R-universe search: parametric

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

35.7 match 57 stars 13.31 score 632 scripts 43 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

26.3 match 13 stars 6.15 score 54 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.

16.0 match 2 stars 9.46 score 748 scripts 82 dependents

gavinsimpson

gratia:Graceful 'ggplot'-Based Graphics and Other Functions for GAMs Fitted Using 'mgcv'

Graceful 'ggplot'-based graphics and utility functions for working with generalized additive models (GAMs) fitted using the 'mgcv' package. Provides a reimplementation of the plot() method for GAMs that 'mgcv' provides, as well as 'tidyverse' compatible representations of estimated smooths.

Maintained by Gavin L. Simpson. Last updated 14 hours ago.

distributional-regression gam gamm generalized-additive-mixed-models generalized-additive-models ggplot2 glm lm mgcv penalized-spline random-effects smoothing splines

11.0 match 217 stars 12.99 score 1.6k scripts 2 dependents

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

14.4 match 7 stars 9.76 score 308 scripts 10 dependents

nunofachada

micompr:Multivariate Independent Comparison of Observations

A procedure for comparing multivariate samples associated with different groups. It uses principal component analysis to convert multivariate observations into a set of linearly uncorrelated statistical measures, which are then compared using a number of statistical methods. The procedure is independent of the distributional properties of samples and automatically selects features that best explain their differences, avoiding manual selection of specific points or summary statistics. It is appropriate for comparing samples of time series, images, spectrometric measures or similar multivariate observations. This package is described in Fachada et al. (2016) <doi:10.32614/RJ-2016-055>.

Maintained by Nuno Fachada. Last updated 7 months ago.

micompr multivariate multivariate-data multivariate-distributions multivariate-observations non-parametric parametric-tests statistical-analysis statistical-data statistical-methods statistical-tests

35.7 match 3 stars 3.89 score 52 scripts

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.

25.1 match 5.30 score 9 scripts

therneau

survival:Survival Analysis

Contains the core survival analysis routines, including definition of Surv objects, Kaplan-Meier and Aalen-Johansen (multi-state) curves, Cox models, and parametric accelerated failure time models.

Maintained by Terry M Therneau. Last updated 3 months ago.

6.3 match 400 stars 20.43 score 29k scripts 3.9k dependents

hojsgaard

pbkrtest:Parametric Bootstrap, Kenward-Roger and Satterthwaite Based Methods for Test in Mixed Models

Computes p-values based on (a) Satterthwaite or Kenward-Rogers degree of freedom methods and (b) parametric bootstrap for mixed effects models as implemented in the 'lme4' package. Implements parametric bootstrap test for generalized linear mixed models as implemented in 'lme4' and generalized linear models. The package is documented in the paper by Halekoh and Højsgaard, (2012, <doi:10.18637/jss.v059.i09>). Please see 'citation("pbkrtest")' for citation details.

Maintained by Søren Højsgaard. Last updated 11 days ago.

8.5 match 5 stars 14.36 score 648 scripts 915 dependents

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

21.7 match 14 stars 5.45 score 2 scripts

jonasmoss

kdensity:Kernel Density Estimation with Parametric Starts and Asymmetric Kernels

Handles univariate non-parametric density estimation with parametric starts and asymmetric kernels in a simple and flexible way. Kernel density estimation with parametric starts involves fitting a parametric density to the data before making a correction with kernel density estimation, see Hjort & Glad (1995) <doi:10.1214/aos/1176324627>. Asymmetric kernels make kernel density estimation more efficient on bounded intervals such as (0, 1) and the positive half-line. Supported asymmetric kernels are the gamma kernel of Chen (2000) <doi:10.1023/A:1004165218295>, the beta kernel of Chen (1999) <doi:10.1016/S0167-9473(99)00010-9>, and the copula kernel of Jones & Henderson (2007) <doi:10.1093/biomet/asm068>. User-supplied kernels, parametric starts, and bandwidths are supported.

Maintained by Jonas Moss. Last updated 13 days ago.

asymmetric-kernels density-estimation kernel-density-estimation non-parametric

16.9 match 16 stars 6.87 score 153 scripts 1 dependents

indrajeetpatil

statsExpressions:Tidy Dataframes and Expressions with Statistical Details

Utilities for producing dataframes with rich details for the most common types of statistical approaches and tests: parametric, nonparametric, robust, and Bayesian t-test, one-way ANOVA, correlation analyses, contingency table analyses, and meta-analyses. The functions are pipe-friendly and provide a consistent syntax to work with tidy data. These dataframes additionally contain expressions with statistical details, and can be used in graphing packages. This package also forms the statistical processing backend for 'ggstatsplot'. References: Patil (2021) <doi:10.21105/joss.03236>.

Maintained by Indrajeet Patil. Last updated 20 days ago.

bayesian-inference bayesian-statistics contingency-table correlation effectsize meta-analysis parametric robust robust-statistics statistical-details statistical-tests tidy

10.5 match 312 stars 10.97 score 146 scripts 2 dependents

rsquaredacademy

inferr:Inferential Statistics

Select set of parametric and non-parametric statistical tests. 'inferr' builds upon the solid set of statistical tests provided in 'stats' package by including additional data types as inputs, expanding and restructuring the test results. The tests included are t tests, variance tests, proportion tests, chi square tests, Levene's test, McNemar Test, Cochran's Q test and Runs test.

Maintained by Aravind Hebbali. Last updated 4 months ago.

inference inferential-statistics non-parametric parametric statistical-tests cpp

18.3 match 37 stars 6.10 score 34 scripts

mpiktas

midasr:Mixed Data Sampling Regression

Methods and tools for mixed frequency time series data analysis. Allows estimation, model selection and forecasting for MIDAS regressions.

Maintained by Vaidotas Zemlys-Balevičius. Last updated 3 years ago.

19.2 match 77 stars 5.76 score 150 scripts

indrajeetpatil

ggstatsplot:'ggplot2' Based Plots with Statistical Details

Extension of 'ggplot2', 'ggstatsplot' creates graphics with details from statistical tests included in the plots themselves. It provides an easier syntax to generate information-rich plots for statistical analysis of continuous (violin plots, scatterplots, histograms, dot plots, dot-and-whisker plots) or categorical (pie and bar charts) data. Currently, it supports the most common types of statistical approaches and tests: parametric, nonparametric, robust, and Bayesian versions of t-test/ANOVA, correlation analyses, contingency table analysis, meta-analysis, and regression analyses. References: Patil (2021) <doi:10.21105/joss.03236>.

Maintained by Indrajeet Patil. Last updated 20 days ago.

bayes-factors datascience dataviz effect-size ggplot-extension hypothesis-testing non-parametric-statistics regression-models statistical-analysis

7.2 match 2.1k stars 14.49 score 3.0k scripts 1 dependents

drostlab

philentropy:Similarity and Distance Quantification Between Probability Functions

Computes 46 optimized distance and similarity measures for comparing probability functions (Drost (2018) <doi:10.21105/joss.00765>). These comparisons between probability functions have their foundations in a broad range of scientific disciplines from mathematics to ecology. The aim of this package is to provide a core framework for clustering, classification, statistical inference, goodness-of-fit, non-parametric statistics, information theory, and machine learning tasks that are based on comparing univariate or multivariate probability functions.

Maintained by Hajk-Georg Drost. Last updated 3 months ago.

distance-measures distance-quantification information-theory jensen-shannon-divergence parametric-distributions similarity-measures statistics cpp

8.0 match 137 stars 12.44 score 484 scripts 24 dependents

tjheaton

carbondate:Calibration and Summarisation of Radiocarbon Dates

Performs Bayesian non-parametric calibration of multiple related radiocarbon determinations, and summarises the calendar age information to plot their joint calendar age density (see Heaton (2022) <doi:10.1111/rssc.12599>). Also models the occurrence of radiocarbon samples as a variable-rate (inhomogeneous) Poisson process, plotting the posterior estimate for the occurrence rate of the samples over calendar time, and providing information about potential change points.

Maintained by Timothy J Heaton. Last updated 2 months ago.

cpp

16.7 match 5 stars 5.78 score 20 scripts

felixthoemmes

rddapp:Regression Discontinuity Design Application

Estimation of both single- and multiple-assignment Regression Discontinuity Designs (RDDs). Provides both parametric (global) and non-parametric (local) estimation choices for both sharp and fuzzy designs, along with power analysis and assumption checks. Introductions to the underlying logic and analysis of RDDs are in Thistlethwaite, D. L., Campbell, D. T. (1960) <doi:10.1037/h0044319> and Lee, D. S., Lemieux, T. (2010) <doi:10.1257/jel.48.2.281>.

Maintained by Felix Thoemmes. Last updated 2 years ago.

non-parametric-rdd parametric-rdd rdd

14.9 match 9 stars 6.30 score 44 scripts

mfaymon

spINAR:(Semi)Parametric Estimation and Bootstrapping of INAR Models

Semiparametric and parametric estimation of INAR models including a finite sample refinement (Faymonville et al. (2022) <doi:10.1007/s10260-022-00655-0>) for the semiparametric setting introduced in Drost et al. (2009) <doi:10.1111/j.1467-9868.2008.00687.x>, different procedures to bootstrap INAR data (Jentsch, C. and Weiß, C.H. (2017) <doi:10.3150/18-BEJ1057>) and flexible simulation of INAR data.

Maintained by Maxime Faymonville. Last updated 10 months ago.

bootstrapping count-data parametric-estimation penalization semiparametric-estimation simulation time-series validation

18.1 match 4 stars 5.20 score 7 scripts

rominsal

pspatreg:Spatial and Spatio-Temporal Semiparametric Regression Models with Spatial Lags

Estimation and inference of spatial and spatio-temporal semiparametric models including spatial or spatio-temporal non-parametric trends, parametric and non-parametric covariates and, possibly, a spatial lag for the dependent variable and temporal correlation in the noise. The spatio-temporal trend can be decomposed in ANOVA way including main and interaction functional terms. Use of SAP algorithm to estimate the spatial or spatio-temporal trend and non-parametric covariates. The methodology of these models can be found in next references Basile, R. et al. (2014), <doi:10.1016/j.jedc.2014.06.011>; Rodriguez-Alvarez, M.X. et al. (2015) <doi:10.1007/s11222-014-9464-2> and, particularly referred to the focus of the package, Minguez, R., Basile, R. and Durban, M. (2020) <doi:10.1007/s10260-019-00492-8>.

Maintained by Roman Minguez. Last updated 3 years ago.

14.5 match 12 stars 6.44 score 77 scripts

fk83

scoringRules:Scoring Rules for Parametric and Simulated Distribution Forecasts

Dictionary-like reference for computing scoring rules in a wide range of situations. Covers both parametric forecast distributions (such as mixtures of Gaussians) and distributions generated via simulation. Further details can be found in the package vignettes <doi:10.18637/jss.v090.i12>, <doi:10.18637/jss.v110.i08>.

Maintained by Fabian Krueger. Last updated 6 months ago.

openblas cpp

8.1 match 59 stars 11.33 score 408 scripts 13 dependents

lbelzile

mev:Modelling of Extreme Values

Various tools for the analysis of univariate, multivariate and functional extremes. Exact simulation from max-stable processes [Dombry, Engelke and Oesting (2016) <doi:10.1093/biomet/asw008>, R-Pareto processes for various parametric models, including Brown-Resnick (Wadsworth and Tawn, 2014, <doi:10.1093/biomet/ast042>) and Extremal Student (Thibaud and Opitz, 2015, <doi:10.1093/biomet/asv045>). Threshold selection methods, including Wadsworth (2016) <doi:10.1080/00401706.2014.998345>, and Northrop and Coleman (2014) <doi:10.1007/s10687-014-0183-z>. Multivariate extreme diagnostics. Estimation and likelihoods for univariate extremes, e.g., Coles (2001) <doi:10.1007/978-1-4471-3675-0>.

Maintained by Leo Belzile. Last updated 5 months ago.

extreme-value-statistics likelihood-functions max-stable simulation threshold-selection openblas cpp openmp

10.9 match 13 stars 8.23 score 94 scripts 4 dependents

r-forge

multcomp:Simultaneous Inference in General Parametric Models

Simultaneous tests and confidence intervals for general linear hypotheses in parametric models, including linear, generalized linear, linear mixed effects, and survival models. The package includes demos reproducing analyzes presented in the book "Multiple Comparisons Using R" (Bretz, Hothorn, Westfall, 2010, CRC Press).

Maintained by Torsten Hothorn. Last updated 2 months ago.

6.5 match 13.49 score 7.5k scripts 366 dependents

jamesliley

OptHoldoutSize:Estimation of Optimal Size for a Holdout Set for Updating a Predictive Score

Predictive scores must be updated with care, because actions taken on the basis of existing risk scores causes bias in risk estimates from the updated score. A holdout set is a straightforward way to manage this problem: a proportion of the population is 'held-out' from computation of the previous risk score. This package provides tools to estimate a size for this holdout set and associated errors. Comprehensive vignettes are included. Please see: Haidar-Wehbe S, Emerson SR, Aslett LJM, Liley J (2022) <arXiv:2202.06374> for details of methods.

Maintained by James Liley. Last updated 3 years ago.

27.6 match 3.18 score 10 scripts

r-forge

distrMod:Object Oriented Implementation of Probability Models

Implements S4 classes for probability models based on packages 'distr' and 'distrEx'.

Maintained by Peter Ruckdeschel. Last updated 2 months ago.

12.8 match 6.71 score 139 scripts 6 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

7.5 match 20 stars 11.34 score 1.4k scripts 56 dependents

hongyuanjia

eplusr:A Toolkit for Using Whole Building Simulation Program 'EnergyPlus'

A rich toolkit of using the whole building simulation program 'EnergyPlus'(<https://energyplus.net>), which enables programmatic navigation, modification of 'EnergyPlus' models and makes it less painful to do parametric simulations and analysis.

