a Bayesian approach to parameter estimation.

Maintained by Alberto Garre. Last updated 2 years ago.

food food-safety inactivation-models isothermal-experiments prediction

12.1 match 3.95 score 18 scripts

insightsengineering

simIDM:Simulating Oncology Trials using an Illness-Death Model

Based on the illness-death model a large number of clinical trials with oncology endpoints progression-free survival (PFS) and overall survival (OS) can be simulated, see Meller, Beyersmann and Rufibach (2019) <doi:10.1002/sim.8295>. The simulation set-up allows for random and event-driven censoring, an arbitrary number of treatment arms, staggered study entry and drop-out. Exponentially, Weibull and piecewise exponentially distributed survival times can be generated. The correlation between PFS and OS can be calculated.

Maintained by Alexandra Erdmann. Last updated 1 years ago.

multistate-models simulation-engine

7.6 match 13 stars 6.26 score 9 scripts

stan-dev

rstanarm:Bayesian Applied Regression Modeling via Stan

Estimates previously compiled regression models using the 'rstan' package, which provides the R interface to the Stan C++ library for Bayesian estimation. Users specify models via the customary R syntax with a formula and data.frame plus some additional arguments for priors.

Maintained by Ben Goodrich. Last updated 9 months ago.

bayesian bayesian-data-analysis bayesian-inference bayesian-methods bayesian-statistics multilevel-models rstan rstanarm stan statistical-modeling cpp

3.0 match 393 stars 15.68 score 5.0k scripts 13 dependents

appliedstat

rQCC:Robust Quality Control Chart

Constructs various robust quality control charts based on the median or Hodges-Lehmann estimator (location) and the median absolute deviation (MAD) or Shamos estimator (scale). The estimators used for the robust control charts are all unbiased with a sample of finite size. For more details, see Park, Kim and Wang (2022) <doi:10.1080/03610918.2019.1699114>. In addition, using this R package, the conventional quality control charts such as X-bar, S, R, p, np, u, c, g, h, and t charts are also easily constructed. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2022R1A2C1091319).

Maintained by Chanseok Park. Last updated 1 years ago.

control-chart goodness-of-fit r-language weibull

10.0 match 2 stars 4.70 score 3 scripts

r-forge

distr:Object Oriented Implementation of Distributions

S4-classes and methods for distributions.

Maintained by Peter Ruckdeschel. Last updated 2 months ago.

5.3 match 8.84 score 327 scripts 32 dependents

paulgovan

WeibullR.shiny:A 'Shiny' App for Weibull Analysis

A 'Shiny' web application for life data analysis that depends on 'WeibullR' by David Silkworth and Jurgen Symynck (2022) <https://CRAN.R-project.org/package=WeibullR>, an R package for Weibull analysis.

Maintained by Paul Govan. Last updated 4 months ago.

life-data-analysis shiny weibull-analysis

13.0 match 3.54 score

oobianom

quickcode:Quick and Essential 'R' Tricks for Better Scripts

The NOT functions, 'R' tricks and a compilation of some simple quick plus often used 'R' codes to improve your scripts. Improve the quality and reproducibility of 'R' scripts.

Maintained by Obinna Obianom. Last updated 13 days ago.

colors data distributions images

5.6 match 5 stars 7.76 score 7 scripts 6 dependents

chjackson

flexsurv:Flexible Parametric Survival and Multi-State Models

Flexible parametric models for time-to-event data, including the Royston-Parmar spline model, generalized gamma and generalized F distributions. Any user-defined parametric distribution can be fitted, given at least an R function defining the probability density or hazard. There are also tools for fitting and predicting from fully parametric multi-state models, based on either cause-specific hazards or mixture models.

Maintained by Christopher Jackson. Last updated 2 months ago.

cpp

3.1 match 57 stars 13.31 score 632 scripts 43 dependents

cran

joint.Cox:Joint Frailty-Copula Models for Tumour Progression and Death in Meta-Analysis

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) <doi:10.1177/0962280215604510>, Emura et al. (2018) <doi:10.1177/0962280216688032>, Emura et al. (2020) <doi:10.1177/0962280219892295>, Shinohara et al. (2020) <doi:10.1080/03610918.2020.1855449>, Wu et al. (2020) <doi:10.1007/s00180-020-00977-1>, and Emura et al. (2021) <doi:10.1177/09622802211046390>. See also the book of Emura et al. (2019) <doi:10.1007/978-981-13-3516-7>. Survival data from ovarian cancer patients are also available.

Maintained by Takeshi Emura. Last updated 3 years ago.

15.4 match 5 stars 2.71 score 17 scripts 2 dependents

nicokubi

penetrance:Methods for Penetrance Estimation in Family-Based Studies

Implements statistical methods for estimating disease penetrance in family-based studies. Penetrance refers to the probability of disease§ manifestation in individuals carrying specific genetic variants. The package provides tools for age-specific penetrance estimation, handling missing data, and accounting for ascertainment bias in family studies. Cite as: Kubista, N., Braun, D. & Parmigiani, G. (2024) <doi:10.48550/arXiv.2411.18816>.

Maintained by Nicolas Kubista. Last updated 16 days ago.

7.5 match 5.41 score

nlmixr2

rxode2:Facilities for Simulating from ODE-Based Models

Facilities for running simulations from ordinary differential equation ('ODE') models, such as pharmacometrics and other compartmental models. A compilation manager translates the ODE model into C, compiles it, and dynamically loads the object code into R for improved computational efficiency. An event table object facilitates the specification of complex dosing regimens (optional) and sampling schedules. NB: The use of this package requires both C and Fortran compilers, for details on their use with R please see Section 6.3, Appendix A, and Appendix D in the "R Administration and Installation" manual. Also the code is mostly released under GPL. The 'VODE' and 'LSODA' are in the public domain. The information is available in the inst/COPYRIGHTS.

Maintained by Matthew L. Fidler. Last updated 29 days ago.

fortran openblas cpp openmp

3.6 match 39 stars 11.16 score 220 scripts 13 dependents

jamescbell

gestate:Generalised Survival Trial Assessment Tool Environment

Provides tools to assist planning and monitoring of time-to-event trials under complicated censoring assumptions and/or non-proportional hazards. There are three main components: The first is analytic calculation of predicted time-to-event trial properties, providing estimates of expected hazard ratio, event numbers and power under different analysis methods. The second is simulation, allowing stochastic estimation of these same properties. Thirdly, it provides parametric event prediction using blinded trial data, including creation of prediction intervals. Methods are based upon numerical integration and a flexible object-orientated structure for defining event, censoring and recruitment distributions (Curves).

Maintained by James Bell. Last updated 2 years ago.

14.9 match 2 stars 2.60 score 8 scripts

lbbe-software

fitdistrplus:Help to Fit of a Parametric Distribution to Non-Censored or Censored Data

Extends the fitdistr() function (of the MASS package) with several functions to help the fit of a parametric distribution to non-censored or censored data. Censored data may contain left censored, right censored and interval censored values, with several lower and upper bounds. In addition to maximum likelihood estimation (MLE), the package provides moment matching (MME), quantile matching (QME), maximum goodness-of-fit estimation (MGE) and maximum spacing estimation (MSE) methods (available only for non-censored data). Weighted versions of MLE, MME, QME and MSE are available. See e.g. Casella & Berger (2002), Statistical inference, Pacific Grove, for a general introduction to parametric estimation.

Maintained by Aurélie Siberchicot. Last updated 12 days ago.

2.4 match 54 stars 16.15 score 4.5k scripts 153 dependents

boost-r

mboost:Model-Based Boosting

Functional gradient descent algorithm (boosting) for optimizing general risk functions utilizing component-wise (penalised) least squares estimates or regression trees as base-learners for fitting generalized linear, additive and interaction models to potentially high-dimensional data. Models and algorithms are described in <doi:10.1214/07-STS242>, a hands-on tutorial is available from <doi:10.1007/s00180-012-0382-5>. The package allows user-specified loss functions and base-learners.

Maintained by Torsten Hothorn. Last updated 4 months ago.

boosting-algorithms gam glm machine-learning mboost modelling r-language tutorials variable-selection openblas

3.0 match 72 stars 12.70 score 540 scripts 27 dependents

greta-dev

greta:Simple and Scalable Statistical Modelling in R

Write statistical models in R and fit them by MCMC and optimisation on CPUs and GPUs, using Google 'TensorFlow'. greta lets you write your own model like in BUGS, JAGS and Stan, except that you write models right in R, it scales well to massive datasets, and it’s easy to extend and build on. See the website for more information, including tutorials, examples, package documentation, and the greta forum.

Maintained by Nicholas Tierney. Last updated 5 days ago.

3.0 match 566 stars 12.53 score 396 scripts 6 dependents

paulgovan

ReliaGrowR:Reliability Growth Analysis

Modeling and plotting functions for Reliability Growth Analysis (RGA). Models include the Duane (1962) <doi:10.1109/TA.1964.4319640>, Non-Homogeneous Poisson Process (NHPP) by Crow (1975) <https://apps.dtic.mil/sti/citations/ADA020296>, Piecewise Weibull NHPP by Guo et al. (2010) <doi:10.1109/RAMS.2010.5448029>, and Piecewise Weibull NHPP with Change Point Detection based on the 'segmented' package by Muggeo (2024) <https://cran.r-project.org/package=segmented>.

Maintained by Paul Govan. Last updated 4 months ago.

change-point-detection crow-amsaa nhpp reliability reliability-growth-testing

6.4 match 1 stars 5.64 score 12 scripts 3 dependents

genentech

psborrow2:Bayesian Dynamic Borrowing Analysis and Simulation

Bayesian dynamic borrowing is an approach to incorporating external data to supplement a randomized, controlled trial analysis in which external data are incorporated in a dynamic way (e.g., based on similarity of outcomes); see Viele 2013 <doi:10.1002/pst.1589> for an overview. This package implements the hierarchical commensurate prior approach to dynamic borrowing as described in Hobbes 2011 <doi:10.1111/j.1541-0420.2011.01564.x>. There are three main functionalities. First, 'psborrow2' provides a user-friendly interface for applying dynamic borrowing on the study results handles the Markov Chain Monte Carlo sampling on behalf of the user. Second, 'psborrow2' provides a simulation framework to compare different borrowing parameters (e.g. full borrowing, no borrowing, dynamic borrowing) and other trial and borrowing characteristics (e.g. sample size, covariates) in a unified way. Third, 'psborrow2' provides a set of functions to generate data for simulation studies, and also allows the user to specify their own data generation process. This package is designed to use the sampling functions from 'cmdstanr' which can be installed from <https://stan-dev.r-universe.dev>.

Maintained by Matt Secrest. Last updated 1 months ago.

bayesian-dynamic-borrowing psborrow2 simulation-study

4.6 match 18 stars 7.87 score 16 scripts

vigou3

actuar:Actuarial Functions and Heavy Tailed Distributions

Functions and data sets for actuarial science: modeling of loss distributions; risk theory and ruin theory; simulation of compound models, discrete mixtures and compound hierarchical models; credibility theory. Support for many additional probability distributions to model insurance loss size and frequency: 23 continuous heavy tailed distributions; the Poisson-inverse Gaussian discrete distribution; zero-truncated and zero-modified extensions of the standard discrete distributions. Support for phase-type distributions commonly used to compute ruin probabilities. Main reference: <doi:10.18637/jss.v025.i07>. Implementation of the Feller-Pareto family of distributions: <doi:10.18637/jss.v103.i06>.

Maintained by Vincent Goulet. Last updated 2 months ago.

openblas

3.8 match 12 stars 9.44 score 732 scripts 35 dependents

raphaelhartmann

ream:Density, Distribution, and Sampling Functions for Evidence Accumulation Models

Calculate the probability density functions (PDFs) for two threshold evidence accumulation models (EAMs). These are defined using the following Stochastic Differential Equation (SDE), dx(t) = v(x(t),t)*dt+D(x(t),t)*dW, where x(t) is the accumulated evidence at time t, v(x(t),t) is the drift rate, D(x(t),t) is the noise scale, and W is the standard Wiener process. The boundary conditions of this process are the upper and lower decision thresholds, represented by b_u(t) and b_l(t), respectively. Upper threshold b_u(t) > 0, while lower threshold b_l(t) < 0. The initial condition of this process x(0) = z where b_l(t) < z < b_u(t). We represent this as the relative start point w = z/(b_u(0)-b_l(0)), defined as a ratio of the initial threshold location. This package generates the PDF using the same approach as the 'python' package it is based upon, 'PyBEAM' by Murrow and Holmes (2023) <doi:10.3758/s13428-023-02162-w>. First, it converts the SDE model into the forwards Fokker-Planck equation dp(x,t)/dt = d(v(x,t)*p(x,t))/dt-0.5*d^2(D(x,t)^2*p(x,t))/dx^2, then solves this equation using the Crank-Nicolson method to determine p(x,t). Finally, it calculates the flux at the decision thresholds, f_i(t) = 0.5*d(D(x,t)^2*p(x,t))/dx evaluated at x = b_i(t), where i is the relevant decision threshold, either upper (i = u) or lower (i = l). The flux at each thresholds f_i(t) is the PDF for each threshold, specifically its PDF. We discuss further details of this approach in this package and 'PyBEAM' publications. Additionally, one can calculate the cumulative distribution functions of and sampling from the EAMs.

Maintained by Raphael Hartmann. Last updated 2 months ago.

cpp

7.1 match 2 stars 5.04 score 2 scripts

garybaylor

mixR:Finite Mixture Modeling for Raw and Binned Data

Performs maximum likelihood estimation for finite mixture models for families including Normal, Weibull, Gamma and Lognormal by using EM algorithm, together with Newton-Raphson algorithm or bisection method when necessary. It also conducts mixture model selection by using information criteria or bootstrap likelihood ratio test. The data used for mixture model fitting can be raw data or binned data. The model fitting process is accelerated by using R package 'Rcpp'.

Maintained by Youjiao Yu. Last updated 5 months ago.

cpp

6.0 match 8 stars 5.66 score 95 scripts 1 dependents

drodriguezperez

growthmodels:Nonlinear Growth Models

A compilation of nonlinear growth models.

Maintained by Daniel Rodriguez. Last updated 7 years ago.

7.3 match 21 stars 4.60 score 38 scripts

andrisignorell

DescTools:Tools for Descriptive Statistics

A collection of miscellaneous basic statistic functions and convenience wrappers for efficiently describing data. The author's intention was to create a toolbox, which facilitates the (notoriously time consuming) first descriptive tasks in data analysis, consisting of calculating descriptive statistics, drawing graphical summaries and reporting the results. The package contains furthermore functions to produce documents using MS Word (or PowerPoint) and functions to import data from Excel. Many of the included functions can be found scattered in other packages and other sources written partly by Titans of R. The reason for collecting them here, was primarily to have them consolidated in ONE instead of dozens of packages (which themselves might depend on other packages which are not needed at all), and to provide a common and consistent interface as far as function and arguments naming, NA handling, recycling rules etc. are concerned. Google style guides were used as naming rules (in absence of convincing alternatives). The 'BigCamelCase' style was consequently applied to functions borrowed from contributed R packages as well.

Maintained by Andri Signorell. Last updated 9 days ago.

fortran cpp

2.0 match 87 stars 16.68 score 7.7k scripts 99 dependents

haraldschellander

mevr:Fitting the Metastatistical Extreme Value Distribution MEVD

Extreme value analysis with the metastatistical extreme value distribution MEVD (Marani and Ignaccolo, 2015, <doi:10.1016/j.advwatres.2015.03.001>) and some of its variants. In particular, analysis can be performed with the simplified metastatistical extreme value distribution SMEV (Marra et al., 2019, <doi:10.1016/j.advwatres.2019.04.002>) and the temporal metastatistical extreme value distribution TMEV (Falkensteiner et al., 2023, <doi:10.1016/j.wace.2023.100601>). Parameters can be estimated with probability weighted moments, maximum likelihood and least squares. The data can also be left-censored prior to a fit. Density, distribution function, quantile function and random generation for the MEVD, SMEV and TMEV are included. In addition, functions for the calculation of return levels including confidence intervals are provided. For a description of use cases please see the provided references.

Maintained by Harald Schellander. Last updated 3 months ago.

extreme-value-statistics

8.1 match 2 stars 4.11 score 1 scripts

sambrilleman

simsurv:Simulate Survival Data

Simulate survival times from standard parametric survival distributions (exponential, Weibull, Gompertz), 2-component mixture distributions, or a user-defined hazard, log hazard, cumulative hazard, or log cumulative hazard function. Baseline covariates can be included under a proportional hazards assumption. Time dependent effects (i.e. non-proportional hazards) can be included by interacting covariates with linear time or a user-defined function of time. Clustered event times are also accommodated. The 2-component mixture distributions can allow for a variety of flexible baseline hazard functions reflecting those seen in practice. If the user wishes to provide a user-defined hazard or log hazard function then this is possible, and the resulting cumulative hazard function does not need to have a closed-form solution. For details see the supporting paper <doi:10.18637/jss.v097.i03>. Note that this package is modelled on the 'survsim' package available in the 'Stata' software (see Crowther and Lambert (2012) <https://www.stata-journal.com/sjpdf.html?articlenum=st0275> or Crowther and Lambert (2013) <doi:10.1002/sim.5823>).

Maintained by Sam Brilleman. Last updated 4 years ago.

4.5 match 23 stars 7.39 score 178 scripts 2 dependents

r-forge

cardidates:Identification of Cardinal Dates in Ecological Time Series

Identification of cardinal dates (begin, time of maximum, end of mass developments) in ecological time series using fitted Weibull functions.

Maintained by Thomas Petzoldt. Last updated 1 years ago.

9.4 match 3.34 score 22 scripts

abdisalammuse

AHSurv:Flexible Parametric Accelerated Hazards Models

Flexible parametric Accelerated Hazards (AH) regression models in overall and relative survival frameworks with 13 distinct Baseline Distributions. The AH Model can also be applied to lifetime data with crossed survival curves. Any user-defined parametric distribution can be fitted, given at least an R function defining the cumulative hazard and hazard rate functions. See Chen and Wang (2000) <doi:10.1080/01621459.2000.10474236>, and Lee (2015) <doi:10.1007/s10985-015-9349-5> for more details.

Maintained by Abdisalam Hassan Muse. Last updated 3 years ago.

21.2 match 1.48 score 1 dependents

daphnegiorgi

IBMPopSim:Individual Based Model Population Simulation

Simulation of the random evolution of heterogeneous populations using stochastic Individual-Based Models (IBMs) <doi:10.48550/arXiv.2303.06183>. The package enables users to simulate population evolution, in which individuals are characterized by their age and some characteristics, and the population is modified by different types of events, including births/arrivals, death/exit events, or changes of characteristics. The frequency at which an event can occur to an individual can depend on their age and characteristics, but also on the characteristics of other individuals (interactions). Such models have a wide range of applications. For instance, IBMs can be used for simulating the evolution of a heterogeneous insurance portfolio with selection or for validating mortality forecasts. This package overcomes the limitations of time-consuming IBMs simulations by implementing new efficient algorithms based on thinning methods, which are compiled using the 'Rcpp' package while providing a user-friendly interface.

Maintained by Daphné Giorgi. Last updated 5 months ago.

cpp

5.3 match 8 stars 5.83 score 14 scripts

epinowcast

epidist:Estimate Epidemiological Delay Distributions With brms

Understanding and accurately estimating epidemiological delay distributions is important for public health policy. These estimates influence epidemic situational awareness, control strategies, and resource allocation. This package provides methods to address the key challenges in estimating these distributions, including truncation, interval censoring, and dynamical biases. These issues are frequently overlooked, resulting in biased conclusions. Built on top of 'brms', it allows for flexible modelling including time-varying spatial components and partially pooled estimates of demographic characteristics.

Maintained by Sam Abbott. Last updated 8 days ago.

4.7 match 14 stars 6.52 score 7 scripts

mitchelloharawild

distributional:Vectorised Probability Distributions

Vectorised distribution objects with tools for manipulating, visualising, and using probability distributions. Designed to allow model prediction outputs to return distributions rather than their parameters, allowing users to directly interact with predictive distributions in a data-oriented workflow. In addition to providing generic replacements for p/d/q/r functions, other useful statistics can be computed including means, variances, intervals, and highest density regions.

Maintained by Mitchell OHara-Wild. Last updated 2 months ago.

probability-distribution statistics vctrs

2.3 match 101 stars 13.50 score 744 scripts 384 dependents

paulgovan

WeibullR.learnr:An Interactive Introduction to Life Data Analysis

An interactive introduction to Life Data Analysis that depends on 'WeibullR' by David Silkworth and Jurgen Symynck (2022) <https://CRAN.R-project.org/package=WeibullR>, a R package for Weibull Analysis, and 'learnr' by Garrick Aden-Buie et al. (2023) <https://CRAN.R-project.org/package=learnr>, a framework for building interactive learning modules in R.

Maintained by Paul Govan. Last updated 9 days ago.

life-data-analysis reliability tutorial weibull-analysis

8.0 match 3.78 score 3 scripts

jwb133

smcfcs:Multiple Imputation of Covariates by Substantive Model Compatible Fully Conditional Specification

Implements multiple imputation of missing covariates by Substantive Model Compatible Fully Conditional Specification. This is a modification of the popular FCS/chained equations multiple imputation approach, and allows imputation of missing covariate values from models which are compatible with the user specified substantive model.

Maintained by Jonathan Bartlett. Last updated 1 days ago.

3.3 match 11 stars 9.00 score 59 scripts 1 dependents

appliedstat

bsgof:Birnbaum-Saunders Goodness-of-Fit Test

Performs goodness-of-fit test for the Birnbaum-Saunders distribution and provides the maximum likelihood estimate and the method-of-moments estimate. For more details, see Park and Wang (2013) <arXiv:2308.10150>. This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korea government (MSIT) (No. 2022R1A2C1091319, RS-2023-00242528).

Maintained by Chanseok Park. Last updated 1 years ago.

control-chart goodness-of-fit r-language weibull

10.0 match 2 stars 3.00 score

kainhofer

MortalityTables:A Framework for Various Types of Mortality / Life Tables

Classes to implement, analyze and plot cohort life tables for actuarial calculations. Birth-year dependent cohort mortality tables using a yearly trend to extrapolate from a base year are implemented, as well as period life table, cohort life tables using an age shift, and merged life tables. Additionally, several data sets from various countries are included to provide widely-used tables out of the box.

Maintained by Reinhold Kainhofer. Last updated 1 years ago.

5.1 match 1 stars 5.70 score 84 scripts 2 dependents

drizopoulos

JM:Joint Modeling of Longitudinal and Survival Data

Shared parameter models for the joint modeling of longitudinal and time-to-event data.

Maintained by Dimitris Rizopoulos. Last updated 3 years ago.

5.9 match 2 stars 4.93 score 112 scripts 1 dependents

xinweihuang-stat

Copula.Markov.survival:Copula Markov Model with Dependent Censoring

Perform likelihood estimation and corresponding analysis under the copula-based Markov chain model for serially dependent event times with a dependent terminal event. Available are statistical methods in Huang, Wang and Emura (2020, JJSD accepted).

Maintained by Xin-Wei Huang. Last updated 5 years ago.

29.0 match 1.00 score

kkholst

lava:Latent Variable Models

A general implementation of Structural Equation Models with latent variables (MLE, 2SLS, and composite likelihood estimators) with both continuous, censored, and ordinal outcomes (Holst and Budtz-Joergensen (2013) <doi:10.1007/s00180-012-0344-y>). Mixture latent variable models and non-linear latent variable models (Holst and Budtz-Joergensen (2020) <doi:10.1093/biostatistics/kxy082>). The package also provides methods for graph exploration (d-separation, back-door criterion), simulation of general non-linear latent variable models, and estimation of influence functions for a broad range of statistical models.

Maintained by Klaus K. Holst. Last updated 2 months ago.

latent-variable-models simulation statistics structural-equation-models

2.3 match 33 stars 12.85 score 610 scripts 476 dependents

jonasmoss

univariateML:Maximum Likelihood Estimation for Univariate Densities

User-friendly maximum likelihood estimation (Fisher (1921) <doi:10.1098/rsta.1922.0009>) of univariate densities.

Maintained by Jonas Moss. Last updated 13 days ago.

density estimation maximum-likelihood

3.5 match 8 stars 8.10 score 62 scripts 7 dependents

fgaspe04

discfrail:Cox Models for Time-to-Event Data with Nonparametric Discrete Group-Specific Frailties

Functions for fitting Cox proportional hazards models for grouped time-to-event data, where the shared group-specific frailties have a discrete nonparametric distribution. There are also functions for simulating from these models, and from similar models with a parametric baseline survival function.

Maintained by Francesca Gasperoni. Last updated 6 years ago.

9.5 match 1 stars 3.00 score 8 scripts

cran

evmix:Extreme Value Mixture Modelling, Threshold Estimation and Boundary Corrected Kernel Density Estimation

The usual distribution functions, maximum likelihood inference and model diagnostics for univariate stationary extreme value mixture models are provided. Kernel density estimation including various boundary corrected kernel density estimation methods and a wide choice of kernels, with cross-validation likelihood based bandwidth estimator. Reasonable consistency with the base functions in the 'evd' package is provided, so that users can safely interchange most code.

Maintained by Carl Scarrott. Last updated 6 years ago.

9.9 match 2 stars 2.85 score 7 dependents

kleest0

SemiCompRisks:Hierarchical Models for Parametric and Semi-Parametric Analyses of Semi-Competing Risks Data

Hierarchical multistate models are considered to perform the analysis of independent/clustered semi-competing risks data. The package allows to choose the specification for model components from a range of options giving users substantial flexibility, including: accelerated failure time or proportional hazards regression models; parametric or non-parametric specifications for baseline survival functions and cluster-specific random effects distribution; a Markov or semi-Markov specification for terminal event following non-terminal event. While estimation is mainly performed within the Bayesian paradigm, the package also provides the maximum likelihood estimation approach for several parametric models. The package also includes functions for univariate survival analysis as complementary analysis tools.

Maintained by Kyu Ha Lee. Last updated 4 years ago.

gsl

11.6 match 2.41 score 26 scripts

danilinares

quickpsy:Fits Psychometric Functions for Multiple Groups

Quickly fits and plots psychometric functions (normal, logistic, Weibull or any function defined by the user) for multiple groups.

Maintained by Daniel Linares. Last updated 2 years ago.

4.8 match 24 stars 5.85 score 66 scripts

cran

icmstate:Interval Censored Multi-State Models

Allows for the non-parametric estimation of transition intensities in interval-censored multi-state models using the approach of Gomon and Putter (2024) <doi:10.48550/arXiv.2409.07176> or Gu et al. (2023) <doi:10.1093/biomet/asad073>.

Maintained by Daniel Gomon. Last updated 4 months ago.

cpp

7.6 match 3.65 score 15 scripts

twolodzko

extraDistr:Additional Univariate and Multivariate Distributions

Density, distribution function, quantile function and random generation for a number of univariate and multivariate distributions. This package implements the following distributions: Bernoulli, beta-binomial, beta-negative binomial, beta prime, Bhattacharjee, Birnbaum-Saunders, bivariate normal, bivariate Poisson, categorical, Dirichlet, Dirichlet-multinomial, discrete gamma, discrete Laplace, discrete normal, discrete uniform, discrete Weibull, Frechet, gamma-Poisson, generalized extreme value, Gompertz, generalized Pareto, Gumbel, half-Cauchy, half-normal, half-t, Huber density, inverse chi-squared, inverse-gamma, Kumaraswamy, Laplace, location-scale t, logarithmic, Lomax, multivariate hypergeometric, multinomial, negative hypergeometric, non-standard beta, normal mixture, Poisson mixture, Pareto, power, reparametrized beta, Rayleigh, shifted Gompertz, Skellam, slash, triangular, truncated binomial, truncated normal, truncated Poisson, Tukey lambda, Wald, zero-inflated binomial, zero-inflated negative binomial, zero-inflated Poisson.