Maintained by Hongyuan Jia. Last updated 8 months ago.

energy-simulation energyplus energyplus-models eplus epw idd idf parametric-simulation r6 simulation

11.5 match 72 stars 7.20 score 91 scripts 4 dependents

causalinference

gfoRmula:Parametric G-Formula

Implements the non-iterative conditional expectation (NICE) algorithm of the g-formula algorithm (Robins (1986) <doi:10.1016/0270-0255(86)90088-6>, Hernán and Robins (2024, ISBN:9781420076165)). The g-formula can estimate an outcome's counterfactual mean or risk under hypothetical treatment strategies (interventions) when there is sufficient information on time-varying treatments and confounders. This package can be used for discrete or continuous time-varying treatments and for failure time outcomes or continuous/binary end of follow-up outcomes. The package can handle a random measurement/visit process and a priori knowledge of the data structure, as well as censoring (e.g., by loss to follow-up) and two options for handling competing events for failure time outcomes. Interventions can be flexibly specified, both as interventions on a single treatment or as joint interventions on multiple treatments. See McGrath et al. (2020) <doi:10.1016/j.patter.2020.100008> for a guide on how to use the package.

Maintained by Sean McGrath. Last updated 27 days ago.

10.1 match 165 stars 8.18 score 132 scripts

ncchung

jackstraw:Statistical Inference for Unsupervised Learning

Test for association between the observed data and their estimated latent variables. The jackstraw package provides a resampling strategy and testing scheme to estimate statistical significance of association between the observed data and their latent variables. Depending on the data type and the analysis aim, the latent variables may be estimated by principal component analysis (PCA), factor analysis (FA), K-means clustering, and related unsupervised learning algorithms. The jackstraw methods learn over-fitting characteristics inherent in this circular analysis, where the observed data are used to estimate the latent variables and used again to test against that estimated latent variables. When latent variables are estimated by PCA, the jackstraw enables statistical testing for association between observed variables and latent variables, as estimated by low-dimensional principal components (PCs). This essentially leads to identifying variables that are significantly associated with PCs. Similarly, unsupervised clustering, such as K-means clustering, partition around medoids (PAM), and others, finds coherent groups in high-dimensional data. The jackstraw estimates statistical significance of cluster membership, by testing association between data and cluster centers. Clustering membership can be improved by using the resulting jackstraw p-values and posterior inclusion probabilities (PIPs), with an application to unsupervised evaluation of cell identities in single cell RNA-seq (scRNA-seq).

Maintained by Neo Christopher Chung. Last updated 3 months ago.

clustering k-means machine-learning pca statistics unsupervised

15.4 match 16 stars 5.29 score 35 scripts

catherineschramm

KSPM:Kernel Semi-Parametric Models

To fit the kernel semi-parametric model and its extensions. It allows multiple kernels and unlimited interactions in the same model. Coefficients are estimated by maximizing a penalized log-likelihood; penalization terms and hyperparameters are estimated by minimizing leave-one-out error. It includes predictions with confidence/prediction intervals, statistical tests for the significance of each kernel, a procedure for variable selection and graphical tools for diagnostics and interpretation of covariate effects. Currently it is implemented for continuous dependent variables. The package is based on the paper of Liu et al. (2007), <doi:10.1111/j.1541-0420.2007.00799.x>.

Maintained by Catherine Schramm. Last updated 5 years ago.

35.2 match 2.26 score 18 scripts

mattmar

rasterdiv:Diversity Indices for Numerical Matrices

Provides methods to calculate diversity indices on numerical matrices based on information theory, as described in Rocchini, Marcantonio and Ricotta (2017) <doi:10.1016/j.ecolind.2016.07.039>, and Rocchini et al. (2021) <doi:10.1101/2021.01.23.427872>.

Maintained by Matteo Marcantonio. Last updated 20 days ago.

10.2 match 15 stars 7.65 score 44 scripts 1 dependents

andrewcparnell

Bchron:Radiocarbon Dating, Age-Depth Modelling, Relative Sea Level Rate Estimation, and Non-Parametric Phase Modelling

Enables quick calibration of radiocarbon dates under various calibration curves (including user generated ones); age-depth modelling as per the algorithm of Haslett and Parnell (2008) <DOI:10.1111/j.1467-9876.2008.00623.x>; Relative sea level rate estimation incorporating time uncertainty in polynomial regression models (Parnell and Gehrels 2015) <DOI:10.1002/9781118452547.ch32>; non-parametric phase modelling via Gaussian mixtures as a means to determine the activity of a site (and as an alternative to the Oxcal function SUM; currently unpublished), and reverse calibration of dates from calibrated into un-calibrated years (also unpublished).

Maintained by Andrew Parnell. Last updated 2 years ago.

9.6 match 36 stars 8.09 score 176 scripts 1 dependents

fercol

paramDemo:Parametric and Non-Parametric Demographic Functions and Applications

Calculate parametric mortality and Fertility models, following packages 'BaSTA' in Colchero, Jones and Rebke (2012) <doi:10.1111/j.2041-210X.2012.00186.x> and 'BaFTA' <https://github.com/fercol/BaFTA>, summary statistics (e.g. ageing rates, life expectancy, lifespan equality, etc.), life table and product limit estimators from census data.

Maintained by Fernando Colchero. Last updated 2 months ago.

22.3 match 1 stars 3.22 score 11 scripts

apjacobson

parmsurvfit:Parametric Models for Survival Data

Executes simple parametric models for right-censored survival data. Functionality emulates capabilities in 'Minitab', including fitting right-censored data, assessing fit, plotting survival functions, and summary statistics and probabilities.

Maintained by Ashley Jacobson. Last updated 6 years ago.

16.2 match 3 stars 4.41 score 17 scripts

mfasiolo

mgcViz:Visualisations for Generalized Additive Models

Extension of the 'mgcv' package, providing visual tools for Generalized Additive Models that exploit the additive structure of such models, scale to large data sets and can be used in conjunction with a wide range of response distributions. The focus is providing visual methods for better understanding the model output and for aiding model checking and development beyond simple exponential family regression. The graphical framework is based on the layering system provided by 'ggplot2'.

Maintained by Matteo Fasiolo. Last updated 7 months ago.

7.5 match 77 stars 9.38 score 1000 scripts

julianfaraway

faraway:Datasets and Functions for Books by Julian Faraway

Books are "Linear Models with R" published 1st Ed. August 2004, 2nd Ed. July 2014, 3rd Ed. February 2025 by CRC press, ISBN 9781439887332, and "Extending the Linear Model with R" published by CRC press in 1st Ed. December 2005 and 2nd Ed. March 2016, ISBN 9781584884248 and "Practical Regression and ANOVA in R" contributed documentation on CRAN (now very dated).

Maintained by Julian Faraway. Last updated 1 months ago.

data

7.2 match 29 stars 9.43 score 1.7k scripts 1 dependents

lem-usp

evolqg:Evolutionary Quantitative Genetics

Provides functions for covariance matrix comparisons, estimation of repeatabilities in measurements and matrices, and general evolutionary quantitative genetics tools. Melo D, Garcia G, Hubbe A, Assis A P, Marroig G. (2016) <doi:10.12688/f1000research.7082.3>.

Maintained by Diogo Melo. Last updated 11 months ago.

openblas cpp

10.7 match 10 stars 6.26 score 114 scripts

mclements

rstpm2:Smooth Survival Models, Including Generalized Survival Models

R implementation of generalized survival models (GSMs), smooth accelerated failure time (AFT) models and Markov multi-state models. For the GSMs, g(S(t|x))=eta(t,x) for a link function g, survival S at time t with covariates x and a linear predictor eta(t,x). The main assumption is that the time effect(s) are smooth <doi:10.1177/0962280216664760>. For fully parametric models with natural splines, this re-implements Stata's 'stpm2' function, which are flexible parametric survival models developed by Royston and colleagues. We have extended the parametric models to include any smooth parametric smoothers for time. We have also extended the model to include any smooth penalized smoothers from the 'mgcv' package, using penalized likelihood. These models include left truncation, right censoring, interval censoring, gamma frailties and normal random effects <doi:10.1002/sim.7451>, and copulas. For the smooth AFTs, S(t|x) = S_0(t*eta(t,x)), where the baseline survival function S_0(t)=exp(-exp(eta_0(t))) is modelled for natural splines for eta_0, and the time-dependent cumulative acceleration factor eta(t,x)=\int_0^t exp(eta_1(u,x)) du for log acceleration factor eta_1(u,x). The Markov multi-state models allow for a range of models with smooth transitions to predict transition probabilities, length of stay, utilities and costs, with differences, ratios and standardisation.

Maintained by Mark Clements. Last updated 5 months ago.

fortran openblas cpp

6.1 match 28 stars 11.01 score 137 scripts 50 dependents

ajsims1704

rdecision:Decision Analytic Modelling in Health Economics

Classes and functions for modelling health care interventions using decision trees and semi-Markov models. Mechanisms are provided for associating an uncertainty distribution with each source variable and for ensuring transparency of the mathematical relationships between variables. The package terminology follows Briggs "Decision Modelling for Health Economic Evaluation" (2006, ISBN:978-0-19-852662-9).

Maintained by Andrew Sims. Last updated 1 months ago.

9.8 match 3 stars 6.46 score 22 scripts

kristyrobledo

VarReg:Semi-Parametric Variance Regression

Methods for fitting semi-parametric mean and variance models, with normal or censored data. Extended to allow a regression in the location, scale and shape parameters, and further for multiple regression in each.

Maintained by Kristy Robledo. Last updated 2 years ago.

14.1 match 1 stars 4.46 score 29 scripts

guillaumepressiat

pmeasyr:Donnees PMSI avec R

Import de donnees PMSI. Gestion des archives. Formats depuis 2011. Connexion et interface avec une db. requetr. Valorisation des rsa, des rapss.

Maintained by Guillaume Pressiat. Last updated 13 days ago.

had mco pmsi psy ssr

9.2 match 20 stars 6.76 score 53 scripts

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

10.7 match 1 stars 5.74 score 140 scripts 11 dependents

tsmodels

tsdistributions:Location Scale Standardized Distributions

Location-Scale based distributions parameterized in terms of mean, standard deviation, skew and shape parameters and estimation using automatic differentiation. Distributions include the Normal, Student and GED as well as their skewed variants ('Fernandez and Steel'), the 'Johnson SU', and the Generalized Hyperbolic. Also included is the semi-parametric piece wise distribution ('spd') with Pareto tails and kernel interior.

Maintained by Alexios Galanos. Last updated 4 months ago.

distributions finance probability-distribution probability-distributions statistical-distributions timeseries cpp

9.2 match 4 stars 6.66 score 19 scripts 2 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.

10.2 match 3 stars 5.96 score 34 scripts 1 dependents

jrdnmdhl

flexsurvcure:Flexible Parametric Cure Models

Flexible parametric mixture and non-mixture cure models for time-to-event data.

Maintained by Jordan Amdahl. Last updated 30 days ago.

8.8 match 16 stars 6.64 score 20 scripts 2 dependents

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 13 days ago.

3.6 match 54 stars 16.15 score 4.5k scripts 153 dependents

winvector

wrapr:Wrap R Tools for Debugging and Parametric Programming

Tools for writing and debugging R code. Provides: '%.>%' dot-pipe (an 'S3' configurable pipe), unpack/to (R style multiple assignment/return), 'build_frame()'/'draw_frame()' ('data.frame' example tools), 'qc()' (quoting concatenate), ':=' (named map builder), 'let()' (converts non-standard evaluation interfaces to parametric standard evaluation interfaces, inspired by 'gtools::strmacro()' and 'base::bquote()'), and more.

Maintained by John Mount. Last updated 2 years ago.

5.1 match 137 stars 11.11 score 390 scripts 12 dependents

tpetzoldt

growthrates:Estimate Growth Rates from Experimental Data

A collection of methods to determine growth rates from experimental data, in particular from batch experiments and plate reader trials.

Maintained by Thomas Petzoldt. Last updated 1 years ago.

7.3 match 27 stars 7.52 score 102 scripts

borisberanger

ExtremalDep:Extremal Dependence Models

A set of procedures for parametric and non-parametric modelling of the dependence structure of multivariate extreme-values is provided. The statistical inference is performed with non-parametric estimators, likelihood-based estimators and Bayesian techniques. It adapts the methodologies of Beranger and Padoan (2015) <doi:10.48550/arXiv.1508.05561>, Marcon et al. (2016) <doi:10.1214/16-EJS1162>, Marcon et al. (2017) <doi:10.1002/sta4.145>, Marcon et al. (2017) <doi:10.1016/j.jspi.2016.10.004> and Beranger et al. (2021) <doi:10.1007/s10687-019-00364-0>. This package also allows for the modelling of spatial extremes using flexible max-stable processes. It provides simulation algorithms and fitting procedures relying on the Stephenson-Tawn likelihood as per Beranger at al. (2021) <doi:10.1007/s10687-020-00376-1>.

Maintained by Simone Padoan. Last updated 3 months ago.

openblas

16.7 match 3.30 score 1 scripts

langcog

tidyboot:Tidyverse-Compatible Bootstrapping

Compute arbitrary non-parametric bootstrap statistics on data in tidy data frames.

Maintained by Mika Braginsky. Last updated 4 years ago.

9.2 match 21 stars 5.96 score 438 scripts

alan-turing-institute

PosteriorBootstrap:Non-Parametric Sampling with Parallel Monte Carlo

An implementation of a non-parametric statistical model using a parallelised Monte Carlo sampling scheme. The method implemented in this package allows non-parametric inference to be regularized for small sample sizes, while also being more accurate than approximations such as variational Bayes. The concentration parameter is an effective sample size parameter, determining the faith we have in the model versus the data. When the concentration is low, the samples are close to the exact Bayesian logistic regression method; when the concentration is high, the samples are close to the simplified variational Bayes logistic regression. The method is described in full in the paper Lyddon, Walker, and Holmes (2018), "Nonparametric learning from Bayesian models with randomized objective functions" <arXiv:1806.11544>.

Maintained by James Robinson. Last updated 2 years ago.

hacktoberfest hut23 hut23-306

11.5 match 4 stars 4.78 score

statdivlab

corncob:Count Regression for Correlated Observations with the Beta-Binomial

Statistical modeling for correlated count data using the beta-binomial distribution, described in Martin et al. (2020) <doi:10.1214/19-AOAS1283>. It allows for both mean and overdispersion covariates.

Maintained by Amy D Willis. Last updated 6 months ago.

5.5 match 105 stars 9.64 score 248 scripts 1 dependents

fbertran

plsRglm:Partial Least Squares Regression for Generalized Linear Models

Provides (weighted) Partial least squares Regression for generalized linear models and repeated k-fold cross-validation of such models using various criteria <arXiv:1810.01005>. It allows for missing data in the explanatory variables. Bootstrap confidence intervals constructions are also available.

Maintained by Frederic Bertrand. Last updated 2 years ago.