Maintained by Tymoteusz Wolodzko. Last updated 10 days ago.

c-plus-plus c-plus-plus-11 distribution multivariate-distributions probability random-generation rcpp statistics cpp

2.4 match 53 stars 11.60 score 1.5k scripts 107 dependents

agi-lab

SynthETIC:Synthetic Experience Tracking Insurance Claims

Creation of an individual claims simulator which generates various features of non-life insurance claims. An initial set of test parameters, designed to mirror the experience of an Auto Liability portfolio, were set up and applied by default to generate a realistic test data set of individual claims (see vignette). The simulated data set then allows practitioners to back-test the validity of various reserving models and to prove and/or disprove certain actuarial assumptions made in claims modelling. The distributional assumptions used to generate this data set can be easily modified by users to match their experiences. Reference: Avanzi B, Taylor G, Wang M, Wong B (2020) "SynthETIC: an individual insurance claim simulator with feature control" <arXiv:2008.05693>.

Maintained by Melantha Wang. Last updated 1 years ago.

4.4 match 12 stars 6.22 score 23 scripts 2 dependents

sparklyr

sparklyr:R Interface to Apache Spark

R interface to Apache Spark, a fast and general engine for big data processing, see <https://spark.apache.org/>. This package supports connecting to local and remote Apache Spark clusters, provides a 'dplyr' compatible back-end, and provides an interface to Spark's built-in machine learning algorithms.

Maintained by Edgar Ruiz. Last updated 9 days ago.

apache-spark distributed dplyr ide livy machine-learning remote-clusters spark sparklyr

1.8 match 959 stars 15.16 score 4.0k scripts 21 dependents

dm13450

dirichletprocess:Build Dirichlet Process Objects for Bayesian Modelling

Perform nonparametric Bayesian analysis using Dirichlet processes without the need to program the inference algorithms. Utilise included pre-built models or specify custom models and allow the 'dirichletprocess' package to handle the Markov chain Monte Carlo sampling. Our Dirichlet process objects can act as building blocks for a variety of statistical models including and not limited to: density estimation, clustering and prior distributions in hierarchical models. See Teh, Y. W. (2011) <https://www.stats.ox.ac.uk/~teh/research/npbayes/Teh2010a.pdf>, among many other sources.

Maintained by Dean Markwick. Last updated 2 years ago.

bayesian bayesian-inference bayesian-statistics dirichlet-process mcmc

3.7 match 58 stars 7.40 score 72 scripts 2 dependents

irsn

Renext:Renewal Method for Extreme Values Extrapolation

Peaks Over Threshold (POT) or 'methode du renouvellement'. The distribution for the excesses can be chosen, and heterogeneous data (including historical data or block data) can be used in a Maximum-Likelihood framework.

Maintained by Yann Richet. Last updated 1 years ago.

5.4 match 4.99 score 82 scripts 4 dependents

rezamoammadi

BDgraph:Bayesian Structure Learning in Graphical Models using Birth-Death MCMC

Advanced statistical tools for Bayesian structure learning in undirected graphical models, accommodating continuous, ordinal, discrete, count, and mixed data. It integrates recent advancements in Bayesian graphical models as presented in the literature, including the works of Mohammadi and Wit (2015) <doi:10.1214/14-BA889>, Mohammadi et al. (2021) <doi:10.1080/01621459.2021.1996377>, Dobra and Mohammadi (2018) <doi:10.1214/18-AOAS1164>, and Mohammadi et al. (2023) <doi:10.48550/arXiv.2307.00127>.

Maintained by Reza Mohammadi. Last updated 7 months ago.

openblas cpp openmp

3.5 match 8 stars 7.45 score 223 scripts 7 dependents

gsk-biostatistics

beastt:Bayesian Evaluation, Analysis, and Simulation Software Tools for Trials

Bayesian dynamic borrowing with covariate adjustment via inverse probability weighting for simulations and data analyses in clinical trials. This makes it easy to use propensity score methods to balance covariate distributions between external and internal data.

Maintained by Christina Fillmore. Last updated 12 days ago.

cpp

3.9 match 3 stars 6.62 score 4 scripts

piusdahinden

disprofas:Non-Parametric Dissolution Profile Analysis

Similarity of dissolution profiles is assessed using the similarity factor f2 according to the EMA guideline (European Medicines Agency 2010) "On the investigation of bioequivalence". Dissolution profiles are regarded as similar if the f2 value is between 50 and 100. For the applicability of the similarity factor f2, the variability between profiles needs to be within certain limits. Often, this constraint is violated. One possibility in this situation is to resample the measured profiles in order to obtain a bootstrap estimate of f2 (Shah et al. (1998) <doi:10.1023/A:1011976615750>). Other alternatives are the model-independent non-parametric multivariate confidence region (MCR) procedure (Tsong et al. (1996) <doi:10.1177/009286159603000427>) or the T2-test for equivalence procedure (Hoffelder (2016) <https://www.ecv.de/suse_item.php?suseId=Z|pi|8430>). Functions for estimation of f1, f2, bootstrap f2, MCR / T2-test for equivalence procedure are implemented.

Maintained by Pius Dahinden. Last updated 9 months ago.

6.9 match 3.70 score 2 scripts

swihart

event:Event History Procedures and Models

Functions for setting up and analyzing event history data.

Maintained by Bruce Swihart. Last updated 8 years ago.

fortran

5.3 match 1 stars 4.74 score 548 scripts

epiverse-trace

superspreading:Understand Individual-Level Variation in Infectious Disease Transmission

Estimate and understand individual-level variation in transmission. Implements density and cumulative compound Poisson discrete distribution functions ('Kremer et al.' (2021) <doi:10.1038/s41598-021-93578-x>), as well as functions to calculate infectious disease outbreak statistics given epidemiological parameters on individual-level transmission; including the probability of an outbreak becoming an epidemic/extinct ('Kucharski et al.' (2020) <doi:10.1016/S1473-3099(20)30144-4>), or the cluster size statistics, e.g. what proportion of cases cause X\% of transmission ('Lloyd-Smith et al.' (2005) <doi:10.1038/nature04153>).

Maintained by Joshua W. Lambert. Last updated 2 months ago.

disease-transmission epidemiology epiverse

3.5 match 4 stars 6.98 score 16 scripts

unuran

Runuran:R Interface to the 'UNU.RAN' Random Variate Generators

Interface to the 'UNU.RAN' library for Universal Non-Uniform RANdom variate generators. Thus it allows to build non-uniform random number generators from quite arbitrary distributions. In particular, it provides an algorithm for fast numerical inversion for distribution with given density function. In addition, the package contains densities, distribution functions and quantiles from a couple of distributions.

Maintained by Josef Leydold. Last updated 5 months ago.

3.5 match 6.87 score 180 scripts 8 dependents

sahirbhatnagar

casebase:Fitting Flexible Smooth-in-Time Hazards and Risk Functions via Logistic and Multinomial Regression

Fit flexible and fully parametric hazard regression models to survival data with single event type or multiple competing causes via logistic and multinomial regression. Our formulation allows for arbitrary functional forms of time and its interactions with other predictors for time-dependent hazards and hazard ratios. From the fitted hazard model, we provide functions to readily calculate and plot cumulative incidence and survival curves for a given covariate profile. This approach accommodates any log-linear hazard function of prognostic time, treatment, and covariates, and readily allows for non-proportionality. We also provide a plot method for visualizing incidence density via population time plots. Based on the case-base sampling approach of Hanley and Miettinen (2009) <DOI:10.2202/1557-4679.1125>, Saarela and Arjas (2015) <DOI:10.1111/sjos.12125>, and Saarela (2015) <DOI:10.1007/s10985-015-9352-x>.

Maintained by Sahir Bhatnagar. Last updated 7 months ago.

competing-risks cox-regression regression-models survival-analysis

3.4 match 9 stars 7.16 score 94 scripts

txm676

sars:Fit and Compare Species-Area Relationship Models Using Multimodel Inference

Implements the basic elements of the multi-model inference paradigm for up to twenty species-area relationship models (SAR), using simple R list-objects and functions, as in Triantis et al. 2012 <DOI:10.1111/j.1365-2699.2011.02652.x>. The package is scalable and users can easily create their own model and data objects. Additional SAR related functions are provided.

Maintained by Thomas J. Matthews. Last updated 10 days ago.

3.5 match 9 stars 6.67 score 95 scripts

cmstatr

cmstatr:Statistical Methods for Composite Material Data

An implementation of the statistical methods commonly used for advanced composite materials in aerospace applications. This package focuses on calculating basis values (lower tolerance bounds) for material strength properties, as well as performing the associated diagnostic tests. This package provides functions for calculating basis values assuming several different distributions, as well as providing functions for non-parametric methods of computing basis values. Functions are also provided for testing the hypothesis that there is no difference between strength and modulus data from an alternate sample and that from a "qualification" or "baseline" sample. For a discussion of these statistical methods and their use, see the Composite Materials Handbook, Volume 1 (2012, ISBN: 978-0-7680-7811-4). Additional details about this package are available in the paper by Kloppenborg (2020, <doi:10.21105/joss.02265>).

Maintained by Stefan Kloppenborg. Last updated 4 months ago.

composite-material-data data materials-science statistical-analysis statistics

3.7 match 4 stars 6.26 score 23 scripts

utiligize

CNAIM:Common Network Asset Indices Methodology (CNAIM)

Implementation of the CNAIM standard in R. Contains a series of algorithms which determine the probability of failure, consequences of failure and monetary risk associated with electricity distribution companies' assets such as transformers and cables. Results are visualized in an easy-to-understand risk matrix.

Maintained by Mohsin Vindhani. Last updated 3 years ago.

3.8 match 5 stars 6.17 score 85 scripts

alessandro-barbiero

DiscreteWeibull:Discrete Weibull Distributions (Type 1 and 3)

Probability mass function, distribution function, quantile function, random generation and parameter estimation for the type I and III discrete Weibull distributions.

Maintained by Alessandro Barbiero. Last updated 9 years ago.

8.8 match 2.56 score 20 scripts 6 dependents

sriharitn

foretell:Projecting Customer Retention Based on Fader and Hardie Probability Models

Project Customer Retention based on Beta Geometric, Beta Discrete Weibull and Latent Class Discrete Weibull Models.This package is based on Fader and Hardie (2007) <doi:10.1002/dir.20074> and Fader and Hardie et al. (2018) <doi:10.1016/j.intmar.2018.01.002>.

Maintained by Srihari Jaganathan. Last updated 2 months ago.

5.9 match 5 stars 3.70 score 2 scripts

chgrl

bReeze:Functions for Wind Resource Assessment

A collection of functions to analyse, visualize and interpret wind data and to calculate the potential energy production of wind turbines.

Maintained by Christian Graul. Last updated 1 years ago.

5.0 match 20 stars 4.34 score 22 scripts

flyaflya

causact:Fast, Easy, and Visual Bayesian Inference

Accelerate Bayesian analytics workflows in 'R' through interactive modelling, visualization, and inference. Define probabilistic graphical models using directed acyclic graphs (DAGs) as a unifying language for business stakeholders, statisticians, and programmers. This package relies on interfacing with the 'numpyro' python package.

Maintained by Adam Fleischhacker. Last updated 2 months ago.

bayesian-inference dags posterior-probability probabilistic-graphical-models probabilistic-programming

3.0 match 45 stars 7.15 score 52 scripts

ajmcneil

tscopula:Time Series Copula Models

Functions for the analysis of time series using copula models. The package is based on methodology described in the following references. McNeil, A.J. (2021) <doi:10.3390/risks9010014>, Bladt, M., & McNeil, A.J. (2021) <doi:10.1016/j.ecosta.2021.07.004>, Bladt, M., & McNeil, A.J. (2022) <doi:10.1515/demo-2022-0105>.

Maintained by Alexander McNeil. Last updated 24 days ago.

3.9 match 2 stars 5.53 score 12 scripts

abdisalammuse

AmoudSurv:Tractable Parametric Odds-Based Regression Models

Fits tractable fully parametric odds-based regression models for survival data, including proportional odds (PO), accelerated failure time (AFT), accelerated odds (AO), and General Odds (GO) models in overall survival frameworks. Given at least an R function specifying the survivor, hazard rate and cumulative distribution functions, any user-defined parametric distribution can be fitted. We applied and evaluated a minimum of seventeen (17) various baseline distributions that can handle different failure rate shapes for each of the four different proposed odds-based regression models. For more information see Bennet et al., (1983) <doi:10.1002/sim.4780020223>, and Muse et al., (2022) <doi:10.1016/j.aej.2022.01.033>.

Maintained by Abdisalam Hassan Muse. Last updated 3 years ago.

21.3 match 1.00 score

nagodem

rebmix:Finite Mixture Modeling, Clustering & Classification

Random univariate and multivariate finite mixture model generation, estimation, clustering, latent class analysis and classification. Variables can be continuous, discrete, independent or dependent and may follow normal, lognormal, Weibull, gamma, Gumbel, binomial, Poisson, Dirac, uniform or circular von Mises parametric families.

Maintained by Marko Nagode. Last updated 8 months ago.

cpp

8.0 match 1 stars 2.66 score 43 scripts

emrahaltun

ERPeq:Probabilistic Hazard Assessment

Computes the probability density and cumulative distribution functions of fourteen distributions used for the probabilistic hazard assessment. Estimates the model parameters of the distributions using the maximum likelihood and reports the goodness-of-fit statistics. The recurrence interval estimations of earthquakes are computed for each distribution.

Maintained by Emrah Altun. Last updated 2 years ago.

21.2 match 1.00 score

camillejo

cpsurvsim:Simulating Survival Data from Change-Point Hazard Distributions

Simulates time-to-event data with type I right censoring using two methods: the inverse CDF method and our proposed memoryless method. The latter method takes advantage of the memoryless property of survival and simulates a separate distribution between change-points. We include two parametric distributions: exponential and Weibull. Inverse CDF method draws on the work of Rainer Walke (2010), <https://www.demogr.mpg.de/papers/technicalreports/tr-2010-003.pdf>.

Maintained by Camille Hochheimer. Last updated 2 years ago.

6.9 match 3.08 score 12 scripts

mpascariu

MortalityLaws:Parametric Mortality Models, Life Tables and HMD

Fit the most popular human mortality 'laws', and construct full and abridge life tables given various input indices. A mortality law is a parametric function that describes the dying-out process of individuals in a population during a significant portion of their life spans. For a comprehensive review of the most important mortality laws see Tabeau (2001) <doi:10.1007/0-306-47562-6_1>. Practical functions for downloading data from various human mortality databases are provided as well.

Maintained by Marius D. Pascariu. Last updated 1 years ago.

actuarial-science demography download-hmd human-mortality-laws life-table mortality

3.0 match 32 stars 7.00 score 103 scripts 1 dependents

harrysouthworth

texmex:Statistical Modelling of Extreme Values

Statistical extreme value modelling of threshold excesses, maxima and multivariate extremes. Univariate models for threshold excesses and maxima are the Generalised Pareto, and Generalised Extreme Value model respectively. These models may be fitted by using maximum (optionally penalised-)likelihood, or Bayesian estimation, and both classes of models may be fitted with covariates in any/all model parameters. Model diagnostics support the fitting process. Graphical output for visualising fitted models and return level estimates is provided. For serially dependent sequences, the intervals declustering algorithm of Ferro and Segers (2003) <doi:10.1111/1467-9868.00401> is provided, with diagnostic support to aid selection of threshold and declustering horizon. Multivariate modelling is performed via the conditional approach of Heffernan and Tawn (2004) <doi:10.1111/j.1467-9868.2004.02050.x>, with graphical tools for threshold selection and to diagnose estimation convergence.

Maintained by Harry Southworth. Last updated 1 years ago.

cpp

3.0 match 7 stars 6.92 score 66 scripts 1 dependents

sachsmc

stdReg2:Regression Standardization for Causal Inference

Contains more modern tools for causal inference using regression standardization. Four general classes of models are implemented; generalized linear models, conditional generalized estimating equation models, Cox proportional hazards models, and shared frailty gamma-Weibull models. Methodological details are described in Sjölander, A. (2016) <doi:10.1007/s10654-016-0157-3>. Also includes functionality for doubly robust estimation for generalized linear models in some special cases, and the ability to implement custom models.

Maintained by Michael C Sachs. Last updated 15 days ago.

4.0 match 2 stars 5.08 score 9 scripts

geobosh

Countr:Flexible Univariate Count Models Based on Renewal Processes

Flexible univariate count models based on renewal processes. The models may include covariates and can be specified with familiar formula syntax as in glm() and package 'flexsurv'. The methodology is described by Kharrat et all (2019) <doi:10.18637/jss.v090.i13> (included as vignette 'Countr_guide' in the package). If the suggested package 'pscl' is not available from CRAN, it can be installed with 'remotes::install_github("cran/pscl")'. It is no longer used by the functions in this package but is needed for some of the extended examples.

Maintained by Georgi N. Boshnakov. Last updated 1 years ago.

count-data renewal-process sports-modelling openblas cpp

3.5 match 4 stars 5.71 score 43 scripts

pik-piam

mredgebuildings:Prepare data to be used by the EDGE-Buildings model

Prepare data to be used by the EDGE-Buildings model.

Maintained by Robin Hasse. Last updated 1 days ago.

5.4 match 3.72 score

modal-inria

RMixtComp:Mixture Models with Heterogeneous and (Partially) Missing Data

Mixture Composer (Biernacki (2015) <https://inria.hal.science/hal-01253393v1>) is a project to perform clustering using mixture models with heterogeneous data and partially missing data. Mixture models are fitted using a SEM algorithm. It includes 8 models for real, categorical, counting, functional and ranking data.

Maintained by Quentin Grimonprez. Last updated 10 months ago.

clustering cpp heterogeneous-data missing-data mixed-data mixture-model statistics

3.3 match 13 stars 6.10 score 12 scripts

kanji709

marp:Model-Averaged Renewal Process

To implement a model-averaging approach with different renewal models, with a primary focus on forecasting large earthquakes. Based on six renewal models (i.e., Poisson, Gamma, Log-Logistics, Weibull, Log-Normal and BPT), model-averaged point estimates are calculated using AIC (or BIC) weights. Additionally, both percentile and studentized bootstrapped model-averaged confidence intervals are constructed. In comparison, point and interval estimation from the individual or "best" model (determined via model selection) can be retrieved.

Maintained by Jie Kang. Last updated 6 days ago.

5.6 match 1 stars 3.48 score

facebookexperimental

Robyn:Semi-Automated Marketing Mix Modeling (MMM) from Meta Marketing Science

Semi-Automated Marketing Mix Modeling (MMM) aiming to reduce human bias by means of ridge regression and evolutionary algorithms, enables actionable decision making providing a budget allocation and diminishing returns curves and allows ground-truth calibration to account for causation.

Maintained by Gufeng Zhou. Last updated 18 days ago.

adstocking budget-allocation cost-response-curve econometrics evolutionary-algorithm gradient-based-optimisation hyperparameter-optimization marketing-mix-modeling marketing-mix-modelling marketing-science mmm ridge-regression

1.9 match 1.2k stars 10.32 score 95 scripts

bayesball

LearnBayes:Learning Bayesian Inference

Contains functions for summarizing basic one and two parameter posterior distributions and predictive distributions. It contains MCMC algorithms for summarizing posterior distributions defined by the user. It also contains functions for regression models, hierarchical models, Bayesian tests, and illustrations of Gibbs sampling.

Maintained by Jim Albert. Last updated 7 years ago.

1.7 match 38 stars 11.34 score 690 scripts 31 dependents

daniel258

ICcalib:Cox Model with Interval-Censored Starting Time of a Covariate

Calibration and risk-set calibration methods for fitting Cox proportional hazard model when a binary covariate is measured intermittently. Methods include functions to fit calibration models from interval-censored data and modified partial likelihood for the proportional hazard model, Nevo et al. (2018+) <arXiv:1801.01529>.

Maintained by Daniel Nevo. Last updated 7 years ago.

cpp

6.8 match 2.78 score 12 scripts

cran

evd:Functions for Extreme Value Distributions

Extends simulation, distribution, quantile and density functions to univariate and multivariate parametric extreme value distributions, and provides fitting functions which calculate maximum likelihood estimates for univariate and bivariate maxima models, and for univariate and bivariate threshold models.

Maintained by Alec Stephenson. Last updated 6 months ago.

2.0 match 2 stars 9.46 score 748 scripts 82 dependents

fabianenc

r6qualitytools:R6-Based Statistical Methods for Quality Science

A comprehensive suite of statistical tools for Quality Management, designed around the Define, Measure, Analyze, Improve, and Control (DMAIC) cycle used in Six Sigma methodology. Based on the discontinued CRAN package 'qualitytools', this package refactors its original design by incorporating 'R6' object-oriented programming for increased flexibility and performance. It replaces traditional graphics with modern, interactive visualizations using 'ggplot2' and 'plotly'. Built on 'tidyverse' principles, it simplifies data manipulation and visualization, offering an intuitive approach to quality science.

Maintained by Fabian Encarnacion. Last updated 5 months ago.

5.4 match 2 stars 3.48 score 3 scripts

jgoungounga

curesurv:Mixture and Non Mixture Parametric Cure Models to Estimate Cure Indicators

Fits a variety of cure models using excess hazard modeling methodology such as the mixture model proposed by Phillips et al. (2002) <doi:10.1002/sim.1101> The Weibull distribution is used to represent the survival function of the uncured patients; Fits also non-mixture cure model such as the time-to-null excess hazard model proposed by Boussari et al. (2020) <doi:10.1111/biom.13361>.

Maintained by Juste Goungounga. Last updated 8 days ago.

4.9 match 2 stars 3.60 score

wroda22

QAEnsemble:Ensemble Quadratic and Affine Invariant Markov Chain Monte Carlo

The Ensemble Quadratic and Affine Invariant Markov chain Monte Carlo algorithms provide an efficient way to perform Bayesian inference in difficult parameter space geometries. The Ensemble Quadratic Monte Carlo algorithm was developed by Militzer (2023) <doi:10.3847/1538-4357/ace1f1>. The Ensemble Affine Invariant algorithm was developed by Goodman and Weare (2010) <doi:10.2140/camcos.2010.5.65> and it was implemented in Python by Foreman-Mackey et al (2013) <doi:10.48550/arXiv.1202.3665>. The Quadratic Monte Carlo method was shown to perform better than the Affine Invariant method in the paper by Militzer (2023) <doi:10.3847/1538-4357/ace1f1> and the Quadratic Monte Carlo method is the default method used. The Chen-Shao Highest Posterior Density Estimation algorithm is used for obtaining credible intervals and the potential scale reduction factor diagnostic is used for checking the convergence of the chains.

Maintained by Weston Roda. Last updated 2 months ago.

5.5 match 3.18 score

bcgov

ssdtools:Species Sensitivity Distributions

Species sensitivity distributions are cumulative probability distributions which are fitted to toxicity concentrations for different species as described by Posthuma et al.(2001) <isbn:9781566705783>. The ssdtools package uses Maximum Likelihood to fit distributions such as the gamma, log-logistic, log-normal and log-normal log-normal mixture. Multiple distributions can be averaged using Akaike Information Criteria. Confidence intervals on hazard concentrations and proportions are produced by bootstrapping.

Maintained by Joe Thorley. Last updated 23 days ago.

ecotoxicology env species-sensitivity-distribution cpp

1.7 match 31 stars 10.23 score 111 scripts 4 dependents

theomichelot

moveHMM:Animal Movement Modelling using Hidden Markov Models

Provides tools for animal movement modelling using hidden Markov models. These include processing of tracking data, fitting hidden Markov models to movement data, visualization of data and fitted model, decoding of the state process, etc. <doi:10.1111/2041-210X.12578>.

Maintained by Theo Michelot. Last updated 12 months ago.

openblas cpp

2.0 match 38 stars 8.63 score 112 scripts

bmcclintock

momentuHMM:Maximum Likelihood Analysis of Animal Movement Behavior Using Multivariate Hidden Markov Models

Extended tools for analyzing telemetry data using generalized hidden Markov models. Features of momentuHMM (pronounced ``momentum'') include data pre-processing and visualization, fitting HMMs to location and auxiliary biotelemetry or environmental data, biased and correlated random walk movement models, discrete- or continuous-time HMMs, continuous- or discrete-space movement models, approximate Langevin diffusion models, hierarchical HMMs, multiple imputation for incorporating location measurement error and missing data, user-specified design matrices and constraints for covariate modelling of parameters, random effects, decoding of the state process, visualization of fitted models, model checking and selection, and simulation. See McClintock and Michelot (2018) <doi:10.1111/2041-210X.12995>.

Maintained by Brett McClintock. Last updated 30 days ago.

openblas cpp

2.0 match 43 stars 8.47 score 162 scripts

mrc-ide

monty:Monte Carlo Models

Experimental sources for the next generation of mcstate, now called 'monty', which will support much of the old mcstate functionality but new things like better parameter interfaces, Hamiltonian Monte Carlo, and other features.

Maintained by Rich FitzJohn. Last updated 1 months ago.

cpp

2.3 match 3 stars 7.52 score 29 scripts 3 dependents

swihart

rmutil:Utilities for Nonlinear Regression and Repeated Measurements Models

A toolkit of functions for nonlinear regression and repeated measurements not to be used by itself but called by other Lindsey packages such as 'gnlm', 'stable', 'growth', 'repeated', and 'event' (available at <https://www.commanster.eu/rcode.html>).

Maintained by Bruce Swihart. Last updated 2 years ago.

fortran

2.0 match 1 stars 8.35 score 358 scripts 70 dependents

kgoldfeld

simstudy:Simulation of Study Data

Simulates data sets in order to explore modeling techniques or better understand data generating processes. The user specifies a set of relationships between covariates, and generates data based on these specifications. The final data sets can represent data from randomized control trials, repeated measure (longitudinal) designs, and cluster randomized trials. Missingness can be generated using various mechanisms (MCAR, MAR, NMAR).

Maintained by Keith Goldfeld. Last updated 8 months ago.

data-generation data-simulation simulation statistical-models cpp

1.5 match 82 stars 11.00 score 972 scripts 1 dependents

cran

Copula.surv:Analysis of Bivariate Survival Data Based on Copulas

Simulating bivariate survival data from copula models. Estimation of the association parameter in copula models. Two different ways to estimate the association parameter in copula models are implemented. A goodness-of-fit test for a given copula model is implemented. See Emura, Lin and Wang (2010) <doi:10.1016/j.csda.2010.03.013> for details.

Maintained by Takeshi Emura. Last updated 1 months ago.

9.3 match 1.78 score

oleksii-nikolaienko

ExtDist:Extending the Range of Functions for Probability Distributions

A consistent, unified and extensible framework for estimation of parameters for probability distributions, including parameter estimation procedures that allow for weighted samples; the current set of distributions included are: the standard beta, The four-parameter beta, Burr, gamma, Gumbel, Johnson SB and SU, Laplace, logistic, normal, symmetric truncated normal, truncated normal, symmetric-reflected truncated beta, standard symmetric-reflected truncated beta, triangular, uniform, and Weibull distributions; decision criteria and selections based on these decision criteria.