6.9 match 16 stars 7.75 score 103 scripts 5 dependents

philchalmers

mirt:Multidimensional Item Response Theory

Analysis of discrete response data using unidimensional and multidimensional item analysis models under the Item Response Theory paradigm (Chalmers (2012) <doi:10.18637/jss.v048.i06>). Exploratory and confirmatory item factor analysis models are estimated with quadrature (EM) or stochastic (MHRM) methods. Confirmatory bi-factor and two-tier models are available for modeling item testlets using dimension reduction EM algorithms, while multiple group analyses and mixed effects designs are included for detecting differential item, bundle, and test functioning, and for modeling item and person covariates. Finally, latent class models such as the DINA, DINO, multidimensional latent class, mixture IRT models, and zero-inflated response models are supported, as well as a wide family of probabilistic unfolding models.

Maintained by Phil Chalmers. Last updated 11 days ago.

irt mirt openblas cpp openmp

3.5 match 210 stars 14.98 score 2.5k scripts 40 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

21.4 match 2.41 score 26 scripts

kornl

gMCP:Graph Based Multiple Comparison Procedures

Functions and a graphical user interface for graphical described multiple test procedures.

Maintained by Kornelius Rohmeyer. Last updated 12 months ago.

openjdk

7.0 match 10 stars 7.28 score 105 scripts 2 dependents

spatstat

spatstat.model:Parametric Statistical Modelling and Inference for the 'spatstat' Family

Functionality for parametric statistical modelling and inference for spatial data, mainly spatial point patterns, in the 'spatstat' family of packages. (Excludes analysis of spatial data on a linear network, which is covered by the separate package 'spatstat.linnet'.) Supports parametric modelling, formal statistical inference, and model validation. Parametric models include Poisson point processes, Cox point processes, Neyman-Scott cluster processes, Gibbs point processes and determinantal point processes. Models can be fitted to data using maximum likelihood, maximum pseudolikelihood, maximum composite likelihood and the method of minimum contrast. Fitted models can be simulated and predicted. Formal inference includes hypothesis tests (quadrat counting tests, Cressie-Read tests, Clark-Evans test, Berman test, Diggle-Cressie-Loosmore-Ford test, scan test, studentised permutation test, segregation test, ANOVA tests of fitted models, adjusted composite likelihood ratio test, envelope tests, Dao-Genton test, balanced independent two-stage test), confidence intervals for parameters, and prediction intervals for point counts. Model validation techniques include leverage, influence, partial residuals, added variable plots, diagnostic plots, pseudoscore residual plots, model compensators and Q-Q plots.

Maintained by Adrian Baddeley. Last updated 8 days ago.

analysis-of-variance cluster-process confidence-intervals cox-process determinantal-point-processes gibbs-process influence leverage model-diagnostics neyman-scott parameter-estimation poisson-process spatial-analysis spatial-modelling spatial-point-processes statistical-inference

5.6 match 5 stars 9.09 score 6 scripts 46 dependents

venelin

PCMBase:Simulation and Likelihood Calculation of Phylogenetic Comparative Models

Phylogenetic comparative methods represent models of continuous trait data associated with the tips of a phylogenetic tree. Examples of such models are Gaussian continuous time branching stochastic processes such as Brownian motion (BM) and Ornstein-Uhlenbeck (OU) processes, which regard the data at the tips of the tree as an observed (final) state of a Markov process starting from an initial state at the root and evolving along the branches of the tree. The PCMBase R package provides a general framework for manipulating such models. This framework consists of an application programming interface for specifying data and model parameters, and efficient algorithms for simulating trait evolution under a model and calculating the likelihood of model parameters for an assumed model and trait data. The package implements a growing collection of models, which currently includes BM, OU, BM/OU with jumps, two-speed OU as well as mixed Gaussian models, in which different types of the above models can be associated with different branches of the tree. The PCMBase package is limited to trait-simulation and likelihood calculation of (mixed) Gaussian phylogenetic models. The PCMFit package provides functionality for inference of these models to tree and trait data. The package web-site <https://venelin.github.io/PCMBase/> provides access to the documentation and other resources.

Maintained by Venelin Mitov. Last updated 10 months ago.

6.5 match 6 stars 7.56 score 85 scripts 3 dependents

rwehrens

ptw:Parametric Time Warping

Parametric Time Warping aligns patterns, i.e. it aims to put corresponding features at the same locations. The algorithm searches for an optimal polynomial describing the warping. It is possible to align one sample to a reference, several samples to the same reference, or several samples to several references. One can choose between calculating individual warpings, or one global warping for a set of samples and one reference. Two optimization criteria are implemented: RMS (Root Mean Square error) and WCC (Weighted Cross Correlation). Both warping of peak profiles and of peak lists are supported. A vignette for the latter is contained in the inst/doc directory of the source package - the vignette source can be found on the package github site.

Maintained by Ron Wehrens. Last updated 3 years ago.

7.8 match 8 stars 6.31 score 57 scripts 10 dependents

bioc

RCM:Fit row-column association models with the negative binomial distribution for the microbiome

Combine ideas of log-linear analysis of contingency table, flexible response function estimation and empirical Bayes dispersion estimation for explorative visualization of microbiome datasets. The package includes unconstrained as well as constrained analysis. In addition, diagnostic plot to detect lack of fit are available.

Maintained by Stijn Hawinkel. Last updated 5 months ago.

metagenomics dimensionreduction microbiome visualization ordination phyloseq rcm

7.1 match 16 stars 6.90 score 25 scripts

krlmlr

bindr:Parametrized Active Bindings

Provides a simple interface for creating active bindings where the bound function accepts additional arguments.

Maintained by Kirill Müller. Last updated 3 months ago.

7.0 match 29 stars 6.93 score 16 scripts 1 dependents

sebastien-plutniak

archeoViz:Visualisation, Exploration, and Web Communication of Archaeological Spatial Data

An R 'Shiny' application for visual and statistical exploration and web communication of archaeological spatial data, either remains or sites. It offers interactive 3D and 2D visualisations (cross sections and maps of remains, timeline of the work made in a site) which can be exported in SVG and HTML formats. It performs simple spatial statistics (convex hull, regression surfaces, 2D kernel density estimation) and allows exporting data to other online applications for more complex methods. 'archeoViz' can be used offline locally or deployed on a server, either with interactive input of data or with a static data set. Example is provided at <https://analytics.huma-num.fr/archeoviz/en>.

Maintained by Sebastien Plutniak. Last updated 1 months ago.

archaeology archeology data-visualization

6.6 match 19 stars 7.23 score 6 scripts

bozenne

BuyseTest:Generalized Pairwise Comparisons

Implementation of the Generalized Pairwise Comparisons (GPC) as defined in Buyse (2010) <doi:10.1002/sim.3923> for complete observations, and extended in Peron (2018) <doi:10.1177/0962280216658320> to deal with right-censoring. GPC compare two groups of observations (intervention vs. control group) regarding several prioritized endpoints to estimate the probability that a random observation drawn from one group performs better/worse/equivalently than a random observation drawn from the other group. Summary statistics such as the net treatment benefit, win ratio, or win odds are then deduced from these probabilities. Confidence intervals and p-values are obtained based on asymptotic results (Ozenne 2021 <doi:10.1177/09622802211037067>), non-parametric bootstrap, or permutations. The software enables the use of thresholds of minimal importance difference, stratification, non-prioritized endpoints (O Brien test), and can handle right-censoring and competing-risks.

Maintained by Brice Ozenne. Last updated 4 days ago.

generalized-pairwise-comparisons non-parametric statistics cpp

8.0 match 5 stars 5.91 score 90 scripts

alexiosg

spd:Semi Parametric Distribution

The Semi Parametric Piecewise Distribution blends the Generalized Pareto Distribution for the tails with a kernel based interior.

Maintained by Alexios Ghalanos. Last updated 10 years ago.

11.2 match 4.21 score 9 scripts 16 dependents

biodiverse

unmarked:Models for Data from Unmarked Animals

Fits hierarchical models of animal abundance and occurrence to data collected using survey methods such as point counts, site occupancy sampling, distance sampling, removal sampling, and double observer sampling. Parameters governing the state and observation processes can be modeled as functions of covariates. References: Kellner et al. (2023) <doi:10.1111/2041-210X.14123>, Fiske and Chandler (2011) <doi:10.18637/jss.v043.i10>.

Maintained by Ken Kellner. Last updated 1 days ago.

openblas cpp openmp

3.6 match 4 stars 13.03 score 652 scripts 12 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

6.1 match 15 stars 7.52 score 55 scripts 3 dependents

kosukeimai

MatchIt:Nonparametric Preprocessing for Parametric Causal Inference

Selects matched samples of the original treated and control groups with similar covariate distributions -- can be used to match exactly on covariates, to match on propensity scores, or perform a variety of other matching procedures. The package also implements a series of recommendations offered in Ho, Imai, King, and Stuart (2007) <DOI:10.1093/pan/mpl013>. (The 'gurobi' package, which is not on CRAN, is optional and comes with an installation of the Gurobi Optimizer, available at <https://www.gurobi.com>.)

Maintained by Noah Greifer. Last updated 2 days ago.

cpp openmp

3.0 match 220 stars 15.03 score 2.4k scripts 21 dependents

alexisderumigny

MMDCopula:Robust Estimation of Copulas by Maximum Mean Discrepancy

Provides functions for the robust estimation of parametric families of copulas using minimization of the Maximum Mean Discrepancy, following the article Alquier, Chérief-Abdellatif, Derumigny and Fermanian (2022) <doi:10.1080/01621459.2021.2024836>.

Maintained by Alexis Derumigny. Last updated 3 years ago.

copula-models copulas mmd r-pkg

10.1 match 5 stars 4.40 score 3 scripts

yoshidk6

survParamSim:Parametric Survival Simulation with Parameter Uncertainty

Perform survival simulation with parametric survival model generated from 'survreg' function in 'survival' package. In each simulation coefficients are resampled from variance-covariance matrix of parameter estimates to capture uncertainty in model parameters. Prediction intervals of Kaplan-Meier estimates and hazard ratio of treatment effect can be further calculated using simulated survival data.

Maintained by Kenta Yoshida. Last updated 2 years ago.

10.5 match 3 stars 4.18 score 4 scripts

bquast

rddtools:Toolbox for Regression Discontinuity Design ('RDD')

Set of functions for Regression Discontinuity Design ('RDD'), for data visualisation, estimation and testing.

Maintained by Bastiaan Quast. Last updated 1 years ago.

6.4 match 11 stars 6.65 score 203 scripts

zichongli5

PRIMAL:Parametric Simplex Method for Sparse Learning

Implements a unified framework of parametric simplex method for a variety of sparse learning problems (e.g., Dantzig selector (for linear regression), sparse quantile regression, sparse support vector machines, and compressive sensing) combined with efficient hyper-parameter selection strategies. The core algorithm is implemented in C++ with Eigen3 support for portable high performance linear algebra. For more details about parametric simplex method, see Haotian Pang (2017) <https://papers.nips.cc/paper/6623-parametric-simplex-method-for-sparse-learning.pdf>.

Maintained by Zichong Li. Last updated 5 years ago.

cpp

14.2 match 3.00 score 3 scripts

jrhub

spinBayes:Semi-Parametric Gene-Environment Interaction via Bayesian Variable Selection

Many complex diseases are known to be affected by the interactions between genetic variants and environmental exposures beyond the main genetic and environmental effects. Existing Bayesian methods for gene-environment (G×E) interaction studies are challenged by the high-dimensional nature of the study and the complexity of environmental influences. We have developed a novel and powerful semi-parametric Bayesian variable selection method that can accommodate linear and nonlinear G×E interactions simultaneously (Ren et al. (2020) <doi:10.1002/sim.8434>). Furthermore, the proposed method can conduct structural identification by distinguishing nonlinear interactions from main effects only case within Bayesian framework. Spike-and-slab priors are incorporated on both individual and group level to shrink coefficients corresponding to irrelevant main and interaction effects to zero exactly. The Markov chain Monte Carlo algorithms of the proposed and alternative methods are efficiently implemented in C++.

Maintained by Jie Ren. Last updated 1 months ago.

bayesian-variable-selection gene-environment-interactions high-dimensional-data semi-parametric-modeling openblas cpp openmp

13.3 match 1 stars 3.18 score 3 scripts

angelgar

voxel:Mass-Univariate Voxelwise Analysis of Medical Imaging Data

Functions for the mass-univariate voxelwise analysis of medical imaging data that follows the NIfTI <http://nifti.nimh.nih.gov> format.

Maintained by Angel Garcia de la Garza. Last updated 5 years ago.

8.6 match 9 stars 4.85 score 78 scripts

francescobartolucci

LMest:Generalized Latent Markov Models

Latent Markov models for longitudinal continuous and categorical data. See Bartolucci, Pandolfi, Pennoni (2017)<doi:10.18637/jss.v081.i04>.

Maintained by Francesco Bartolucci. Last updated 2 months ago.

fortran openblas

9.1 match 3 stars 4.58 score 42 scripts

smn74

MANOVA.RM:Resampling-Based Analysis of Multivariate Data and Repeated Measures Designs

Implemented are various tests for semi-parametric repeated measures and general MANOVA designs that do neither assume multivariate normality nor covariance homogeneity, i.e., the procedures are applicable for a wide range of general multivariate factorial designs. In addition to asymptotic inference methods, novel bootstrap and permutation approaches are implemented as well. These provide more accurate results in case of small to moderate sample sizes. Furthermore, post-hoc comparisons are provided for the multivariate analyses. Friedrich, S., Konietschke, F. and Pauly, M. (2019) <doi:10.32614/RJ-2019-051>.

Maintained by Sarah Friedrich. Last updated 1 months ago.

multivariate-data permutation repeated-measures resampling

8.9 match 11 stars 4.63 score 39 scripts

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 30 days ago.

fortran openblas cpp openmp

3.7 match 40 stars 11.24 score 220 scripts 13 dependents

teppeiyamamoto

mediation:Causal Mediation Analysis

We implement parametric and non parametric mediation analysis. This package performs the methods and suggestions in Imai, Keele and Yamamoto (2010) <DOI:10.1214/10-STS321>, Imai, Keele and Tingley (2010) <DOI:10.1037/a0020761>, Imai, Tingley and Yamamoto (2013) <DOI:10.1111/j.1467-985X.2012.01032.x>, Imai and Yamamoto (2013) <DOI:10.1093/pan/mps040> and Yamamoto (2013) <http://web.mit.edu/teppei/www/research/IVmediate.pdf>. In addition to the estimation of causal mediation effects, the software also allows researchers to conduct sensitivity analysis for certain parametric models.