Maintained by Oleksii Nikolaienko. Last updated 2 years ago.

2.8 match 1 stars 5.84 score 58 scripts 2 dependents

padpadpadpad

rTPC:Fitting and Analysing Thermal Performance Curves

Helps to fit thermal performance curves (TPCs). 'rTPC' contains 26 model formulations previously used to fit TPCs and has helper functions to set sensible start parameters, upper and lower parameter limits and estimate parameters useful in downstream analyses, such as cardinal temperatures, maximum rate and optimum temperature. See Padfield et al. (2021) <doi:10.1111/2041-210X.13585>.

Maintained by Daniel Padfield. Last updated 23 days ago.

1.8 match 25 stars 9.07 score 267 scripts

johnaponte

survobj:Objects to Simulate Survival Times

Generate objects that simulate survival times. Random values for the distributions are generated using the method described by Bender (2003) <https://epub.ub.uni-muenchen.de/id/eprint/1716> and Leemis (1987) in Operations Research, 35(6), 892–894.

Maintained by Aponte John. Last updated 7 months ago.

3.3 match 1 stars 4.74 score 11 scripts

traitecoevo

plant:A Package for Modelling Forest Trait Ecology and Evolution

Solves trait, size and patch structured model from (Falster et al. 2016) using either method of characteristics or as stochastic, finite-sized population.

Maintained by Daniel Falster. Last updated 7 days ago.

c-plus-plus demography dynamic ecology evolution forests plant-physiology science-research simulation trait cpp

2.7 match 53 stars 5.87 score

rstudio

tfprobability:Interface to 'TensorFlow Probability'

Interface to 'TensorFlow Probability', a 'Python' library built on 'TensorFlow' that makes it easy to combine probabilistic models and deep learning on modern hardware ('TPU', 'GPU'). 'TensorFlow Probability' includes a wide selection of probability distributions and bijectors, probabilistic layers, variational inference, Markov chain Monte Carlo, and optimizers such as Nelder-Mead, BFGS, and SGLD.

Maintained by Tomasz Kalinowski. Last updated 3 years ago.

1.8 match 54 stars 8.63 score 221 scripts 3 dependents

andrmenezes

unitquantreg:Parametric Quantile Regression Models for Bounded Data

A collection of parametric quantile regression models for bounded data. At present, the package provides 13 parametric quantile regression models. It can specify regression structure for any quantile and shape parameters. It also provides several S3 methods to extract information from fitted model, such as residual analysis, prediction, plotting, and model comparison. For more computation efficient the [dpqr]'s, likelihood, score and hessian functions are written in C++. For further details see Mazucheli et. al (2022) <doi:10.1016/j.cmpb.2022.106816>.

Maintained by André F. B. Menezes. Last updated 2 years ago.

cpp

3.9 match 1 stars 4.00 score 7 scripts

xiangdonggu

straweib:Stratified Weibull Regression Model

The main function is icweib(), which fits a stratified Weibull proportional hazards model for left censored, right censored, interval censored, and non-censored survival data. We parameterize the Weibull regression model so that it allows a stratum-specific baseline hazard function, but where the effects of other covariates are assumed to be constant across strata. Please refer to Xiangdong Gu, David Shapiro, Michael D. Hughes and Raji Balasubramanian (2014) <doi:10.32614/RJ-2014-003> for more details.

Maintained by Xiangdong Gu. Last updated 5 years ago.

5.7 match 2.70 score 2 scripts

kevhuy

WALS:Weighted-Average Least Squares Model Averaging

Implements Weighted-Average Least Squares model averaging for negative binomial regression models of Huynh (2024) <doi:10.48550/arXiv.2404.11324>, generalized linear models of De Luca, Magnus, Peracchi (2018) <doi:10.1016/j.jeconom.2017.12.007> and linear regression models of Magnus, Powell, Pruefer (2010) <doi:10.1016/j.jeconom.2009.07.004>, see also Magnus, De Luca (2016) <doi:10.1111/joes.12094>. Weighted-Average Least Squares for the linear regression model is based on the original 'MATLAB' code by Magnus and De Luca <https://www.janmagnus.nl/items/WALS.pdf>, see also Kumar, Magnus (2013) <doi:10.1007/s13571-013-0060-9> and De Luca, Magnus (2011) <doi:10.1177/1536867X1201100402>.

Maintained by Kevin Huynh. Last updated 9 months ago.

4.8 match 1 stars 3.18 score 1 scripts

cran

lmom:L-Moments

Functions related to L-moments: computation of L-moments and trimmed L-moments of distributions and data samples; parameter estimation; L-moment ratio diagram; plot vs. quantiles of an extreme-value distribution.

Maintained by J. R. M. Hosking. Last updated 6 months ago.

fortran

2.3 match 2 stars 6.59 score 99 scripts 139 dependents

epiverse-trace

epiparameter:Classes and Helper Functions for Working with Epidemiological Parameters

Classes and helper functions for loading, extracting, converting, manipulating, plotting and aggregating epidemiological parameters for infectious diseases. Epidemiological parameters extracted from the literature are loaded from the 'epiparameterDB' R package.

Maintained by Joshua W. Lambert. Last updated 2 months ago.

data-access data-package epidemiology epiverse probability-distribution

1.5 match 33 stars 9.84 score 102 scripts 1 dependents

lukasgudm

qmap:Statistical Transformations for Post-Processing Climate Model Output

Empirical adjustment of the distribution of variables originating from (regional) climate model simulations using quantile mapping.

Maintained by Lukas Gudmundsson. Last updated 2 months ago.

3.8 match 1 stars 3.89 score 93 scripts 5 dependents

cran

ACDm:Tools for Autoregressive Conditional Duration Models

Package for Autoregressive Conditional Duration (ACD, Engle and Russell, 1998) models. Creates trade, price or volume durations from transactions (tic) data, performs diurnal adjustments, fits various ACD models and tests them.

Maintained by Markus Belfrage. Last updated 1 years ago.

6.6 match 3 stars 2.18 score 17 scripts 1 dependents

thiagomini

WeibullFit:Fits and Plots a Dataset to the Weibull Probability Distribution Function

Provides a single function to fit data of an input data frame into one of the selected Weibull functions (w2, w3 and it's truncated versions), calculating the scale, location and shape parameters accordingly. The resulting plots and files are saved into the 'folder' parameter provided by the user. References: a) John C. Nash, Ravi Varadhan (2011). "Unifying Optimization Algorithms to Aid Software System Users: optimx for R" <doi:10.18637/jss.v043.i09>.

Maintained by Thiago Martins. Last updated 6 years ago.

5.4 match 2.70 score

gabrielshimizu

AgroReg:Regression Analysis Linear and Nonlinear for Agriculture

Linear and nonlinear regression analysis common in agricultural science articles (Archontoulis & Miguez (2015). <doi:10.2134/agronj2012.0506>). The package includes polynomial, exponential, gaussian, logistic, logarithmic, segmented, non-parametric models, among others. The functions return the model coefficients and their respective p values, coefficient of determination, root mean square error, AIC, BIC, as well as graphs with the equations automatically.

Maintained by Gabriel Danilo Shimizu. Last updated 1 years ago.

5.3 match 2.71 score 102 scripts

cran

hce:Design and Analysis of Hierarchical Composite Endpoints

Simulate and analyze hierarchical composite endpoints. Win odds is the main analysis method, but other win statistics (win ratio, net benefit) are also implemented, provided there is no censoring. See Gasparyan SB et al (2023) "Hierarchical Composite Endpoints in COVID-19: The DARE-19 Trial." Case Studies in Innovative Clinical Trials, 95-148. Chapman; Hall/CRC. <doi:10.1201/9781003288640-7>.

Maintained by Samvel B. Gasparyan. Last updated 11 days ago.

3.2 match 4.48 score 1 dependents

danforthcenter

pcvr:Plant Phenotyping and Bayesian Statistics

Analyse common types of plant phenotyping data, provide a simplified interface to longitudinal growth modeling and select Bayesian statistics, and streamline use of 'PlantCV' output. Several Bayesian methods and reporting guidelines for Bayesian methods are described in Kruschke (2018) <doi:10.1177/2515245918771304>, Kruschke (2013) <doi:10.1037/a0029146>, and Kruschke (2021) <doi:10.1038/s41562-021-01177-7>.

Maintained by Josh Sumner. Last updated 4 days ago.

2.0 match 4 stars 6.99 score 39 scripts

valentint

robust:Port of the S+ "Robust Library"

Methods for robust statistics, a state of the art in the early 2000s, notably for robust regression and robust multivariate analysis.

Maintained by Valentin Todorov. Last updated 7 months ago.

fortran openblas

1.8 match 7.52 score 572 scripts 8 dependents

cranhaven

marp:Model-Averaged Renewal Process

To implement a model-averaging approach with different renewal models, with a primary focus on forecasting large earthquakes. Based on six renewal models (i.e., Poisson, Gamma, Log-Logistics, Weibull, Log-Normal and BPT), model-averaged point estimates are calculated using AIC (or BIC) weights. Additionally, both percentile and studentized bootstrapped model-averaged confidence intervals are constructed. In comparison, point and interval estimation from the individual or "best" model (determined via model selection) can be retrieved.

Maintained by Jie Kang. Last updated 14 days ago.

archived packages r-universe

5.6 match 5 stars 2.40 score

cran

RSizeBiased:Hypothesis Testing Based on R-Size Biased Samples

Provides functions and examples for testing hypothesis about the population mean and variance on samples drawn by r-size biased sampling schemes.

Maintained by Dimitrios Bagkavos. Last updated 4 years ago.

13.4 match 1.00 score

r-forge

RobExtremes:Optimally Robust Estimation for Extreme Value Distributions

Optimally robust estimation for extreme value distributions using S4 classes and methods (based on packages 'distr', 'distrEx', 'distrMod', 'RobAStBase', and 'ROptEst'); the underlying theoretic results can be found in Ruckdeschel and Horbenko, (2013 and 2012), \doi{10.1080/02331888.2011.628022} and \doi{10.1007/s00184-011-0366-4}.

Maintained by Peter Ruckdeschel. Last updated 2 months ago.

3.6 match 3.67 score 39 scripts

cran

VGAMextra:Additions and Extensions of the 'VGAM' Package

Extending the functionalities of the 'VGAM' package with additional functions and datasets. At present, 'VGAMextra' comprises new family functions (ffs) to estimate several time series models by maximum likelihood using Fisher scoring, unlike popular packages in CRAN relying on optim(), including ARMA-GARCH-like models, the Order-(p, d, q) ARIMAX model (non- seasonal), the Order-(p) VAR model, error correction models for cointegrated time series, and ARMA-structures with Student-t errors. For independent data, new ffs to estimate the inverse- Weibull, the inverse-gamma, the generalized beta of the second kind and the general multivariate normal distributions are available. In addition, 'VGAMextra' incorporates new VGLM-links for the mean-function, and the quantile-function (as an alternative to ordinary quantile modelling) of several 1-parameter distributions, that are compatible with the class of VGLM/VGAM family functions. Currently, only fixed-effects models are implemented. All functions are subject to change; see the NEWS for further details on the latest changes.

Maintained by Victor Miranda. Last updated 1 months ago.

10.5 match 1 stars 1.26 score

onofriandreapg

statforbiology:Data Analyses in Agriculture and Biology

Contains several tools for nonlinear regression analyses and general data analysis in biology and agriculture. Contains also datasets for practicing and teaching purposes. Supports the blog: Onofri (2024) "Fixing the bridge between biologists and statisticians" <https://www.statforbiology.com> and the book: Onofri (2024) "Experimental Methods in Agriculture" <https://www.statforbiology.com/_statbookeng/>. The blog is a collection of short articles aimed at improving the efficiency of communication between biologists and statisticians, as pointed out in Kozak (2016) <doi:10.1590/0103-9016-2015-0399>, spreading a better awareness of the potential usefulness, beauty and limitations of biostatistic.

Maintained by Andrea Onofri. Last updated 4 months ago.

3.9 match 1 stars 3.40 score

akmn35

new.dist:Alternative Continuous and Discrete Distributions

The aim is to develop an R package, which is the 'new.dist' package, for the probability (density) function, the distribution function, the quantile function and the associated random number generation function for discrete and continuous distributions, which have recently been proposed in the literature. This package implements the following distributions: The Power Muth Distribution, a Bimodal Weibull Distribution, the Discrete Lindley Distribution, The Gamma-Lomax Distribution, Weighted Geometric Distribution, a Power Log-Dagum Distribution, Kumaraswamy Distribution, Lindley Distribution, the Unit-Inverse Gaussian Distribution, EP Distribution, Akash Distribution, Ishita Distribution, Maxwell Distribution, the Standard Omega Distribution, Slashed Generalized Rayleigh Distribution, Two-Parameter Rayleigh Distribution, Muth Distribution, Uniform-Geometric Distribution, Discrete Weibull Distribution.

Maintained by Ramazan Akman. Last updated 1 years ago.

2.8 match 4.70 score 101 scripts

zrmacc

PracticalEquiDesign:Design of Practical Equivalence Trials

Sample size calculations for practical equivalence trial design with a time to event endpoint.

Maintained by Zachary McCaw. Last updated 3 years ago.

6.5 match 2.00 score 3 scripts

onofriandreapg

drcSeedGerm:Utilities for Data Analyses in Seed Germination/Emergence Assays

Utility functions to be used to analyse datasets obtained from seed germination/emergence assays. Fits several types of seed germination/emergence models, including those reported in Onofri et al. (2018) "Hydrothermal-time-to-event models for seed germination", European Journal of Agronomy, 101, 129-139 <doi:10.1016/j.eja.2018.08.011>. Contains several datasets for practicing.

Maintained by Andrea Onofri. Last updated 2 months ago.

nonlinear-regression seed-germination-assays time-to-event

3.2 match 5 stars 3.97 score 37 scripts

tlamontsmith

kdist:K-Distribution and Weibull Paper

Density, distribution function, quantile function and random generation for the K-distribution. A plotting function that plots data on Weibull paper and another function to draw additional lines. See results from package in T Lamont-Smith (2018), submitted J. R. Stat. Soc.

Maintained by Tim Lamont-Smith. Last updated 7 years ago.

7.4 match 1.70 score 3 scripts

cran

BGFD:Bell-G and Complementary Bell-G Family of Distributions

Evaluates the probability density function, cumulative distribution function, quantile function, random numbers, survival function, hazard rate function, and maximum likelihood estimates for the following distributions: Bell exponential, Bell extended exponential, Bell Weibull, Bell extended Weibull, Bell-Fisk, Bell-Lomax, Bell Burr-XII, Bell Burr-X, complementary Bell exponential, complementary Bell extended exponential, complementary Bell Weibull, complementary Bell extended Weibull, complementary Bell-Fisk, complementary Bell-Lomax, complementary Bell Burr-XII and complementary Bell Burr-X distribution. Related work includes: a) Fayomi A., Tahir M. H., Algarni A., Imran M. and Jamal F. (2022). "A new useful exponential model with applications to quality control and actuarial data". Computational Intelligence and Neuroscience, 2022. <doi:10.1155/2022/2489998>. b) Alanzi, A. R., Imran M., Tahir M. H., Chesneau C., Jamal F. Shakoor S. and Sami, W. (2023). "Simulation analysis, properties and applications on a new Burr XII model based on the Bell-X functionalities". AIMS Mathematics, 8(3): 6970-7004. <doi:10.3934/math.2023352>. c) Algarni A. (2022). "Group Acceptance Sampling Plan Based on New Compounded Three-Parameter Weibull Model". Axioms, 11(9): 438. <doi:10.3390/axioms11090438>.

Maintained by Michail Tsagris. Last updated 2 years ago.

8.5 match 1.48 score 1 dependents

lenz99

incubate:Parametric Time-to-Event Analysis with Variable Incubation Phases

Fit parametric models for time-to-event data that show an initial 'incubation period', i.e., a variable delay phase where the hazard is zero. The delayed Weibull distribution serves as foundational data model. The specific method of 'MPSE' (maximum product of spacings estimation) and MLE-based methods are used for parameter estimation. Bootstrap confidence intervals for parameters and significance tests in a two group setting are provided.

Maintained by Matthias Kuhn. Last updated 7 months ago.

4.2 match 3.00 score

simnph

SimNPH:Simulate Non-Proportional Hazards

A toolkit for simulation studies concerning time-to-event endpoints with non-proportional hazards. 'SimNPH' encompasses functions for simulating time-to-event data in various scenarios, simulating different trial designs like fixed-followup, event-driven, and group sequential designs. The package provides functions to calculate the true values of common summary statistics for the implemented scenarios and offers common analysis methods for time-to-event data. Helper functions for running simulations with the 'SimDesign' package and for aggregating and presenting the results are also included. Results of the conducted simulation study are available in the paper: "A Comparison of Statistical Methods for Time-To-Event Analyses in Randomized Controlled Trials Under Non-Proportional Hazards", Klinglmüller et al. (2025) <doi:10.1002/sim.70019>.

Maintained by Tobias Fellinger. Last updated 12 days ago.

clinical-trial-simulations non-proportional-hazards statistical-simulation statistics survival-analysis

1.9 match 6 stars 6.63 score 43 scripts

blawson-bates

simEd:Simulation Education

Contains various functions to be used for simulation education, including simple Monte Carlo simulation functions, queueing simulation functions, variate generation functions capable of producing independent streams and antithetic variates, functions for illustrating random variate generation for various discrete and continuous distributions, and functions to compute time-persistent statistics. Also contains functions for visualizing: event-driven details of a single-server queue model; a Lehmer random number generator; variate generation via acceptance-rejection; and of generating a non-homogeneous Poisson process via thinning. Also contains two queueing data sets (one fabricated, one real-world) to facilitate input modeling. More details on the use of these functions can be found in Lawson and Leemis (2015) <doi:10.1109/WSC.2017.8248124>, in Kudlay, Lawson, and Leemis (2020) <doi:10.1109/WSC48552.2020.9384010>, and in Lawson and Leemis (2021) <doi:10.1109/WSC52266.2021.9715299>.

Maintained by Barry Lawson. Last updated 1 years ago.

3.6 match 3.35 score 45 scripts

jsanchezalv

WARDEN:Workflows for Health Technology Assessments in R using Discrete EveNts

Toolkit to support and perform discrete event simulations without resource constraints in the context of health technology assessments (HTA). The package focuses on cost-effectiveness modelling and aims to be submission-ready to relevant HTA bodies in alignment with 'NICE TSD 15' <https://www.sheffield.ac.uk/nice-dsu/tsds/patient-level-simulation>. More details an examples can be found in the package website <https://jsanchezalv.github.io/WARDEN/>.

Maintained by Javier Sanchez Alvarez. Last updated 3 months ago.

1.8 match 6 stars 6.69 score 9 scripts

ashesitr

reservr:Fit Distributions and Neural Networks to Censored and Truncated Data

Define distribution families and fit them to interval-censored and interval-truncated data, where the truncation bounds may depend on the individual observation. The defined distributions feature density, probability, sampling and fitting methods as well as efficient implementations of the log-density log f(x) and log-probability log P(x0 <= X <= x1) for use in 'TensorFlow' neural networks via the 'tensorflow' package. Allows training parametric neural networks on interval-censored and interval-truncated data with flexible parameterization. Applications include Claims Development in Non-Life Insurance, e.g. modelling reporting delay distributions from incomplete data, see Bücher, Rosenstock (2022) <doi:10.1007/s13385-022-00314-4>.

Maintained by Alexander Rosenstock. Last updated 9 months ago.

openblas cpp openmp

2.3 match 5 stars 5.35 score 9 scripts

giabaio

survHEhmc:Survival Analysis in Health Economic Evaluation using Hamiltonian Monte Carlo

A module to complement the backbone structure of the package 'survHE' and expand its functionality to run survival models under a Bayesian approach (based on Hamiltonian Monte Carlo). <doi:10.18637/jss.v095.i14>.

Maintained by Gianluca Baio. Last updated 12 days ago.

hamiltonian-monte-carlo health-economic-evaluations rstan survival-analysis uncertainty cpp openjdk

3.9 match 5 stars 3.00 score 1 scripts

atchanut

dtgiw:Discrete Transmuted Generalized Inverse Weibull Distribution

The Discrete Transmuted Generalized Inverse Weibull (DTGIW) distribution is a new distribution for count data analysis. The DTGIW is discrete distribution based on Atchanut and Sirinapa (2021). <DOI: 10.14456/sjst-psu.2021.149>.

Maintained by Atchanut Rattanalertnusorn. Last updated 3 years ago.

11.7 match 1.00 score 1 scripts

imranshakoor

ComRiskModel:Fitting of Complementary Risk Models

Evaluates the probability density function (PDF), cumulative distribution function (CDF), quantile function (QF), random numbers and maximum likelihood estimates (MLEs) of well-known complementary binomial-G, complementary negative binomial-G and complementary geometric-G families of distributions taking baseline models such as exponential, extended exponential, Weibull, extended Weibull, Fisk, Lomax, Burr-XII and Burr-X. The functions also allow computing the goodness-of-fit measures namely the Akaike-information-criterion (AIC), the Bayesian-information-criterion (BIC), the minimum value of the negative log-likelihood (-2L) function, Anderson-Darling (A) test, Cramer-Von-Mises (W) test, Kolmogorov-Smirnov test, P-value and convergence status. Moreover, some commonly used data sets from the fields of actuarial, reliability, and medical science are also provided. Related works include: a) Tahir, M. H., & Cordeiro, G. M. (2016). Compounding of distributions: a survey and new generalized classes. Journal of Statistical Distributions and Applications, 3, 1-35. <doi:10.1186/s40488-016-0052-1>.

Maintained by Muhammad Imran. Last updated 2 years ago.

11.7 match 1.00 score

thinhong

denim:Generate and Simulate Deterministic Discrete-Time Compartmental Models

R package to build and simulate deterministic discrete-time compartmental models that can be non-Markov. Length of stay in each compartment can be defined to follow a parametric distribution (d_exponential(), d_gamma(), d_weibull(), d_lognormal()) or a non-parametric distribution (nonparametric()). Other supported types of transition from one compartment to another includes fixed transition (constant()), multinomial (multinomial()), fixed transition probability (transprob()).

Maintained by Anh Phan. Last updated 6 days ago.

cpp

2.0 match 2 stars 5.82 score 8 scripts

dsy109

tolerance:Statistical Tolerance Intervals and Regions

Statistical tolerance limits provide the limits between which we can expect to find a specified proportion of a sampled population with a given level of confidence. This package provides functions for estimating tolerance limits (intervals) for various univariate distributions (binomial, Cauchy, discrete Pareto, exponential, two-parameter exponential, extreme value, hypergeometric, Laplace, logistic, negative binomial, negative hypergeometric, normal, Pareto, Poisson-Lindley, Poisson, uniform, and Zipf-Mandelbrot), Bayesian normal tolerance limits, multivariate normal tolerance regions, nonparametric tolerance intervals, tolerance bands for regression settings (linear regression, nonlinear regression, nonparametric regression, and multivariate regression), and analysis of variance tolerance intervals. Visualizations are also available for most of these settings.

Maintained by Derek S. Young. Last updated 9 months ago.

tolerance-intervals

1.8 match 4 stars 6.39 score 153 scripts 7 dependents

wenjie2wang

reda:Recurrent Event Data Analysis

Contains implementations of recurrent event data analysis routines including (1) survival and recurrent event data simulation from stochastic process point of view by the thinning method proposed by Lewis and Shedler (1979) <doi:10.1002/nav.3800260304> and the inversion method introduced in Cinlar (1975, ISBN:978-0486497976), (2) the mean cumulative function (MCF) estimation by the Nelson-Aalen estimator of the cumulative hazard rate function, (3) two-sample recurrent event responses comparison with the pseudo-score tests proposed by Lawless and Nadeau (1995) <doi:10.2307/1269617>, (4) gamma frailty model with spline rate function following Fu, et al. (2016) <doi:10.1080/10543406.2014.992524>.

Maintained by Wenjie Wang. Last updated 1 years ago.

mcf mean-cumulative-function recurrent-event survival-analysis cpp

1.5 match 15 stars 7.52 score 55 scripts 3 dependents

arvsjo

stdReg:Regression Standardization

Contains functionality for regression standardization. Four general classes of models are allowed; generalized linear models, conditional generalized estimating equation models, Cox proportional hazards models and shared frailty gamma-Weibull models. Sjolander, A. (2016) <doi:10.1007/s10654-016-0157-3>.

Maintained by Arvid Sjolander. Last updated 4 years ago.

4.0 match 2.80 score 53 scripts 1 dependents

remkoduursma

fitplc:Fit Hydraulic Vulnerability Curves

Fits Weibull or sigmoidal models to percent loss conductivity (plc) curves as a function of plant water potential, computes confidence intervals of parameter estimates and predictions with bootstrap or parametric methods, and provides convenient plotting methods.

Maintained by Remko Duursma. Last updated 6 years ago.

2.5 match 5 stars 4.42 score 15 scripts

cran

rriskDistributions:Fitting Distributions to Given Data or Known Quantiles

Collection of functions for fitting distributions to given data or by known quantiles. Two main functions fit.perc() and fit.cont() provide users a GUI that allows to choose a most appropriate distribution without any knowledge of the R syntax. Note, this package is a part of the 'rrisk' project.

Maintained by Matthias Greiner. Last updated 8 years ago.

3.8 match 2.94 score 1 dependents

news11

survELtest:Comparing Multiple Survival Functions with Crossing Hazards

Computing the one-sided/two-sided integrated/maximally selected EL statistics for simultaneous testing, the one-sided/two-sided EL tests for pointwise testing, and an initial test that precedes one-sided testing to exclude the possibility of crossings or alternative orderings among the survival functions.

Maintained by Guo-You Lan. Last updated 5 years ago.

3.4 match 3.20 score 32 scripts

nlmixr2

nlmixr2lib:A Model Library for 'nlmixr2'

A model library for 'nlmixr2'. The models include (and plan to include) pharmacokinetic, pharmacodynamic, and disease models used in pharmacometrics. Where applicable, references for each model are included in the meta-data for each individual model. The package also includes model composition and modification functions to make model updates easier.

Maintained by Bill Denney. Last updated 2 months ago.

1.7 match 6 stars 6.38 score 9 scripts

cran

BayesFBHborrow:Bayesian Dynamic Borrowing with Flexible Baseline Hazard Function

Allows Bayesian borrowing from a historical dataset for time-to- event data. A flexible baseline hazard function is achieved via a piecewise exponential likelihood with time varying split points and smoothing prior on the historic baseline hazards. The method is described in Scott and Lewin (2024) <doi:10.48550/arXiv.2401.06082>, and the software paper is in Axillus et al. (2024) <doi:10.48550/arXiv.2408.04327>.

Maintained by Darren Scott. Last updated 6 months ago.

7.2 match 1.48 score 1 scripts

beanumber

tidychangepoint:A Tidy Framework for Changepoint Detection Analysis

Changepoint detection algorithms for R are widespread but have different interfaces and reporting conventions. This makes the comparative analysis of results difficult. We solve this problem by providing a tidy, unified interface for several different changepoint detection algorithms. We also provide consistent numerical and graphical reporting leveraging the 'broom' and 'ggplot2' packages.