Maintained by Teppei Yamamoto. Last updated 6 years ago.

3.9 match 10.48 score 896 scripts 11 dependents

mrc-ide

EpiEstim:Estimate Time Varying Reproduction Numbers from Epidemic Curves

Tools to quantify transmissibility throughout an epidemic from the analysis of time series of incidence as described in Cori et al. (2013) <doi:10.1093/aje/kwt133> and Wallinga and Teunis (2004) <doi:10.1093/aje/kwh255>.

Maintained by Anne Cori. Last updated 7 months ago.

3.4 match 95 stars 12.00 score 1.0k scripts 7 dependents

angabrio

missingHE:Missing Outcome Data in Health Economic Evaluation

Contains a suite of functions for health economic evaluations with missing outcome data. The package can fit different types of statistical models under a fully Bayesian approach using the software 'JAGS' (which should be installed locally and which is loaded in 'missingHE' via the 'R' package 'R2jags'). Three classes of models can be fitted under a variety of missing data assumptions: selection models, pattern mixture models and hurdle models. In addition to model fitting, 'missingHE' provides a set of specialised functions to assess model convergence and fit, and to summarise the statistical and economic results using different types of measures and graphs. The methods implemented are described in Mason (2018) <doi:10.1002/hec.3793>, Molenberghs (2000) <doi:10.1007/978-1-4419-0300-6_18> and Gabrio (2019) <doi:10.1002/sim.8045>.

Maintained by Andrea Gabrio. Last updated 2 years ago.

cost-effectiveness-analysis health-economic-evaluation individual-level-data jags missing-data parametric-modelling sensitivity-analysis cpp

7.5 match 5 stars 5.38 score 24 scripts

moggces

Rcurvep:Concentration-Response Data Analysis using Curvep

An R interface for processing concentration-response datasets using Curvep, a response noise filtering algorithm. The algorithm was described in the publications (Sedykh A et al. (2011) <doi:10.1289/ehp.1002476> and Sedykh A (2016) <doi:10.1007/978-1-4939-6346-1_14>). Other parametric fitting approaches (e.g., Hill equation) are also adopted for ease of comparison. 3-parameter Hill equation from 'tcpl' package (Filer D et al., <doi:10.1093/bioinformatics/btw680>) and 4-parameter Hill equation from Curve Class2 approach (Wang Y et al., <doi:10.2174/1875397301004010057>) are available. Also, methods for calculating the confidence interval around the activity metrics are also provided. The methods are based on the bootstrap approach to simulate the datasets (Hsieh J-H et al. <doi:10.1093/toxsci/kfy258>). The simulated datasets can be used to derive the baseline noise threshold in an assay endpoint. This threshold is critical in the toxicological studies to derive the point-of-departure (POD).

Maintained by Jui-Hua Hsieh. Last updated 1 years ago.

openjdk

8.5 match 3 stars 4.71 score 17 scripts

ewenharrison

finalfit:Quickly Create Elegant Regression Results Tables and Plots when Modelling

Generate regression results tables and plots in final format for publication. Explore models and export directly to PDF and 'Word' using 'RMarkdown'.

Maintained by Ewen Harrison. Last updated 7 months ago.

3.5 match 270 stars 11.43 score 1.0k scripts

federico-rotolo

parfm:Parametric Frailty Models

Fits Parametric Frailty Models by maximum marginal likelihood. Possible baseline hazards: exponential, Weibull, inverse Weibull (Fréchet), Gompertz, lognormal, log-skew-normal, and loglogistic. Possible Frailty distributions: gamma, positive stable, inverse Gaussian and lognormal.

Maintained by Federico Rotolo. Last updated 2 years ago.

14.6 match 2.73 score 36 scripts 1 dependents

bioc

SPsimSeq:Semi-parametric simulation tool for bulk and single-cell RNA sequencing data

SPsimSeq uses a specially designed exponential family for density estimation to constructs the distribution of gene expression levels from a given real RNA sequencing data (single-cell or bulk), and subsequently simulates a new dataset from the estimated marginal distributions using Gaussian-copulas to retain the dependence between genes. It allows simulation of multiple groups and batches with any required sample size and library size.

Maintained by Joris Meys. Last updated 5 months ago.

geneexpression rnaseq singlecell sequencing dnaseq

5.5 match 10 stars 7.14 score 29 scripts 1 dependents

nepem-ufsc

metan:Multi Environment Trials Analysis

Performs stability analysis of multi-environment trial data using parametric and non-parametric methods. Parametric methods includes Additive Main Effects and Multiplicative Interaction (AMMI) analysis by Gauch (2013) <doi:10.2135/cropsci2013.04.0241>, Ecovalence by Wricke (1965), Genotype plus Genotype-Environment (GGE) biplot analysis by Yan & Kang (2003) <doi:10.1201/9781420040371>, geometric adaptability index by Mohammadi & Amri (2008) <doi:10.1007/s10681-007-9600-6>, joint regression analysis by Eberhart & Russel (1966) <doi:10.2135/cropsci1966.0011183X000600010011x>, genotypic confidence index by Annicchiarico (1992), Murakami & Cruz's (2004) method, power law residuals (POLAR) statistics by Doring et al. (2015) <doi:10.1016/j.fcr.2015.08.005>, scale-adjusted coefficient of variation by Doring & Reckling (2018) <doi:10.1016/j.eja.2018.06.007>, stability variance by Shukla (1972) <doi:10.1038/hdy.1972.87>, weighted average of absolute scores by Olivoto et al. (2019a) <doi:10.2134/agronj2019.03.0220>, and multi-trait stability index by Olivoto et al. (2019b) <doi:10.2134/agronj2019.03.0221>. Non-parametric methods includes superiority index by Lin & Binns (1988) <doi:10.4141/cjps88-018>, nonparametric measures of phenotypic stability by Huehn (1990) <doi:10.1007/BF00024241>, TOP third statistic by Fox et al. (1990) <doi:10.1007/BF00040364>. Functions for computing biometrical analysis such as path analysis, canonical correlation, partial correlation, clustering analysis, and tools for inspecting, manipulating, summarizing and plotting typical multi-environment trial data are also provided.

Maintained by Tiago Olivoto. Last updated 9 days ago.

4.1 match 2 stars 9.48 score 1.3k scripts 2 dependents

bioc

NPARC:Non-parametric analysis of response curves for thermal proteome profiling experiments

Perform non-parametric analysis of response curves as described by Childs, Bach, Franken et al. (2019): Non-parametric analysis of thermal proteome profiles reveals novel drug-binding proteins.

Maintained by Nils Kurzawa. Last updated 5 months ago.

software proteomics

7.1 match 5.35 score 37 scripts

ctmm-initiative

ctmm:Continuous-Time Movement Modeling

Functions for identifying, fitting, and applying continuous-space, continuous-time stochastic-process movement models to animal tracking data. The package is described in Calabrese et al (2016) <doi:10.1111/2041-210X.12559>, with models and methods based on those introduced and detailed in Fleming & Calabrese et al (2014) <doi:10.1086/675504>, Fleming et al (2014) <doi:10.1111/2041-210X.12176>, Fleming et al (2015) <doi:10.1103/PhysRevE.91.032107>, Fleming et al (2015) <doi:10.1890/14-2010.1>, Fleming et al (2016) <doi:10.1890/15-1607>, Péron & Fleming et al (2016) <doi:10.1186/s40462-016-0084-7>, Fleming & Calabrese (2017) <doi:10.1111/2041-210X.12673>, Péron et al (2017) <doi:10.1002/ecm.1260>, Fleming et al (2017) <doi:10.1016/j.ecoinf.2017.04.008>, Fleming et al (2018) <doi:10.1002/eap.1704>, Winner & Noonan et al (2018) <doi:10.1111/2041-210X.13027>, Fleming et al (2019) <doi:10.1111/2041-210X.13270>, Noonan & Fleming et al (2019) <doi:10.1186/s40462-019-0177-1>, Fleming et al (2020) <doi:10.1101/2020.06.12.130195>, Noonan et al (2021) <doi:10.1111/2041-210X.13597>, Fleming et al (2022) <doi:10.1111/2041-210X.13815>, Silva et al (2022) <doi:10.1111/2041-210X.13786>, Alston & Fleming et al (2023) <doi:10.1111/2041-210X.14025>.

Maintained by Christen H. Fleming. Last updated 2 months ago.

3.5 match 49 stars 10.57 score 534 scripts 4 dependents

wicaksh

saekernel:Small Area Estimation Non-Parametric Based Nadaraya-Watson Kernel

Propose an area-level, non-parametric regression estimator based on Nadaraya-Watson kernel on small area mean. Adopt a two-stage estimation approach proposed by Prasad and Rao (1990). Mean Squared Error (MSE) estimators are not readily available, so resampling method that called bootstrap is applied. This package are based on the model proposed in Two stage non-parametric approach for small area estimation by Pushpal Mukhopadhyay and Tapabrata Maiti(2004) <http://www.asasrms.org/Proceedings/y2004/files/Jsm2004-000737.pdf>.

Maintained by Wicak Surya Hasani. Last updated 4 years ago.

10.1 match 3.70 score 2 scripts

epiforecasts

EpiNow2:Estimate Real-Time Case Counts and Time-Varying Epidemiological Parameters

Estimates the time-varying reproduction number, rate of spread, and doubling time using a range of open-source tools (Abbott et al. (2020) <doi:10.12688/wellcomeopenres.16006.1>), and current best practices (Gostic et al. (2020) <doi:10.1101/2020.06.18.20134858>). It aims to help users avoid some of the limitations of naive implementations in a framework that is informed by community feedback and is actively supported.

Maintained by Sebastian Funk. Last updated 25 days ago.

backcalculation covid-19 gaussian-processes open-source reproduction-number stan cpp

3.1 match 120 stars 11.88 score 210 scripts

openpharma

graphicalMCP:Graphical Multiple Comparison Procedures

Multiple comparison procedures (MCPs) control the familywise error rate in clinical trials. Graphical MCPs include many commonly used procedures as special cases; see Bretz et al. (2011) <doi:10.1002/bimj.201000239>, Lu (2016) <doi:10.1002/sim.6985>, and Xi et al. (2017) <doi:10.1002/bimj.201600233>. This package is a low-dependency implementation of graphical MCPs which allow mixed types of tests. It also includes power simulations and visualization of graphical MCPs.

Maintained by Dong Xi. Last updated 4 months ago.

5.0 match 17 stars 7.35 score 18 scripts

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

5.6 match 6.58 score 24 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

10.2 match 8 stars 3.60 score 1 scripts

emvolz

treedater:Fast Molecular Clock Dating of Phylogenetic Trees with Rate Variation

Functions for estimating times of common ancestry and molecular clock rates of evolution using a variety of evolutionary models, parametric and nonparametric bootstrap confidence intervals, methods for detecting outlier lineages, root-to-tip regression, and a statistical test for selecting molecular clock models. The methods are described in Volz, E.M. and S.D.W. Frost (2017) <doi:10.1093/ve/vex025>.

Maintained by Erik Volz. Last updated 3 years ago.

5.3 match 24 stars 6.86 score 60 scripts

cran

sae:Small Area Estimation

Functions for small area estimation.

Maintained by Yolanda Marhuenda. Last updated 5 years ago.

6.6 match 6 stars 5.49 score 83 scripts 8 dependents

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

5.1 match 32 stars 7.00 score 103 scripts 1 dependents

matthias-studer

WeightedCluster:Clustering of Weighted Data

Clusters state sequences and weighted data. It provides an optimized weighted PAM algorithm as well as functions for aggregating replicated cases, computing cluster quality measures for a range of clustering solutions and plotting (fuzzy) clusters of state sequences. Parametric bootstraps methods to validate typology of sequences are also provided. Finally, it provides a fuzzy and crisp CLARA algorithm to cluster large database with sequence analysis.

Maintained by Matthias Studer. Last updated 3 months ago.

cpp

6.5 match 5.55 score 106 scripts 4 dependents

hongyuanjia

epluspar:Conduct Parametric Analysis on 'EnergyPlus' Models

A toolkit for conducting parametric analysis on 'EnergyPlus'(<https://energyplus.net>) models in R, including sensitivity analysis using Morris method and Bayesian calibration using using 'Stan'(<https://mc-stan.org>). References: Chong (2018) <doi:10.1016/j.enbuild.2018.06.028>.

Maintained by Hongyuan Jia. Last updated 12 months ago.

bayesian-calibration energyplus parametric sensitivity-analysis cpp

13.5 match 9 stars 2.65 score 4 scripts

diogoferrari

hdpGLM:Hierarchical Dirichlet Process Generalized Linear Models

Implementation of MCMC algorithms to estimate the Hierarchical Dirichlet Process Generalized Linear Model (hdpGLM) presented in the paper Ferrari (2020) Modeling Context-Dependent Latent Heterogeneity, Political Analysis <DOI:10.1017/pan.2019.13> and <doi:10.18637/jss.v107.i10>.

Maintained by Diogo Ferrari. Last updated 1 years ago.

dirichlet-process-mixtures hierarchical-clustering nonparametric nonparametricbayes npb semi-parametric openblas cpp

7.5 match 12 stars 4.78 score 5 scripts

bioc

flowMeans:Non-parametric Flow Cytometry Data Gating

Identifies cell populations in Flow Cytometry data using non-parametric clustering and segmented-regression-based change point detection. Note: R 2.11.0 or newer is required.

Maintained by Nima Aghaeepour. Last updated 5 months ago.

immunooncology flowcytometry cellbiology clustering

6.3 match 5.64 score 36 scripts 2 dependents

lme4

lme4:Linear Mixed-Effects Models using 'Eigen' and S4

Fit linear and generalized linear mixed-effects models. The models and their components are represented using S4 classes and methods. The core computational algorithms are implemented using the 'Eigen' C++ library for numerical linear algebra and 'RcppEigen' "glue".