Maintained by Benjamin S. Baumer. Last updated 1 months ago.

2.0 match 2 stars 5.30 score 8 scripts

hamedhm

BDWreg:Bayesian Inference for Discrete Weibull Regression

A Bayesian regression model for discrete response, where the conditional distribution is modelled via a discrete Weibull distribution. This package provides an implementation of Metropolis-Hastings and Reversible-Jumps algorithms to draw samples from the posterior. It covers a wide range of regularizations through any two parameter prior. Examples are Laplace (Lasso), Gaussian (ridge), Uniform, Cauchy and customized priors like a mixture of priors. An extensive visual toolbox is included to check the validity of the results as well as several measures of goodness-of-fit.

Maintained by Hamed Haselimashhadi. Last updated 8 years ago.

5.3 match 2.00 score 4 scripts

martinblostein

ltmix:Left-Truncated Mixtures of Gamma, Weibull, and Lognormal Distributions

Mixture modelling of one-dimensional data using combinations of left-truncated Gamma, Weibull, and Lognormal Distributions. Blostein, Martin & Miljkovic, Tatjana. (2019) <doi:10.1016/j.insmatheco.2018.12.001>.

Maintained by Martin Blostein. Last updated 1 years ago.

5.1 match 2.00 score

ludkinm

SBMSplitMerge:Inference for a Generalised SBM with a Split Merge Sampler

Inference in a Bayesian framework for a generalised stochastic block model. The generalised stochastic block model (SBM) can capture group structure in network data without requiring conjugate priors on the edge-states. Two sampling methods are provided to perform inference on edge parameters and block structure: a split-merge Markov chain Monte Carlo algorithm and a Dirichlet process sampler. Green, Richardson (2001) <doi:10.1111/1467-9469.00242>; Neal (2000) <doi:10.1080/10618600.2000.10474879>; Ludkin (2019) <arXiv:1909.09421>.

Maintained by Matthew Ludkin. Last updated 5 years ago.

3.8 match 2.70 score 3 scripts

atchanut

tpwb:The Three Parameter Weibull Distribution

Density, distribution function, the quantile function, random generation function, and maximum likelihood estimation.

Maintained by Atchanut Rattanalertnusorn. Last updated 10 months ago.

10.0 match 1.00 score

cran

stratifyR:Optimal Stratification of Univariate Populations

The stratification of univariate populations under stratified sampling designs is implemented according to Khan et al. (2002) <doi:10.1177/0008068320020518> and Khan et al. (2015) <doi:10.1080/02664763.2015.1018674> in this library. It determines the Optimum Strata Boundaries (OSB) and Optimum Sample Sizes (OSS) for the study variable, y, using the best-fit frequency distribution of a survey variable (if data is available) or a hypothetical distribution (if data is not available). The method formulates the problem of determining the OSB as mathematical programming problem which is solved by using a dynamic programming technique. If a dataset of the population is available to the surveyor, the method estimates its best-fit distribution and determines the OSB and OSS under Neyman allocation directly. When the dataset is not available, stratification is made based on the assumption that the values of the study variable, y, are available as hypothetical realizations of proxy values of y from recent surveys. Thus, it requires certain distributional assumptions about the study variable. At present, it handles stratification for the populations where the study variable follows a continuous distribution, namely, Pareto, Triangular, Right-triangular, Weibull, Gamma, Exponential, Uniform, Normal, Log-normal and Cauchy distributions.

Maintained by Karuna G. Reddy. Last updated 21 days ago.

4.2 match 2.34 score 22 scripts

bayesiandemography

rvec:Vector Representing a Random Variable

Random vectors, called rvecs. An rvec holds multiple draws, but tries to behave like a standard R vector, including working well in data frames. Rvecs are useful for working with output from a simulation or a Bayesian analysis.

Maintained by John Bryant. Last updated 6 months ago.

1.8 match 2 stars 5.46 score 24 scripts 2 dependents

hesim-dev

hesim:Health Economic Simulation Modeling and Decision Analysis

A modular and computationally efficient R package for parameterizing, simulating, and analyzing health economic simulation models. The package supports cohort discrete time state transition models (Briggs et al. 1998) <doi:10.2165/00019053-199813040-00003>, N-state partitioned survival models (Glasziou et al. 1990) <doi:10.1002/sim.4780091106>, and individual-level continuous time state transition models (Siebert et al. 2012) <doi:10.1016/j.jval.2012.06.014>, encompassing both Markov (time-homogeneous and time-inhomogeneous) and semi-Markov processes. Decision uncertainty from a cost-effectiveness analysis is quantified with standard graphical and tabular summaries of a probabilistic sensitivity analysis (Claxton et al. 2005, Barton et al. 2008) <doi:10.1002/hec.985>, <doi:10.1111/j.1524-4733.2008.00358.x>. Use of C++ and data.table make individual-patient simulation, probabilistic sensitivity analysis, and incorporation of patient heterogeneity fast.

Maintained by Devin Incerti. Last updated 6 months ago.

health-economic-evaluation microsimulation simulation-modeling cpp

1.2 match 67 stars 8.12 score 41 scripts

cran

mixdist:Finite Mixture Distribution Models

Fit finite mixture distribution models to grouped data and conditional data by maximum likelihood using a combination of a Newton-type algorithm and the EM algorithm.

Maintained by Peter Macdonald. Last updated 7 years ago.

3.5 match 2.78 score 2 dependents

pistacliffcho

icenReg:Regression Models for Interval Censored Data

Regression models for interval censored data. Currently supports Cox-PH, proportional odds, and accelerated failure time models. Allows for semi and fully parametric models (parametric only for accelerated failure time models) and Bayesian parametric models. Includes functions for easy visual diagnostics of model fits and imputation of censored data.

Maintained by Clifford Anderson-Bergman. Last updated 1 years ago.

cpp

1.7 match 1 stars 5.74 score 140 scripts 11 dependents

sysbiolab

PathwaySpace:Spatial Projection of Network Signals along Geodesic Paths

For a given graph containing vertices, edges, and a signal associated with the vertices, the 'PathwaySpace' package performs a convolution operation, which involves a weighted combination of neighboring vertices and their associated signals. The package then uses a decay function to project these signals, creating geodesic paths on a 2D-image space. 'PathwaySpace' could have various applications, such as visualizing and analyzing network data in a graphical format that highlights the relationships and signal strengths between vertices. It can be particularly useful for understanding the influence of signals through complex networks. By combining graph theory, signal processing, and visualization, the 'PathwaySpace' package provides a novel way of representing and analyzing graph data.

Maintained by Mauro Castro. Last updated 2 months ago.

bioinformatics biological-networks graph

2.0 match 2 stars 4.85 score 5 scripts

cran

DWreg:Parametric Regression for Discrete Response

Regression for a discrete response, where the conditional distribution is modelled via a discrete Weibull distribution.

Maintained by Veronica Vinciotti. Last updated 9 years ago.

6.5 match 1.48 score 1 dependents

alexwaterboybezzina

CalcThemAll.PRM:Calculate Pesticide Risk Metric (PRM) Values from Multiple Pesticides...Calc Them All

Contains functions which can be used to calculate Pesticide Risk Metric values in aquatic environments from concentrations of multiple pesticides with known species sensitive distributions (SSDs). Pesticides provided by this package have all be validated however if the user has their own pesticides with SSD values they can append them to the pesticide_info table to include them in estimates.

Maintained by Alexander Bezzina. Last updated 11 months ago.

2.0 match 2 stars 4.78 score

gkremling

gofreg:Bootstrap-Based Goodness-of-Fit Tests for Parametric Regression

Provides statistical methods to check if a parametric family of conditional density functions fits to some given dataset of covariates and response variables. Different test statistics can be used to determine the goodness-of-fit of the assumed model, see Andrews (1997) <doi:10.2307/2171880>, Bierens & Wang (2012) <doi:10.1017/S0266466611000168>, Dikta & Scheer (2021) <doi:10.1007/978-3-030-73480-0> and Kremling & Dikta (2024) <doi:10.48550/arXiv.2409.20262>. As proposed in these papers, the corresponding p-values are approximated using a parametric bootstrap method.

Maintained by Gitte Kremling. Last updated 5 months ago.

1.8 match 5.30 score 9 scripts

bioc

DelayedRandomArray:Delayed Arrays of Random Values

Implements a DelayedArray of random values where the realization of the sampled values is delayed until they are needed. Reproducible sampling within any subarray is achieved by chunking where each chunk is initialized with a different random seed and stream. The usual distributions in the stats package are supported, along with scalar, vector and arrays for the parameters.

Maintained by Aaron Lun. Last updated 2 months ago.

datarepresentation cpp

1.8 match 5.26 score 6 scripts 1 dependents

nlmixr2

rxode2ll:Log-Likelihood Functions for 'rxode2'

Provides the log-likelihoods with gradients from 'stan' (Carpenter et al (2015), <doi:10.48550/arXiv.1509.07164>) needed for generalized log-likelihood estimation in 'nlmixr2' (Fidler et al (2019) <doi:10.1002/psp4.12445>). This is split of to reduce computational burden of recompiling 'rxode2' (Wang, Hallow and James (2016) <doi:10.1002/psp4.12052>) which runs the 'nlmixr2' models during estimation.

Maintained by Matthew L. Fidler. Last updated 3 months ago.

cpp

1.8 match 2 stars 5.14 score 1 scripts 14 dependents

cran

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>).

Maintained by Chaoyu Yuan. Last updated 3 months ago.

cpp

5.4 match 1.70 score

dmazarei

ntsDists:Neutrosophic Distributions

Computes the pdf, cdf, quantile function and generating random numbers for neutrosophic distributions. This family have been developed by different authors in the recent years. See Patro and Smarandache (2016) <doi:10.5281/zenodo.571153> and Rao et al (2023) <doi:10.5281/zenodo.7832786>.

Maintained by Danial Mazarei. Last updated 8 months ago.

distribution distributions neutrosophic neutrosophic-distributions r-programming

2.0 match 2 stars 4.56 score 1 dependents

ruben0dewitte

distributionsrd:Distribution Fitting and Evaluation

A library of density, distribution function, quantile function, (bounded) raw moments and random generation for a collection of distributions relevant for the firm size literature. Additionally, the package contains tools to fit these distributions using maximum likelihood and evaluate these distributions based on (i) log-likelihood ratio and (ii) deviations between the empirical and parametrically implied moments of the distributions. We add flexibility by allowing the considered distributions to be combined into piecewise composite or finite mixture distributions, as well as to be used when truncated. See Dewitte (2020) <https://hdl.handle.net/1854/LU-8644700> for a description and application of methods available in this package.

Maintained by Ruben Dewitte. Last updated 5 years ago.

5.3 match 1.70 score 10 scripts

brpetrucci

paleobuddy:Simulating Diversification Dynamics

Simulation of species diversification, fossil records, and phylogenies. While the literature on species birth-death simulators is extensive, including important software like 'paleotree' and 'APE', we concluded there were interesting gaps to be filled regarding possible diversification scenarios. Here we strove for flexibility over focus, implementing a large array of regimens for users to experiment with and combine. In this way, 'paleobuddy' can be used in complement to other simulators as a flexible jack of all trades, or, in the case of scenarios implemented only here, can allow for robust and easy simulations for novel situations. Environmental data modified from that in 'RPANDA': Morlon H. et al (2016) <doi:10.1111/2041-210X.12526>.

Maintained by Bruno do Rosario Petrucci. Last updated 1 months ago.

evolution macroevolution paleobiology paleontology phylogenetics

1.8 match 6 stars 4.95 score 4 scripts

tpetzoldt

FAdist:Distributions that are Sometimes Used in Hydrology

Probability distributions that are sometimes useful in hydrology.

Maintained by Thomas Petzoldt. Last updated 3 years ago.

1.9 match 4 stars 4.49 score 51 scripts 1 dependents

ryansunwork

reconstructKM:Reconstruct Individual-Level Data from Published KM Plots

Functions for reconstructing individual-level data (time, status, arm) from Kaplan-MEIER curves published in academic journals (e.g. NEJM, JCO, JAMA). The individual-level data can be used for re-analysis, meta-analysis, methodology development, etc. This package was used to generate the data for commentary such as Sun, Rich, & Wei (2018) <doi:10.1056/NEJMc1808567>. Please see the vignette for a quickstart guide.

Maintained by Ryan Sun. Last updated 4 years ago.

3.6 match 2.30 score 2 scripts

mtwesley

greta.censored:Censored Distributions for 'greta'

Provides additional censored distributions for use with 'greta', a probabilistic programming framework for Bayesian modeling. Includes censored versions of Normal, Log-Normal, Student's T, Gamma, Exponential, Weibull, Pareto, and Beta distributions with support for right, left, and interval censoring. For details on 'greta', see Golding (2019) <doi:10.21105/joss.01601>. The methods are implemented using 'TensorFlow' and 'TensorFlow Probability' for efficient computation.

Maintained by Mlen-Too Wesley. Last updated 4 months ago.

2.5 match 1 stars 3.30 score 5 scripts

cran

DFBA:Distribution-Free Bayesian Analysis

A set of functions to perform distribution-free Bayesian analyses. Included are Bayesian analogues to the frequentist Mann-Whitney U test, the Wilcoxon Signed-Ranks test, Kendall's Tau Rank Correlation Coefficient, Goodman and Kruskal's Gamma, McNemar's Test, the binomial test, the sign test, the median test, as well as distribution-free methods for testing contrasts among condition and for computing Bayes factors for hypotheses. The package also includes procedures to estimate the power of distribution-free Bayesian tests based on data simulations using various probability models for the data. The set of functions provide data analysts with a set of Bayesian procedures that avoids requiring parametric assumptions about measurement error and is robust to problem of extreme outlier scores.

Maintained by Daniel H. Barch. Last updated 1 years ago.

2.6 match 3.15 score

urswilke

ggbenjamini:Generate ficus benjamina leaf shapes with bezier curves

This package can be used to generate shapes in the form of ficus benjamina (weeping fig) leaves with bezier curves. The main output of the package are dataframes containing all the information of these bezier curves.

Maintained by Urs Wilke. Last updated 2 years ago.

1.8 match 19 stars 4.58 score 7 scripts

vladimirholy

gasmodel:Generalized Autoregressive Score Models

Estimation, forecasting, and simulation of generalized autoregressive score (GAS) models of Creal, Koopman, and Lucas (2013) <doi:10.1002/jae.1279> and Harvey (2013) <doi:10.1017/cbo9781139540933>. Model specification allows for various data types and distributions, different parametrizations, exogenous variables, joint and separate modeling of exogenous variables and dynamics, higher score and autoregressive orders, custom and unconditional initial values of time-varying parameters, fixed and bounded values of coefficients, and missing values. Model estimation is performed by the maximum likelihood method.

Maintained by Vladimír Holý. Last updated 1 years ago.

dcs gas time-series

1.5 match 14 stars 5.45 score 2 scripts

cran

double.truncation:Analysis of Doubly-Truncated Data

Likelihood-based inference methods with doubly-truncated data are developed under various models. Nonparametric models are based on Efron and Petrosian (1999) <doi:10.1080/01621459.1999.10474187> and Emura, Konno, and Michimae (2015) <doi:10.1007/s10985-014-9297-5>. Parametric models from the special exponential family (SEF) are based on Hu and Emura (2015) <doi:10.1007/s00180-015-0564-z> and Emura, Hu and Konno (2017) <doi:10.1007/s00362-015-0730-y>. The parametric location-scale models are based on Dorre et al. (2021) <doi:10.1007/s00180-020-01027-6>.

Maintained by Takeshi Emura. Last updated 3 months ago.

8.1 match 1.00 score

marc-girondot

embryogrowth:Tools to Analyze the Thermal Reaction Norm of Embryo Growth

Tools to analyze the embryo growth and the sexualisation thermal reaction norms. See <doi:10.7717/peerj.8451> for tsd functions; see <doi:10.1016/j.jtherbio.2014.08.005> for thermal reaction norm of embryo growth.

Maintained by Marc Girondot. Last updated 7 months ago.

3.4 match 1 stars 2.40 score 252 scripts

laurimeh

lmfor:Functions for Forest Biometrics

Functions for different purposes related to forest biometrics, including illustrative graphics, numerical computation, modeling height-diameter relationships, prediction of tree volumes, modelling of diameter distributions and estimation off stand density using ITD. Several empirical datasets are also included.

Maintained by Lauri Mehtatalo. Last updated 3 years ago.

3.3 match 3 stars 2.42 score 29 scripts 1 dependents

mbelitz

phenesse:Estimate Phenological Metrics using Presence-Only Data

Generates Weibull-parameterized estimates of phenology for any percentile of a distribution using the framework established in Cooke (1979) <doi:10.1093/biomet/66.2.367>. Extensive testing against other estimators suggest the weib_percentile() function is especially useful in generating more accurate and less biased estimates of onset and offset (Belitz et al. 2020 <doi.org:10.1111/2041-210X.13448>. Non-parametric bootstrapping can be used to generate confidence intervals around those estimates, although this is computationally expensive. Additionally, this package offers an easy way to perform non-parametric bootstrapping to generate confidence intervals for quantile estimates, mean estimates, or any statistical function of interest.

Maintained by Michael Belitz. Last updated 5 years ago.

2.7 match 2.95 score 18 scripts

jepusto

ARPobservation:Tools for Simulating Direct Behavioral Observation Recording Procedures Based on Alternating Renewal Processes

Tools for simulating data generated by direct observation recording. Behavior streams are simulated based on an alternating renewal process, given specified distributions of event durations and interim times. Different procedures for recording data can then be applied to the simulated behavior streams. Functions are provided for the following recording methods: continuous duration recording, event counting, momentary time sampling, partial interval recording, whole interval recording, and augmented interval recording.

Maintained by James E. Pustejovsky. Last updated 2 years ago.

1.8 match 4.41 score 52 scripts

rudjer

REBayes:Empirical Bayes Estimation and Inference

Kiefer-Wolfowitz maximum likelihood estimation for mixture models and some other density estimation and regression methods based on convex optimization. See Koenker and Gu (2017) REBayes: An R Package for Empirical Bayes Mixture Methods, Journal of Statistical Software, 82, 1--26, <DOI:10.18637/jss.v082.i08>.

Maintained by Roger Koenker. Last updated 9 months ago.

2.0 match 3 stars 3.90 score 27 scripts 1 dependents

boehringer-ingelheim

oncomsm:Bayesian Multi-State Models for Early Oncology

Implements methods to fit a parametric Bayesian multi-state model to tumor response data. The model can be used to sample from the predictive distribution to impute missing data and calculate probability of success for custom decision criteria in early clinical trials during an ongoing trial. The inference is implemented using 'stan'.

Maintained by Kevin Kunzmann. Last updated 2 years ago.

cpp

1.3 match 8 stars 5.80 score 13 scripts

cran

mistr:Mixture and Composite Distributions

A flexible computational framework for mixture distributions with the focus on the composite models.

Maintained by Lukas Sablica. Last updated 2 years ago.

1.8 match 4.28 score 80 scripts 4 dependents

mahditeimouri

ForestFit:Statistical Modelling for Plant Size Distributions

Developed for the following tasks. 1 ) Computing the probability density function, cumulative distribution function, random generation, and estimating the parameters of the eleven mixture models. 2 ) Point estimation of the parameters of two - parameter Weibull distribution using twelve methods and three - parameter Weibull distribution using nine methods. 3 ) The Bayesian inference for the three - parameter Weibull distribution. 4 ) Estimating parameters of the three - parameter Birnbaum - Saunders, generalized exponential, and Weibull distributions fitted to grouped data using three methods including approximated maximum likelihood, expectation maximization, and maximum likelihood. 5 ) Estimating the parameters of the gamma, log-normal, and Weibull mixture models fitted to the grouped data through the EM algorithm, 6 ) Estimating parameters of the nonlinear height curve fitted to the height - diameter observation, 7 ) Estimating parameters, computing probability density function, cumulative distribution function, and generating realizations from gamma shape mixture model introduced by Venturini et al. (2008) <doi:10.1214/07-AOAS156> , 8 ) The Bayesian inference, computing probability density function, cumulative distribution function, and generating realizations from univariate and bivariate Johnson SB distribution, 9 ) Robust multiple linear regression analysis when error term follows skewed t distribution, 10 ) Estimating parameters of a given distribution fitted to grouped data using method of maximum likelihood, and 11 ) Estimating parameters of the Johnson SB distribution through the Bayesian, method of moment, conditional maximum likelihood, and two - percentile method.

Maintained by Mahdi Teimouri. Last updated 2 months ago.

7.7 match 1.00 score 2 scripts

begglest

BayesCTDesign:Two Arm Bayesian Clinical Trial Design with and Without Historical Control Data

A set of functions to help clinical trial researchers calculate power and sample size for two-arm Bayesian randomized clinical trials that do or do not incorporate historical control data. At some point during the design process, a clinical trial researcher who is designing a basic two-arm Bayesian randomized clinical trial needs to make decisions about power and sample size within the context of hypothesized treatment effects. Through simulation, the simple_sim() function will estimate power and other user specified clinical trial characteristics at user specified sample sizes given user defined scenarios about treatment effect,control group characteristics, and outcome. If the clinical trial researcher has access to historical control data, then the researcher can design a two-arm Bayesian randomized clinical trial that incorporates the historical data. In such a case, the researcher needs to work through the potential consequences of historical and randomized control differences on trial characteristics, in addition to working through issues regarding power in the context of sample size, treatment effect size, and outcome. If a researcher designs a clinical trial that will incorporate historical control data, the researcher needs the randomized controls to be from the same population as the historical controls. What if this is not the case when the designed trial is implemented? During the design phase, the researcher needs to investigate the negative effects of possible historic/randomized control differences on power, type one error, and other trial characteristics. Using this information, the researcher should design the trial to mitigate these negative effects. Through simulation, the historic_sim() function will estimate power and other user specified clinical trial characteristics at user specified sample sizes given user defined scenarios about historical and randomized control differences as well as treatment effects and outcomes. The results from historic_sim() and simple_sim() can be printed with print_table() and graphed with plot_table() methods. Outcomes considered are Gaussian, Poisson, Bernoulli, Lognormal, Weibull, and Piecewise Exponential. The methods are described in Eggleston et al. (2021) <doi:10.18637/jss.v100.i21>.

Maintained by Barry Eggleston. Last updated 3 years ago.

2.4 match 2 stars 3.15 score 14 scripts

hheiling

glmmPen:High Dimensional Penalized Generalized Linear Mixed Models (pGLMM)

Fits high dimensional penalized generalized linear mixed models using the Monte Carlo Expectation Conditional Minimization (MCECM) algorithm. The purpose of the package is to perform variable selection on both the fixed and random effects simultaneously for generalized linear mixed models. The package supports fitting of Binomial, Gaussian, and Poisson data with canonical links, and supports penalization using the MCP, SCAD, or LASSO penalties. The MCECM algorithm is described in Rashid et al. (2020) <doi:10.1080/01621459.2019.1671197>. The techniques used in the minimization portion of the procedure (the M-step) are derived from the procedures of the 'ncvreg' package (Breheny and Huang (2011) <doi:10.1214/10-AOAS388>) and 'grpreg' package (Breheny and Huang (2015) <doi:10.1007/s11222-013-9424-2>), with appropriate modifications to account for the estimation and penalization of the random effects. The 'ncvreg' and 'grpreg' packages also describe the MCP, SCAD, and LASSO penalties.

Maintained by Hillary Heiling. Last updated 7 months ago.

cpp

2.0 match 6 stars 3.73 score 18 scripts

alec42

Distributacalcul:Probability Distribution Functions

Calculates expected values, variance, different moments (kth moment, truncated mean), stop-loss, mean excess loss, Value-at-Risk (VaR) and Tail Value-at-Risk (TVaR) as well as some density and cumulative (survival) functions of continuous, discrete and compound distributions. This package also includes a visual 'Shiny' component to enable students to visualize distributions and understand the impact of their parameters. This package is intended to expand the 'stats' package so as to enable students to develop an intuition for probability.

Maintained by Alec James van Rassel. Last updated 1 years ago.

2.3 match 2 stars 3.30 score 9 scripts

alessandro-barbiero

DiscreteInverseWeibull:Discrete Inverse Weibull Distribution

Probability mass function, distribution function, quantile function, random generation and parameter estimation for the discrete inverse Weibull distribution.

Maintained by Alessandro Barbiero. Last updated 9 years ago.

7.4 match 1.00 score 9 scripts

jahmadkhan

DEEVD:Density Estimation by Extreme Value Distributions

Provides mean squared error (MSE) and plot the kernel densities related to extreme value distributions with their estimated values. By using Gumbel and Weibull Kernel. See Salha et al. (2014) <doi:10.4236/ojs.2014.48061> and Khan and Akbar (2021) <doi:10.4236/ojs.2021.112018 >.

Maintained by Javaria Ahmad Khan. Last updated 3 years ago.

7.2 match 1.00 score

drjp

nimbleNoBounds:Transformed Distributions for Improved MCMC Efficiency

A collection of common univariate bounded probability distributions transformed to the unbounded real line, for the purpose of increased MCMC efficiency.

Maintained by David Pleydell. Last updated 9 months ago.

1.9 match 1 stars 3.70 score 2 scripts

sachsmc

pseval:Methods for Evaluating Principal Surrogates of Treatment Response

Contains the core methods for the evaluation of principal surrogates in a single clinical trial. Provides a flexible interface for defining models for the risk given treatment and the surrogate, the models for integration over the missing counterfactual surrogate responses, and the estimation methods. Estimated maximum likelihood and pseudo-score can be used for estimation, and the bootstrap for inference. A variety of post-estimation summary methods are provided, including print, summary, plot, and testing.

Maintained by Michael C Sachs. Last updated 6 years ago.

1.8 match 1 stars 3.88 score 15 scripts

fbartos

RoBSA:Robust Bayesian Survival Analysis

A framework for estimating ensembles of parametric survival models with different parametric families. The RoBSA framework uses Bayesian model-averaging to combine the competing parametric survival models into a model ensemble, weights the posterior parameter distributions based on posterior model probabilities and uses Bayes factors to test for the presence or absence of the individual predictors or preference for a parametric family (Bartoš, Aust & Haaf, 2022, <doi:10.1186/s12874-022-01676-9>). The user can define a wide range of informative priors for all parameters of interest. The package provides convenient functions for summary, visualizations, fit diagnostics, and prior distribution calibration.

Maintained by František Bartoš. Last updated 2 years ago.

bayesian model-averaging survival-analysis jags cpp

1.9 match 8 stars 3.60 score 1 scripts

asmahani

BSGW:Bayesian Survival Model with Lasso Shrinkage Using Generalized Weibull Regression

Bayesian survival model using Weibull regression on both scale and shape parameters. Dependence of shape parameter on covariates permits deviation from proportional-hazard assumption, leading to dynamic - i.e. non-constant with time - hazard ratios between subjects. Bayesian Lasso shrinkage in the form of two Laplace priors - one for scale and one for shape coefficients - allows for many covariates to be included. Cross-validation helper functions can be used to tune the shrinkage parameters. Monte Carlo Markov Chain (MCMC) sampling using a Gibbs wrapper around Radford Neal's univariate slice sampler (R package MfUSampler) is used for coefficient estimation.

Maintained by Alireza S. Mahani. Last updated 2 years ago.

6.7 match 1 stars 1.00 score 8 scripts