Maintained by Ben Bolker. Last updated 3 days ago.

cpp

1.7 match 647 stars 20.69 score 35k scripts 1.5k dependents

mfasiolo

qgam:Smooth Additive Quantile Regression Models

Smooth additive quantile regression models, fitted using the methods of Fasiolo et al. (2020) <doi:10.1080/01621459.2020.1725521>. See Fasiolo at al. (2021) <doi:10.18637/jss.v100.i09> for an introduction to the package. Differently from 'quantreg', the smoothing parameters are estimated automatically by marginal loss minimization, while the regression coefficients are estimated using either PIRLS or Newton algorithm. The learning rate is determined so that the Bayesian credible intervals of the estimated effects have approximately the correct coverage. The main function is qgam() which is similar to gam() in 'mgcv', but fits non-parametric quantile regression models.

Maintained by Matteo Fasiolo. Last updated 5 days ago.

3.4 match 33 stars 10.13 score 133 scripts 15 dependents

sandrinepavoine

adiv:Analysis of Diversity

Functions, data sets and examples for the calculation of various indices of biodiversity including species, functional and phylogenetic diversity. Part of the indices are expressed in terms of equivalent numbers of species. The package also provides ways to partition biodiversity across spatial or temporal scales (alpha, beta, gamma diversities). In addition to the quantification of biodiversity, ordination approaches are available which rely on diversity indices and allow the detailed identification of species, functional or phylogenetic differences between communities.

Maintained by Sandrine Pavoine. Last updated 1 years ago.

14.9 match 1 stars 2.28 score 63 scripts

mlr-org

mlr3extralearners:Extra Learners For mlr3

Extra learners for use in mlr3.

Maintained by Sebastian Fischer. Last updated 4 months ago.

machine-learning mlr3

3.7 match 94 stars 9.16 score 474 scripts

dwarton

ecostats:Code and Data Accompanying the Eco-Stats Text (Warton 2022)

Functions and data supporting the Eco-Stats text (Warton, 2022, Springer), and solutions to exercises. Functions include tools for using simulation envelopes in diagnostic plots, and a function for diagnostic plots of multivariate linear models. Datasets mentioned in the package are included here (where not available elsewhere) and there is a vignette for each chapter of the text with solutions to exercises.

Maintained by David Warton. Last updated 1 years ago.

5.0 match 8 stars 6.58 score 53 scripts

bioc

wavClusteR:Sensitive and highly resolved identification of RNA-protein interaction sites in PAR-CLIP data

The package provides an integrated pipeline for the analysis of PAR-CLIP data. PAR-CLIP-induced transitions are first discriminated from sequencing errors, SNPs and additional non-experimental sources by a non- parametric mixture model. The protein binding sites (clusters) are then resolved at high resolution and cluster statistics are estimated using a rigorous Bayesian framework. Post-processing of the results, data export for UCSC genome browser visualization and motif search analysis are provided. In addition, the package allows to integrate RNA-Seq data to estimate the False Discovery Rate of cluster detection. Key functions support parallel multicore computing. Note: while wavClusteR was designed for PAR-CLIP data analysis, it can be applied to the analysis of other NGS data obtained from experimental procedures that induce nucleotide substitutions (e.g. BisSeq).

Maintained by Federico Comoglio. Last updated 5 months ago.

immunooncology sequencing technology ripseq rnaseq bayesian

7.0 match 4.60 score 3 scripts

stmcg

estmeansd:Estimating the Sample Mean and Standard Deviation from Commonly Reported Quantiles in Meta-Analysis

Implements the methods of McGrath et al. (2020) <doi:10.1177/0962280219889080> and Cai et al. (2021) <doi:10.1177/09622802211047348> for estimating the sample mean and standard deviation from commonly reported quantiles in meta-analysis. These methods can be applied to studies that report the sample median, sample size, and one or both of (i) the sample minimum and maximum values and (ii) the first and third quartiles. The corresponding standard error estimators described by McGrath et al. (2023) <doi:10.1177/09622802221139233> are also included.

Maintained by Sean McGrath. Last updated 1 years ago.

6.8 match 2 stars 4.70 score 58 scripts 2 dependents

glmmtmb

glmmTMB:Generalized Linear Mixed Models using Template Model Builder

Fit linear and generalized linear mixed models with various extensions, including zero-inflation. The models are fitted using maximum likelihood estimation via 'TMB' (Template Model Builder). Random effects are assumed to be Gaussian on the scale of the linear predictor and are integrated out using the Laplace approximation. Gradients are calculated using automatic differentiation.

Maintained by Mollie Brooks. Last updated 12 days ago.

cpp openmp

1.9 match 312 stars 16.77 score 3.7k scripts 24 dependents

spedygiorgio

mbbefd:Maxwell Boltzmann Bose Einstein Fermi Dirac Distribution and Destruction Rate Modelling

Distributions that are typically used for exposure rating in general insurance, in particular to price reinsurance contracts. The vignette shows code snippets to fit the distribution to empirical data. See, e.g., Bernegger (1997) <doi:10.2143/AST.27.1.563208> freely available on-line.

Maintained by Christophe Dutang. Last updated 22 days ago.

actuarial destruction-rate-modeling reinsurance cpp

4.4 match 15 stars 7.05 score 99 scripts

maiermarco

DirichletReg:Dirichlet Regression

Implements Dirichlet regression models.

Maintained by Marco Johannes Maier. Last updated 4 years ago.

dirichlet-distribution dirichlet-regression

3.5 match 13 stars 8.70 score 222 scripts 8 dependents

baeyc

varTestnlme:Variance Components Testing for Linear and Nonlinear Mixed Effects Models

An implementation of the Likelihood ratio Test (LRT) for testing that, in a (non)linear mixed effects model, the variances of a subset of the random effects are equal to zero. There is no restriction on the subset of variances that can be tested: for example, it is possible to test that all the variances are equal to zero. Note that the implemented test is asymptotic. This package should be used on model fits from packages 'nlme', 'lmer', and 'saemix'. Charlotte Baey and Estelle Kuhn (2019) <doi:10.18637/jss.v107.i06>.

Maintained by Charlotte Baey. Last updated 1 years ago.

6.8 match 2 stars 4.48 score 4 scripts 1 dependents

lotze

COMPoissonReg:Conway-Maxwell Poisson (COM-Poisson) Regression

Fit Conway-Maxwell Poisson (COM-Poisson or CMP) regression models to count data (Sellers & Shmueli, 2010) <doi:10.1214/09-AOAS306>. The package provides functions for model estimation, dispersion testing, and diagnostics. Zero-inflated CMP regression (Sellers & Raim, 2016) <doi:10.1016/j.csda.2016.01.007> is also supported.

Maintained by Andrew Raim. Last updated 1 years ago.

cpp

4.5 match 9 stars 6.63 score 53 scripts 3 dependents

scottkosty

bootstrap:Functions for the Book "An Introduction to the Bootstrap"

Software (bootstrap, cross-validation, jackknife) and data for the book "An Introduction to the Bootstrap" by B. Efron and R. Tibshirani, 1993, Chapman and Hall. This package is primarily provided for projects already based on it, and for support of the book. New projects should preferentially use the recommended package "boot".

Maintained by Scott Kostyshak. Last updated 6 years ago.

fortran

3.9 match 7.62 score 890 scripts 30 dependents

jamesotto852

ggdensity:Interpretable Bivariate Density Visualization with 'ggplot2'

The 'ggplot2' package provides simple functions for visualizing contours of 2-d kernel density estimates. 'ggdensity' implements several additional density estimators as well as more interpretable visualizations based on highest density regions instead of the traditional height of the estimated density surface.

Maintained by James Otto. Last updated 1 years ago.

3.6 match 231 stars 8.11 score 185 scripts 2 dependents

aloy

lmeresampler:Bootstrap Methods for Nested Linear Mixed-Effects Models

Bootstrap routines for nested linear mixed effects models fit using either 'lme4' or 'nlme'. The provided 'bootstrap()' function implements the parametric, residual, cases, random effect block (REB), and wild bootstrap procedures. An overview of these procedures can be found in Van der Leeden et al. (2008) <doi: 10.1007/978-0-387-73186-5_11>, Carpenter, Goldstein & Rasbash (2003) <doi: 10.1111/1467-9876.00415>, and Chambers & Chandra (2013) <doi: 10.1080/10618600.2012.681216>.

Maintained by Adam Loy. Last updated 1 years ago.

3.7 match 37 stars 7.83 score 102 scripts 3 dependents

boennecd

mdgc:Missing Data Imputation Using Gaussian Copulas

Provides functions to impute missing values using Gaussian copulas for mixed data types as described by Christoffersen et al. (2021) <arXiv:2102.02642>. The method is related to Hoff (2007) <doi:10.1214/07-AOAS107> and Zhao and Udell (2019) <arXiv:1910.12845> but differs by making a direct approximation of the log marginal likelihood using an extended version of the Fortran code created by Genz and Bretz (2002) <doi:10.1198/106186002394> in addition to also support multinomial variables.

Maintained by Benjamin Christoffersen. Last updated 2 years ago.

binary gaussian-copula imputation multinomial-variables ordinal semi-parametric fortran openblas cpp openmp

7.5 match 10 stars 3.78 score 12 scripts

tidymodels

rsample:General Resampling Infrastructure

Classes and functions to create and summarize different types of resampling objects (e.g. bootstrap, cross-validation).

Maintained by Hannah Frick. Last updated 5 days ago.

1.7 match 341 stars 16.72 score 5.2k scripts 79 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

3.9 match 9 stars 7.16 score 94 scripts

rohelab

vsp:Vintage Sparse PCA for Semi-Parametric Factor Analysis

Provides fast spectral estimation of latent factors in random dot product graphs using the vsp estimator. Under mild assumptions, the vsp estimator is consistent for (degree-corrected) stochastic blockmodels, (degree-corrected) mixed-membership stochastic blockmodels, and degree-corrected overlapping stochastic blockmodels.

Maintained by Alex Hayes. Last updated 4 months ago.

4.5 match 26 stars 6.17 score 19 scripts

kornl

mutoss:Unified Multiple Testing Procedures

Designed to ease the application and comparison of multiple hypothesis testing procedures for FWER, gFWER, FDR and FDX. Methods are standardized and usable by the accompanying 'mutossGUI'.

Maintained by Kornelius Rohmeyer. Last updated 12 months ago.

3.3 match 4 stars 8.44 score 24 scripts 16 dependents

giabaio

survHE:Survival Analysis in Health Economic Evaluation

Contains a suite of functions for survival analysis in health economics. These can be used to run survival models under a frequentist (based on maximum likelihood) or a Bayesian approach (both based on Integrated Nested Laplace Approximation or Hamiltonian Monte Carlo). To run the Bayesian models, the user needs to install additional modules (packages), i.e. 'survHEinla' and 'survHEhmc'. These can be installed using 'remotes::install_github' from their GitHub repositories: (<https://github.com/giabaio/survHEhmc> and <https://github.com/giabaio/survHEinla/> respectively). 'survHEinla' is based on the package INLA, which is available for download at <https://inla.r-inla-download.org/R/stable/>. The user can specify a set of parametric models using a common notation and select the preferred mode of inference. The results can also be post-processed to produce probabilistic sensitivity analysis and can be used to export the output to an Excel file (e.g. for a Markov model, as often done by modellers and practitioners). <doi:10.18637/jss.v095.i14>.

Maintained by Gianluca Baio. Last updated 9 days ago.

frequentist hamiltonian-monte-carlo health-economic-evaluation inla plotting-survival-curves rstan survival-analysis survival-models uncertainty openjdk

4.0 match 42 stars 6.88 score 2 dependents

florianhartig

DHARMa:Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models

The 'DHARMa' package uses a simulation-based approach to create readily interpretable scaled (quantile) residuals for fitted (generalized) linear mixed models. Currently supported are linear and generalized linear (mixed) models from 'lme4' (classes 'lmerMod', 'glmerMod'), 'glmmTMB', 'GLMMadaptive', and 'spaMM'; phylogenetic linear models from 'phylolm' (classes 'phylolm' and 'phyloglm'); generalized additive models ('gam' from 'mgcv'); 'glm' (including 'negbin' from 'MASS', but excluding quasi-distributions) and 'lm' model classes. Moreover, externally created simulations, e.g. posterior predictive simulations from Bayesian software such as 'JAGS', 'STAN', or 'BUGS' can be processed as well. The resulting residuals are standardized to values between 0 and 1 and can be interpreted as intuitively as residuals from a linear regression. The package also provides a number of plot and test functions for typical model misspecification problems, such as over/underdispersion, zero-inflation, and residual spatial, phylogenetic and temporal autocorrelation.

Maintained by Florian Hartig. Last updated 13 days ago.

glmm regression regression-diagnostics residual

1.9 match 226 stars 14.74 score 2.8k scripts 10 dependents

alanaw1

flintyR:Flexible and Interpretable Non-Parametric Tests of Exchangeability

Given a multivariate dataset and some knowledge about the dependencies between its features, it is important to ensure the observations or individuals are exchangeable before fitting a model to the data in order to make inferences from it, or assigning randomized treatments in order to estimate treatment effects. This package provides a flexible non-parametric test of exchangeability, allowing the user to specify the feature dependencies by hand. It can be used directly to evaluate whether a sample is exchangeable, and can also be piped into larger procedures that require exchangeable samples as outputs (e.g., clustering or community detection). See Aw, Spence and Song (2021+) for the accompanying paper.

Maintained by Alan Aw. Last updated 3 years ago.

cpp

8.7 match 2 stars 3.18 score 15 scripts

psychmeta

psychmeta:Psychometric Meta-Analysis Toolkit

Tools for computing bare-bones and psychometric meta-analyses and for generating psychometric data for use in meta-analysis simulations. Supports bare-bones, individual-correction, and artifact-distribution methods for meta-analyzing correlations and d values. Includes tools for converting effect sizes, computing sporadic artifact corrections, reshaping meta-analytic databases, computing multivariate corrections for range variation, and more. Bugs can be reported to <https://github.com/psychmeta/psychmeta/issues> or <issues@psychmeta.com>.

Maintained by Jeffrey A. Dahlke. Last updated 9 months ago.

hacktoberfest meta-analysis psychology psychometric psychometrics

3.3 match 57 stars 8.25 score 151 scripts

bioc

parody:Parametric And Resistant Outlier DYtection

Provide routines for univariate and multivariate outlier detection with a focus on parametric methods, but support for some methods based on resistant statistics.

Maintained by Vince Carey. Last updated 1 months ago.

multiplecomparison

6.7 match 4.08 score 12 scripts

hippolyteboucher

SpeTestNP:Non-Parametric Tests of Parametric Specifications

Performs non-parametric tests of parametric specifications. Five tests are available. Specific bandwidth and kernel methods can be chosen along with many other options. Allows parallel computing to quickly compute p-values based on the bootstrap. Methods implemented in the package are H.J. Bierens (1982) <doi:10.1016/0304-4076(82)90105-1>, J.C. Escanciano (2006) <doi:10.1017/S0266466606060506>, P.L. Gozalo (1997) <doi:10.1016/S0304-4076(97)86571-2>, P. Lavergne and V. Patilea (2008) <doi:10.1016/j.jeconom.2007.08.014>, P. Lavergne and V. Patilea (2012) <doi:10.1198/jbes.2011.07152>, J.H. Stock and M.W. Watson (2006) <doi:10.1111/j.1538-4616.2007.00014.x>, C.F.J. Wu (1986) <doi:10.1214/aos/1176350142>, J. Yin, Z. Geng, R. Li, H. Wang (2010) <https://www.jstor.org/stable/24309002> and J.X. Zheng (1996) <doi:10.1016/0304-4076(95)01760-7>.

Maintained by Hippolyte Boucher. Last updated 2 years ago.

7.3 match 3.70 score 2 scripts

ekstroem

MESS:Miscellaneous Esoteric Statistical Scripts

A mixed collection of useful and semi-useful diverse statistical functions, some of which may even be referenced in The R Primer book. See Ekstrøm, C. T. (2016). The R Primer. 2nd edition. Chapman & Hall.

Maintained by Claus Thorn Ekstrøm. Last updated 29 days ago.

biostatistics power-analysis statistical-analysis statistical-methods statistical-models openblas cpp

3.5 match 4 stars 7.76 score 328 scripts 13 dependents

lbb220

GWmodel:Geographically-Weighted Models

Techniques from a particular branch of spatial statistics,termed geographically-weighted (GW) models. GW models suit situations when data are not described well by some global model, but where there are spatial regions where a suitably localised calibration provides a better description. 'GWmodel' includes functions to calibrate: GW summary statistics (Brunsdon et al., 2002)<doi: 10.1016/s0198-9715(01)00009-6>, GW principal components analysis (Harris et al., 2011)<doi: 10.1080/13658816.2011.554838>, GW discriminant analysis (Brunsdon et al., 2007)<doi: 10.1111/j.1538-4632.2007.00709.x> and various forms of GW regression (Brunsdon et al., 1996)<doi: 10.1111/j.1538-4632.1996.tb00936.x>; some of which are provided in basic and robust (outlier resistant) forms.

Maintained by Binbin Lu. Last updated 6 months ago.

openblas cpp openmp

4.3 match 18 stars 6.38 score 266 scripts 4 dependents

stathin

ggm:Graphical Markov Models with Mixed Graphs

Provides functions for defining mixed graphs containing three types of edges, directed, undirected and bi-directed, with possibly multiple edges. These graphs are useful because they capture fundamental independence structures in multivariate distributions and in the induced distributions after marginalization and conditioning. The package is especially concerned with Gaussian graphical models for (i) ML estimation for directed acyclic graphs, undirected and bi-directed graphs and ancestral graph models (ii) testing several conditional independencies (iii) checking global identification of DAG Gaussian models with one latent variable (iv) testing Markov equivalences and generating Markov equivalent graphs of specific types.

Maintained by Giovanni M. Marchetti. Last updated 1 years ago.

3.8 match 7.07 score 295 scripts 29 dependents

tidymodels

dials:Tools for Creating Tuning Parameter Values

Many models contain tuning parameters (i.e. parameters that cannot be directly estimated from the data). These tools can be used to define objects for creating, simulating, or validating values for such parameters.

Maintained by Hannah Frick. Last updated 30 days ago.

1.9 match 114 stars 14.31 score 426 scripts 52 dependents

bxc147

Epi:Statistical Analysis in Epidemiology

Functions for demographic and epidemiological analysis in the Lexis diagram, i.e. register and cohort follow-up data. In particular representation, manipulation, rate estimation and simulation for multistate data - the Lexis suite of functions, which includes interfaces to 'mstate', 'etm' and 'cmprsk' packages. Contains functions for Age-Period-Cohort and Lee-Carter modeling and a function for interval censored data and some useful functions for tabulation and plotting, as well as a number of epidemiological data sets.

Maintained by Bendix Carstensen. Last updated 2 months ago.

2.8 match 4 stars 9.65 score 708 scripts 11 dependents

jeksterslab

bootStateSpace:Bootstrap for State Space Models

Provides a streamlined and user-friendly framework for bootstrapping in state space models, particularly when the number of subjects/units (n) exceeds one, a scenario commonly encountered in social and behavioral sciences. For an introduction to state space models in social and behavioral sciences, refer to Chow, Ho, Hamaker, and Dolan (2010) <doi:10.1080/10705511003661553>.

Maintained by Ivan Jacob Agaloos Pesigan. Last updated 30 days ago.

bootstrap state-space-model

6.7 match 4.01 score 51 scripts

r-forge

copula:Multivariate Dependence with Copulas

Classes (S4) of commonly used elliptical, Archimedean, extreme-value and other copula families, as well as their rotations, mixtures and asymmetrizations. Nested Archimedean copulas, related tools and special functions. Methods for density, distribution, random number generation, bivariate dependence measures, Rosenblatt transform, Kendall distribution function, perspective and contour plots. Fitting of copula models with potentially partly fixed parameters, including standard errors. Serial independence tests, copula specification tests (independence, exchangeability, radial symmetry, extreme-value dependence, goodness-of-fit) and model selection based on cross-validation. Empirical copula, smoothed versions, and non-parametric estimators of the Pickands dependence function.

Maintained by Martin Maechler. Last updated 12 days ago.

2.2 match 11.83 score 1.2k scripts 86 dependents

mflores72000

qcr:Quality Control Review

Univariate and multivariate SQC tools that completes and increases the SQC techniques available in R. Apart from integrating different R packages devoted to SQC ('qcc','MSQC'), provides nonparametric tools that are highly useful when Gaussian assumption is not met. This package computes standard univariate control charts for individual measurements, X-bar, S, R, p, np, c, u, EWMA and CUSUM. In addition, it includes functions to perform multivariate control charts such as Hotelling T2, MEWMA and MCUSUM. As representative feature, multivariate nonparametric alternatives based on data depth are implemented in this package: r, Q and S control charts. In addition, Phase I and II control charts for functional data are included. This package also allows the estimation of the most complete set of capability indices from first to fourth generation, covering the nonparametric alternatives, and performing the corresponding capability analysis graphical outputs, including the process capability plots.

Maintained by Miguel Flores. Last updated 2 years ago.

5.5 match 4.71 score 172 scripts 1 dependents

kelliejarcher

hdcuremodels:Penalized Mixture Cure Models for High-Dimensional Data

Provides functions for fitting various penalized parametric and semi-parametric mixture cure models with different penalty functions, testing for a significant cure fraction, and testing for sufficient follow-up as described in Fu et al (2022)<doi:10.1002/sim.9513> and Archer et al (2024)<doi:10.1186/s13045-024-01553-6>. False discovery rate controlled variable selection is provided using model-X knock-offs.

Maintained by Kellie J. Archer. Last updated 5 days ago.

5.8 match 4.40 score 5 scripts

hwborchers

pracma:Practical Numerical Math Functions

Provides a large number of functions from numerical analysis and linear algebra, numerical optimization, differential equations, time series, plus some well-known special mathematical functions. Uses 'MATLAB' function names where appropriate to simplify porting.

Maintained by Hans W. Borchers. Last updated 1 years ago.

2.0 match 29 stars 12.34 score 6.6k scripts 931 dependents

merck

gMCPLite:Lightweight Graph Based Multiple Comparison Procedures

A lightweight fork of 'gMCP' with functions for graphical described multiple test procedures introduced in Bretz et al. (2009) <doi:10.1002/sim.3495> and Bretz et al. (2011) <doi:10.1002/bimj.201000239>. Implements a flexible function using 'ggplot2' to create multiplicity graph visualizations. Contains instructions of multiplicity graph and graphical testing for group sequential design, described in Maurer and Bretz (2013) <doi:10.1080/19466315.2013.807748>, with necessary unit testing using 'testthat'.

Maintained by Nan Xiao. Last updated 1 years ago.

4.3 match 11 stars 5.79 score 14 scripts

irtools

irtoys:A Collection of Functions Related to Item Response Theory (IRT)

A collection of functions useful in learning and practicing IRT, which can be combined into larger programs. Provides basic CTT analysis, a simple common interface to the estimation of item parameters in IRT models for binary responses with three different programs (ICL, BILOG-MG, and ltm), ability estimation (MLE, BME, EAP, WLE, plausible values), item and person fit statistics, scaling methods (MM, MS, Stocking-Lord, and the complete Hebaera method), and a rich array of parametric and non-parametric (kernel) plots. Estimates and plots Haberman's interaction model when all items are dichotomously scored.

Maintained by Ivailo Partchev. Last updated 3 years ago.

4.5 match 3 stars 5.39 score 55 scripts 5 dependents

jaimemosg

EstimationTools:Maximum Likelihood Estimation for Probability Functions from Data Sets

Total Time on Test plot and routines for parameter estimation of any lifetime distribution implemented in R via maximum likelihood (ML) given a data set. It is implemented thinking on parametric survival analysis, but it feasible to use in parameter estimation of probability density or mass functions in any field. The main routines 'maxlogL' and 'maxlogLreg' are wrapper functions specifically developed for ML estimation. There are included optimization procedures such as 'nlminb' and 'optim' from base package, and 'DEoptim' Mullen (2011) <doi:10.18637/jss.v040.i06>. Standard errors are estimated with 'numDeriv' Gilbert (2011) <https://CRAN.R-project.org/package=numDeriv> or the option 'Hessian = TRUE' of 'optim' function.

Maintained by Jaime Mosquera. Last updated 1 years ago.

distance-sampling maximum-likelihood-estimation ttt-plot

4.0 match 4 stars 6.07 score 97 scripts 1 dependents

tingtingzhan

fmx:Finite Mixture Parametrization

A parametrization framework for finite mixture distribution using S4 objects. Density, cumulative density, quantile and simulation functions are defined. Currently normal, Tukey g-&-h, skew-normal and skew-t distributions are well tested. The gamma, negative binomial distributions are being tested.

Maintained by Tingting Zhan. Last updated 1 days ago.

7.6 match 3.18 score 7 scripts 1 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 14 days ago.

density estimation maximum-likelihood

3.0 match 8 stars 8.10 score 62 scripts 7 dependents

majkamichal

naivebayes:High Performance Implementation of the Naive Bayes Algorithm

In this implementation of the Naive Bayes classifier following class conditional distributions are available: 'Bernoulli', 'Categorical', 'Gaussian', 'Poisson', 'Multinomial' and non-parametric representation of the class conditional density estimated via Kernel Density Estimation. Implemented classifiers handle missing data and can take advantage of sparse data.

Maintained by Michal Majka. Last updated 1 months ago.

classification-model datascience machine-learning naive-bayes

2.3 match 37 stars 10.47 score 1.0k scripts 6 dependents

r-spatial

spatialreg:Spatial Regression Analysis

A collection of all the estimation functions for spatial cross-sectional models (on lattice/areal data using spatial weights matrices) contained up to now in 'spdep'. These model fitting functions include maximum likelihood methods for cross-sectional models proposed by 'Cliff' and 'Ord' (1973, ISBN:0850860369) and (1981, ISBN:0850860814), fitting methods initially described by 'Ord' (1975) <doi:10.1080/01621459.1975.10480272>. The models are further described by 'Anselin' (1988) <doi:10.1007/978-94-015-7799-1>. Spatial two stage least squares and spatial general method of moment models initially proposed by 'Kelejian' and 'Prucha' (1998) <doi:10.1023/A:1007707430416> and (1999) <doi:10.1111/1468-2354.00027> are provided. Impact methods and MCMC fitting methods proposed by 'LeSage' and 'Pace' (2009) <doi:10.1201/9781420064254> are implemented for the family of cross-sectional spatial regression models. Methods for fitting the log determinant term in maximum likelihood and MCMC fitting are compared by 'Bivand et al.' (2013) <doi:10.1111/gean.12008>, and model fitting methods by 'Bivand' and 'Piras' (2015) <doi:10.18637/jss.v063.i18>; both of these articles include extensive lists of references. A recent review is provided by 'Bivand', 'Millo' and 'Piras' (2021) <doi:10.3390/math9111276>. 'spatialreg' >= 1.1-* corresponded to 'spdep' >= 1.1-1, in which the model fitting functions were deprecated and passed through to 'spatialreg', but masked those in 'spatialreg'. From versions 1.2-*, the functions have been made defunct in 'spdep'. From version 1.3-6, add Anselin-Kelejian (1997) test to `stsls` for residual spatial autocorrelation <doi:10.1177/016001769702000109>.

Maintained by Roger Bivand. Last updated 3 days ago.

bayesian impacts maximum-likelihood spatial-dependence spatial-econometrics spatial-regression openblas

1.9 match 46 stars 12.92 score 916 scripts 24 dependents

bioc

MSnbase:Base Functions and Classes for Mass Spectrometry and Proteomics

MSnbase provides infrastructure for manipulation, processing and visualisation of mass spectrometry and proteomics data, ranging from raw to quantitative and annotated data.

Maintained by Laurent Gatto. Last updated 2 days ago.

immunooncology infrastructure proteomics massspectrometry qualitycontrol dataimport bioconductor bioinformatics mass-spectrometry proteomics-data visualisation cpp

1.9 match 130 stars 12.81 score 772 scripts 36 dependents

stochastictree

stochtree:Stochastic Tree Ensembles (XBART and BART) for Supervised Learning and Causal Inference

Flexible stochastic tree ensemble software. Robust implementations of Bayesian Additive Regression Trees (BART) Chipman, George, McCulloch (2010) <doi:10.1214/09-AOAS285> for supervised learning and Bayesian Causal Forests (BCF) Hahn, Murray, Carvalho (2020) <doi:10.1214/19-BA1195> for causal inference. Enables model serialization and parallel sampling and provides a low-level interface for custom stochastic forest samplers.

Maintained by Drew Herren. Last updated 18 days ago.

bart bayesian-machine-learning bayesian-methods decision-trees gradient-boosted-trees machine-learning probabilistic-models tree-ensembles cpp

2.8 match 20 stars 8.52 score 40 scripts

chrhennig

prabclus:Functions for Clustering and Testing of Presence-Absence, Abundance and Multilocus Genetic Data

Distance-based parametric bootstrap tests for clustering with spatial neighborhood information. Some distance measures, Clustering of presence-absence, abundance and multilocus genetic data for species delimitation, nearest neighbor based noise detection. Genetic distances between communities. Tests whether various distance-based regressions are equal. Try package?prabclus for on overview.

Maintained by Christian Hennig. Last updated 6 months ago.

4.0 match 1 stars 5.99 score 90 scripts 71 dependents

rstudio

keras3:R Interface to 'Keras'

Interface to 'Keras' <https://keras.io>, a high-level neural networks API. 'Keras' was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations of the two), and runs seamlessly on both CPU and GPU devices.

Maintained by Tomasz Kalinowski. Last updated 4 days ago.

1.8 match 845 stars 13.57 score 264 scripts 2 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

3.0 match 18 stars 7.87 score 16 scripts

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

2.9 match 38 stars 8.09 score 75 scripts 26 dependents

chjackson

survextrap:Survival Extrapolation with a Flexible Parametric Model and External Data

Survival analysis using a flexible Bayesian model for individual-level right-censored data, optionally combined with aggregate data on counts of survivors in different periods of time. An M-spline is used to describe the hazard function, with a hierarchical prior on the coefficients to control overfitting. Proportional hazards or flexible non-proportional hazards models can be used to relate survival to predictors. Mixture cure models, additive hazards (relative survival) models and waning treatment effects models are also supported. Priors can be customised and calibrated to substantive beliefs. Posterior distributions are estimated using Stan, and outputs are arranged in a tidy format. See See Jackson (2023) <doi:10.48550/arXiv.2306.03957>.

Maintained by Christopher Jackson. Last updated 18 days ago.

cpp

4.7 match 10 stars 5.00 score 11 scripts

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.

5.0 match 1 stars 4.57 score 123 scripts 2 dependents

cran

mgcv:Mixed GAM Computation Vehicle with Automatic Smoothness Estimation

Generalized additive (mixed) models, some of their extensions and other generalized ridge regression with multiple smoothing parameter estimation by (Restricted) Marginal Likelihood, Generalized Cross Validation and similar, or using iterated nested Laplace approximation for fully Bayesian inference. See Wood (2017) <doi:10.1201/9781315370279> for an overview. Includes a gam() function, a wide variety of smoothers, 'JAGS' support and distributions beyond the exponential family.

Maintained by Simon Wood. Last updated 1 years ago.

openblas openmp

1.8 match 32 stars 12.71 score 17k scripts 7.8k 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

3.6 match 22 stars 6.31 score 186 scripts

bioc

mina:Microbial community dIversity and Network Analysis

An increasing number of microbiome datasets have been generated and analyzed with the help of rapidly developing sequencing technologies. At present, analysis of taxonomic profiling data is mainly conducted using composition-based methods, which ignores interactions between community members. Besides this, a lack of efficient ways to compare microbial interaction networks limited the study of community dynamics. To better understand how community diversity is affected by complex interactions between its members, we developed a framework (Microbial community dIversity and Network Analysis, mina), a comprehensive framework for microbial community diversity analysis and network comparison. By defining and integrating network-derived community features, we greatly reduce noise-to-signal ratio for diversity analyses. A bootstrap and permutation-based method was implemented to assess community network dissimilarities and extract discriminative features in a statistically principled way.

Maintained by Rui Guan. Last updated 5 months ago.

software workflowstep cpp

4.7 match 5 stars 4.85 score 14 scripts

cran

nlme:Linear and Nonlinear Mixed Effects Models

Fit and compare Gaussian linear and nonlinear mixed-effects models.

Maintained by R Core Team. Last updated 2 months ago.

fortran

1.8 match 6 stars 13.00 score 13k scripts 8.7k dependents

jernejjevsenak

MLFS:Machine Learning Forest Simulator

Climate-sensitive forest simulator based on the principles of machine learning. It stimulates all key processes in the forest: radial growth, height growth, mortality, crown recession, regeneration and harvesting. The method for predicting tree heights was described by Skudnik and Jevšenak (2022) <doi:10.1016/j.foreco.2022.120017>, while the method for predicting basal area increments (BAI) was described by Jevšenak and Skudnik (2021) <doi:10.1016/j.foreco.2020.118601>.

Maintained by Jernej Jevsenak. Last updated 3 years ago.

6.7 match 2 stars 3.40 score 25 scripts

sineadmorris

ushr:Understanding Suppression of HIV

Analyzes longitudinal data of HIV decline in patients on antiretroviral therapy using the canonical biphasic exponential decay model (pioneered, for example, by work in Perelson et al. (1997) <doi:10.1038/387188a0>; and Wu and Ding (1999) <doi:10.1111/j.0006-341X.1999.00410.x>). Model fitting and parameter estimation are performed, with additional options to calculate the time to viral suppression. Plotting and summary tools are also provided for fast assessment of model results.

Maintained by Sinead E. Morris. Last updated 5 years ago.

art hiv modeling-tools

5.6 match 2 stars 4.04 score 11 scripts

entjos

JointFPM:A Parametric Model for Estimating the Mean Number of Events

Implementation of a parametric joint model for modelling recurrent and competing event processes using generalised survival models as described in Entrop et al., (2025) <doi:10.1002/bimj.70038>. The joint model can subsequently be used to predict the mean number of events in the presence of competing risks at different time points. Comparisons of the mean number of event functions, e.g. the differences in mean number of events between two exposure groups, are also available.

Maintained by Joshua P. Entrop. Last updated 24 days ago.

recurrent-events survival-analysis

5.2 match 7 stars 4.36 score 11 scripts

bioc

HEM:Heterogeneous error model for identification of differentially expressed genes under multiple conditions

This package fits heterogeneous error models for analysis of microarray data

Maintained by HyungJun Cho. Last updated 5 months ago.

microarray differentialexpression

5.3 match 4.30 score 6 scripts

alexisderumigny

CondCopulas:Estimation and Inference for Conditional Copula Models

Provides functions for the estimation of conditional copulas models, various estimators of conditional Kendall's tau (proposed in Derumigny and Fermanian (2019a, 2019b, 2020) <doi:10.1515/demo-2019-0016>, <doi:10.1016/j.csda.2019.01.013>, <doi:10.1016/j.jmva.2020.104610>), and test procedures for the simplifying assumption (proposed in Derumigny and Fermanian (2017) <doi:10.1515/demo-2017-0011> and Derumigny, Fermanian and Min (2022) <doi:10.1002/cjs.11742>).

Maintained by Alexis Derumigny. Last updated 6 months ago.

conditional-copulas conditional-kendalls-tau copulas r-pkg simplifying-assumption

4.8 match 2 stars 4.70 score 7 scripts

cran

airGRteaching:Teaching Hydrological Modelling with the GR Rainfall-Runoff Models ('Shiny' Interface Included)

Add-on package to the 'airGR' package that simplifies its use and is aimed at being used for teaching hydrology. The package provides 1) three functions that allow to complete very simply a hydrological modelling exercise 2) plotting functions to help students to explore observed data and to interpret the results of calibration and simulation of the GR ('Génie rural') models 3) a 'Shiny' graphical interface that allows for displaying the impact of model parameters on hydrographs and models internal variables.

Maintained by Olivier Delaigue. Last updated 1 months ago.

4.7 match 6 stars 4.82 score

covid19datahub

COVID19:COVID-19 Data Hub

Unified datasets for a better understanding of COVID-19.

Maintained by Emanuele Guidotti. Last updated 28 days ago.

2019-ncov coronavirus covid-19 covid-data covid19-data

2.0 match 252 stars 11.08 score 265 scripts

lassehjort

cuRe:Parametric Cure Model Estimation

Contains functions for estimating generalized parametric mixture and non-mixture cure models, loss of lifetime, mean residual lifetime, and crude event probabilities.

Maintained by Lasse Hjort Jakobsen. Last updated 2 years ago.

5.7 match 9 stars 3.90 score 22 scripts

acguidoum

Sim.DiffProc:Simulation of Diffusion Processes

It provides users with a wide range of tools to simulate, estimate, analyze, and visualize the dynamics of stochastic differential systems in both forms Ito and Stratonovich. Statistical analysis with parallel Monte Carlo and moment equations methods of SDEs <doi:10.18637/jss.v096.i02>. Enabled many searchers in different domains to use these equations to modeling practical problems in financial and actuarial modeling and other areas of application, e.g., modeling and simulate of first passage time problem in shallow water using the attractive center (Boukhetala K, 1996) ISBN:1-56252-342-2.

Maintained by Arsalane Chouaib Guidoum. Last updated 1 years ago.

dynamic-system moment-equations monte-carlo-simulation parallel-computing stochastic-calculus stochastic-differential-equation transition-density

2.9 match 13 stars 7.69 score 86 scripts 4 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

5.5 match 1 stars 4.00 score 7 scripts

bozenne

LMMstar:Repeated Measurement Models for Discrete Times

Companion R package for the course "Statistical analysis of correlated and repeated measurements for health science researchers" taught by the section of Biostatistics of the University of Copenhagen. It implements linear mixed models where the model for the variance-covariance of the residuals is specified via patterns (compound symmetry, toeplitz, unstructured, ...). Statistical inference for mean, variance, and correlation parameters is performed based on the observed information and a Satterthwaite approximation of the degrees of freedom. Normalized residuals are provided to assess model misspecification. Statistical inference can be performed for arbitrary linear or non-linear combination(s) of model coefficients. Predictions can be computed conditional to covariates only or also to outcome values.

Maintained by Brice Ozenne. Last updated 5 months ago.

3.5 match 4 stars 6.28 score 141 scripts

dongwenluo

predictmeans:Predicted Means for Linear and Semiparametric Models

Providing functions to diagnose and make inferences from various linear models, such as those obtained from 'aov', 'lm', 'glm', 'gls', 'lme', 'lmer', 'glmmTMB' and 'semireg'. Inferences include predicted means and standard errors, contrasts, multiple comparisons, permutation tests, adjusted R-square and graphs.

Maintained by Dongwen Luo. Last updated 11 months ago.

3.5 match 2 stars 6.26 score 152 scripts 2 dependents

bioc

waddR:Statistical tests for detecting differential distributions based on the 2-Wasserstein distance

The package offers statistical tests based on the 2-Wasserstein distance for detecting and characterizing differences between two distributions given in the form of samples. Functions for calculating the 2-Wasserstein distance and testing for differential distributions are provided, as well as a specifically tailored test for differential expression in single-cell RNA sequencing data.

Maintained by Julian Flesch. Last updated 5 months ago.

software statisticalmethod singlecell differentialexpression cpp

3.2 match 25 stars 6.70 score 6 scripts

emf-creaf

medfateland:Mediterranean Landscape Simulation

Simulate forest hydrology, forest function and dynamics over landscapes [De Caceres et al. (2015) <doi:10.1016/j.agrformet.2015.06.012>]. Parallelization is allowed in several simulation functions and simulations may be conducted including spatial processes such as lateral water transfer and seed dispersal.

Maintained by Miquel De Cáceres. Last updated 25 days ago.

cpp

4.0 match 5 stars 5.41 score 41 scripts

dgarrimar

manta:Multivariate Asymptotic Non-Parametric Test of Association

The Multivariate Asymptotic Non-parametric Test of Association (MANTA) enables non-parametric, asymptotic P-value computation for multivariate linear models. MANTA relies on the asymptotic null distribution of the PERMANOVA test statistic. P-values are computed using a highly accurate approximation of the corresponding cumulative distribution function. Garrido-Martín et al. (2022) <doi:10.1101/2022.06.06.493041>.

Maintained by Diego Garrido-Martín. Last updated 1 years ago.

cpp

5.4 match 13 stars 4.02 score 16 scripts

alexiosg

rugarch:Univariate GARCH Models

ARFIMA, in-mean, external regressors and various GARCH flavors, with methods for fit, forecast, simulation, inference and plotting.

Maintained by Alexios Galanos. Last updated 3 months ago.

cpp

1.8 match 26 stars 12.13 score 1.3k scripts 15 dependents

tkmckenzie

snfa:Smooth Non-Parametric Frontier Analysis

Fitting of non-parametric production frontiers for use in efficiency analysis. Methods are provided for both a smooth analogue of Data Envelopment Analysis (DEA) and a non-parametric analogue of Stochastic Frontier Analysis (SFA). Frontiers are constructed for multiple inputs and a single output using constrained kernel smoothing as in Racine et al. (2009), which allow for the imposition of monotonicity and concavity constraints on the estimated frontier.

Maintained by Taylor McKenzie. Last updated 5 years ago.

5.7 match 3.70 score 8 scripts

distancedevelopment

dsm:Density Surface Modelling of Distance Sampling Data

Density surface modelling of line transect data. A Generalized Additive Model-based approach is used to calculate spatially-explicit estimates of animal abundance from distance sampling (also presence/absence and strip transect) data. Several utility functions are provided for model checking, plotting and variance estimation.

Maintained by Laura Marshall. Last updated 2 years ago.

3.4 match 8 stars 6.09 score 146 scripts

r-forge

zipfR:Statistical Models for Word Frequency Distributions

Statistical models and utilities for the analysis of word frequency distributions. The utilities include functions for loading, manipulating and visualizing word frequency data and vocabulary growth curves. The package also implements several statistical models for the distribution of word frequencies in a population. (The name of this package derives from the most famous word frequency distribution, Zipf's law.)

Maintained by Stefan Evert. Last updated 4 years ago.

3.5 match 5.94 score 188 scripts 12 dependents

dicook

nullabor:Tools for Graphical Inference

Tools for visual inference. Generate null data sets and null plots using permutation and simulation. Calculate distance metrics for a lineup, and examine the distributions of metrics.

Maintained by Di Cook. Last updated 1 months ago.

2.0 match 57 stars 10.38 score 370 scripts 2 dependents

nt-williams

lmtp:Non-Parametric Causal Effects of Feasible Interventions Based on Modified Treatment Policies

Non-parametric estimators for casual effects based on longitudinal modified treatment policies as described in Diaz, Williams, Hoffman, and Schenck <doi:10.1080/01621459.2021.1955691>, traditional point treatment, and traditional longitudinal effects. Continuous, binary, categorical treatments, and multivariate treatments are allowed as well are censored outcomes. The treatment mechanism is estimated via a density ratio classification procedure irrespective of treatment variable type. For both continuous and binary outcomes, additive treatment effects can be calculated and relative risks and odds ratios may be calculated for binary outcomes. Supports survival outcomes with competing risks (Diaz, Hoffman, and Hejazi; <doi:10.1007/s10985-023-09606-7>).

Maintained by Nicholas Williams. Last updated 9 days ago.

causal-inference censored-data longitudinal-data machine-learning modified-treatment-policy nonparametric-statistics precision-medicine robust-statistics statistics stochastic-interventions survival-analysis targeted-learning

3.3 match 64 stars 6.37 score 91 scripts

jinhuasu

SemiEstimate:Solve Semi-Parametric Estimation by Implicit Profiling

Semi-parametric estimation problem can be solved by two-step Newton-Raphson iteration. The implicit profiling method<arXiv:2108.07928> is an improved method of two-step NR iteration especially for the implicit-bundled type of the parametric part and non-parametric part. This package provides a function semislv() supporting the above two methods and numeric derivative approximation for unprovided Jacobian matrix.

Maintained by Jinhua Su. Last updated 3 years ago.

5.6 match 3.70 score 3 scripts

cran

expertsurv:Incorporate Expert Opinion with Parametric Survival Models

Enables users to incorporate expert opinion with parametric survival analysis using a Bayesian or frequentist approach. Expert Opinion can be provided on the survival probabilities at certain time-point(s) or for the difference in mean survival between two treatment arms. Please reference it's use as Cooney, P., White, A. (2023) <doi:10.1177/0272989X221150212>.

Maintained by Philip Cooney. Last updated 24 days ago.

cpp

6.9 match 3.00 score 1 scripts

philips-software

latrend:A Framework for Clustering Longitudinal Data

A framework for clustering longitudinal datasets in a standardized way. The package provides an interface to existing R packages for clustering longitudinal univariate trajectories, facilitating reproducible and transparent analyses. Additionally, standard tools are provided to support cluster analyses, including repeated estimation, model validation, and model assessment. The interface enables users to compare results between methods, and to implement and evaluate new methods with ease. The 'akmedoids' package is available from <https://github.com/MAnalytics/akmedoids>.

Maintained by Niek Den Teuling. Last updated 2 months ago.

cluster-analysis clustering-evaluation clustering-methods data-science longitudinal-clustering longitudinal-data mixture-models time-series-analysis

3.0 match 30 stars 6.77 score 26 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.

2.8 match 23 stars 7.39 score 178 scripts 2 dependents

merck

psm3mkv:Evaluate Partitioned Survival and State Transition Models

Fits and evaluates three-state partitioned survival analyses (PartSAs) and Markov models (clock forward or clock reset) to progression and overall survival data typically collected in oncology clinical trials. These model structures are typically considered in cost-effectiveness modeling in advanced/metastatic cancer indications. Muston (2024). "Informing structural assumptions for three state oncology cost-effectiveness models through model efficiency and fit". Applied Health Economics and Health Policy.

Maintained by Dominic Muston. Last updated 9 months ago.

3.2 match 10 stars 6.43 score 1 scripts

plant-functional-trait-course

traitstrap:Bootstrap Trait Values to Calculate Moments

Calculates trait moments from trait and community data using the methods developed in Maitner et al (2021) <doi:10.22541/au.162196147.76797968/v1>.

Maintained by Richard James Telford. Last updated 9 months ago.

3.5 match 12 stars 5.83 score 28 scripts

mmaechler

sfsmisc:Utilities from 'Seminar fuer Statistik' ETH Zurich

Useful utilities ['goodies'] from Seminar fuer Statistik ETH Zurich, some of which were ported from S-plus in the 1990s. For graphics, have pretty (Log-scale) axes eaxis(), an enhanced Tukey-Anscombe plot, combining histogram and boxplot, 2d-residual plots, a 'tachoPlot()', pretty arrows, etc. For robustness, have a robust F test and robust range(). For system support, notably on Linux, provides 'Sys.*()' functions with more access to system and CPU information. Finally, miscellaneous utilities such as simple efficient prime numbers, integer codes, Duplicated(), toLatex.numeric() and is.whole().

Maintained by Martin Maechler. Last updated 5 months ago.

1.9 match 11 stars 10.87 score 566 scripts 119 dependents

t-kalinowski

keras:R Interface to 'Keras'

Interface to 'Keras' <https://keras.io>, a high-level neural networks 'API'. 'Keras' was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations of the two), and runs seamlessly on both 'CPU' and 'GPU' devices.

Maintained by Tomasz Kalinowski. Last updated 11 months ago.

1.9 match 10.82 score 10k scripts 54 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.

5.2 match 1 stars 3.89 score 93 scripts 5 dependents

bioc

rain:Rhythmicity Analysis Incorporating Non-parametric Methods

This package uses non-parametric methods to detect rhythms in time series. It deals with outliers, missing values and is optimized for time series comprising 10-100 measurements. As it does not assume expect any distinct waveform it is optimal or detecting oscillating behavior (e.g. circadian or cell cycle) in e.g. genome- or proteome-wide biological measurements such as: micro arrays, proteome mass spectrometry, or metabolome measurements.

Maintained by Paul F. Thaben. Last updated 5 months ago.

timecourse genetics systemsbiology proteomics microarray multiplecomparison

5.2 match 3.88 score 19 scripts

capnrefsmmat

regressinator:Simulate and Diagnose (Generalized) Linear Models

Simulate samples from populations with known covariate distributions, generate response variables according to common linear and generalized linear model families, draw from sampling distributions of regression estimates, and perform visual inference on diagnostics from model fits.

Maintained by Alex Reinhart. Last updated 5 months ago.

statistics

3.3 match 4 stars 6.08 score 25 scripts

konne55

TesiproV:Calculation of Reliability and Failure Probability in Civil Engineering

Calculate the failure probability of civil engineering problems with Level I up to Level III Methods. Have fun and enjoy. References: Spaethe (1991, ISBN:3-211-82348-4) "Die Sicherheit tragender Baukonstruktionen", AU,BECK (2001) "Estimation of small failure probabilities in high dimensions by subset simulation." <doi:10.1016/S0266-8920(01)00019-4>, Breitung (1989) "Asymptotic approximations for probability integrals." <doi:10.1016/0266-8920(89)90024-6>.

Maintained by Konstantin Nille-Hauf. Last updated 3 years ago.

7.4 match 2.70 score 2 scripts

gamlss-dev

gamlss:Generalized Additive Models for Location Scale and Shape

Functions for fitting the Generalized Additive Models for Location Scale and Shape introduced by Rigby and Stasinopoulos (2005), <doi:10.1111/j.1467-9876.2005.00510.x>. The models use a distributional regression approach where all the parameters of the conditional distribution of the response variable are modelled using explanatory variables.

Maintained by Mikis Stasinopoulos. Last updated 4 months ago.

1.8 match 16 stars 11.23 score 2.0k scripts 49 dependents

swampthingpaul

NADA2:Data Analysis for Censored Environmental Data

Contains methods described by Dennis Helsel in his book "Statistics for Censored Environmental Data using Minitab and R" (2011) and courses and videos at <https://practicalstats.com>. This package adds new functions to the `NADA` Package.

Maintained by Paul Julian. Last updated 6 months ago.

3.2 match 15 stars 6.16 score 16 scripts

nago2020

depCensoring:Statistical Methods for Survival Data with Dependent Censoring

Several statistical methods for analyzing survival data under various forms of dependent censoring are implemented in the package. In addition to accounting for dependent censoring, it offers tools to adjust for unmeasured confounding factors. The implemented approaches allow users to estimate the dependency between survival time and dependent censoring time, based solely on observed survival data. For more details on the methods, refer to Deresa and Van Keilegom (2021) <doi:10.1093/biomet/asaa095>, Czado and Van Keilegom (2023) <doi:10.1093/biomet/asac067>, Crommen et al. (2024) <doi:10.1007/s11749-023-00903-9>, Deresa and Van Keilegom (2024) <doi:10.1080/01621459.2022.2161387>, Rutten et al. (2024+) <doi:10.48550/arXiv.2403.11860> and Ding and Van Keilegom (2024).

Maintained by Negera Wakgari Deresa. Last updated 11 days ago.

7.1 match 2.78 score 5 scripts

f-rousset

spaMM:Mixed-Effect Models, with or without Spatial Random Effects

Inference based on models with or without spatially-correlated random effects, multivariate responses, or non-Gaussian random effects (e.g., Beta). Variation in residual variance (heteroscedasticity) can itself be represented by a mixed-effect model. Both classical geostatistical models (Rousset and Ferdy 2014 <doi:10.1111/ecog.00566>), and Markov random field models on irregular grids (as considered in the 'INLA' package, <https://www.r-inla.org>), can be fitted, with distinct computational procedures exploiting the sparse matrix representations for the latter case and other autoregressive models. Laplace approximations are used for likelihood or restricted likelihood. Penalized quasi-likelihood and other variants discussed in the h-likelihood literature (Lee and Nelder 2001 <doi:10.1093/biomet/88.4.987>) are also implemented.

Maintained by François Rousset. Last updated 9 months ago.

gsl cpp openmp

4.0 match 4.94 score 208 scripts 5 dependents

squidlobster

castor:Efficient Phylogenetics on Large Trees

Efficient phylogenetic analyses on massive phylogenies comprising up to millions of tips. Functions include pruning, rerooting, calculation of most-recent common ancestors, calculating distances from the tree root and calculating pairwise distances. Calculation of phylogenetic signal and mean trait depth (trait conservatism), ancestral state reconstruction and hidden character prediction of discrete characters, simulating and fitting models of trait evolution, fitting and simulating diversification models, dating trees, comparing trees, and reading/writing trees in Newick format. Citation: Louca, Stilianos and Doebeli, Michael (2017) <doi:10.1093/bioinformatics/btx701>.

Maintained by Stilianos Louca. Last updated 4 months ago.

cpp

3.4 match 2 stars 5.75 score 450 scripts 9 dependents

scheike

timereg:Flexible Regression Models for Survival Data

Programs for Martinussen and Scheike (2006), `Dynamic Regression Models for Survival Data', Springer Verlag. Plus more recent developments. Additive survival model, semiparametric proportional odds model, fast cumulative residuals, excess risk models and more. Flexible competing risks regression including GOF-tests. Two-stage frailty modelling. PLS for the additive risk model. Lasso in the 'ahaz' package.

Maintained by Thomas Scheike. Last updated 6 months ago.

openblas

1.9 match 31 stars 10.42 score 289 scripts 44 dependents

jean997

rcc:Parametric Bootstrapping to Control Rank Conditional Coverage

Functions to implement the parametric and non-parametric bootstrap confidence interval methods described in Morrison and Simon (2017) <arXiv:1702.06986>.

Maintained by Jean Morrison. Last updated 8 years ago.

7.2 match 2.70 score 5 scripts

pedrohcgs

pstest:Specification Tests for Parametric Propensity Score Models

The propensity score is one of the most widely used tools in studying the causal effect of a treatment, intervention, or policy. Given that the propensity score is usually unknown, it has to be estimated, implying that the reliability of many treatment effect estimators depends on the correct specification of the (parametric) propensity score. This package implements the data-driven nonparametric diagnostic tools for detecting propensity score misspecification proposed by Sant'Anna and Song (2019) <doi:10.1016/j.jeconom.2019.02.002>.

Maintained by Pedro H. C. SantAnna. Last updated 3 years ago.

5.1 match 13 stars 3.81 score 2 scripts

refunders

refund:Regression with Functional Data

Methods for regression for functional data, including function-on-scalar, scalar-on-function, and function-on-function regression. Some of the functions are applicable to image data.

Maintained by Julia Wrobel. Last updated 6 months ago.

1.9 match 41 stars 10.25 score 472 scripts 16 dependents

cschwarz-stat-sfu-ca

BTSPAS:Bayesian Time-Stratified Population Analysis

Provides advanced Bayesian methods to estimate abundance and run-timing from temporally-stratified Petersen mark-recapture experiments. Methods include hierarchical modelling of the capture probabilities and spline smoothing of the daily run size. Theory described in Bonner and Schwarz (2011) <doi:10.1111/j.1541-0420.2011.01599.x>.

Maintained by Carl J Schwarz. Last updated 5 months ago.

jags cpp

3.2 match 1 stars 6.04 score 28 scripts 1 dependents

cran

DCchoice:Analyzing Dichotomous Choice Contingent Valuation Data

Functions for analyzing dichotomous choice contingent valuation (CV) data. It provides functions for estimating parametric and nonparametric models for single-, one-and-one-half-, and double-bounded CV data. For details, see Aizaki et al. (2022) <doi:10.1007/s42081-022-00171-1>.

Maintained by Hideo Aizaki. Last updated 2 years ago.

8.8 match 1 stars 2.18 score 31 scripts 1 dependents

fmestre1

MetaLandSim:Landscape and Range Expansion Simulation

Tools to generate random landscape graphs, evaluate species occurrence in dynamic landscapes, simulate future landscape occupation and evaluate range expansion when new empty patches are available (e.g. as a result of climate change). References: Mestre, F., Canovas, F., Pita, R., Mira, A., Beja, P. (2016) <doi:10.1016/j.envsoft.2016.03.007>; Mestre, F., Risk, B., Mira, A., Beja, P., Pita, R. (2017) <doi:10.1016/j.ecolmodel.2017.06.013>; Mestre, F., Pita, R., Mira, A., Beja, P. (2020) <doi:10.1186/s12898-019-0273-5>.

Maintained by Frederico Mestre. Last updated 2 years ago.

biogeography ecology metapopulation species

3.8 match 3 stars 5.10 score 28 scripts

vdorie

stan4bart:Bayesian Additive Regression Trees with Stan-Sampled Parametric Extensions

Fits semiparametric linear and multilevel models with non-parametric additive Bayesian additive regression tree (BART; Chipman, George, and McCulloch (2010) <doi:10.1214/09-AOAS285>) components and Stan (Stan Development Team (2021) <https://mc-stan.org/>) sampled parametric ones. Multilevel models can be expressed using 'lme4' syntax (Bates, Maechler, Bolker, and Walker (2015) <doi:10.18637/jss.v067.i01>).

Maintained by Vincent Dorie. Last updated 6 months ago.

bart bayesian stan cpp

3.6 match 42 stars 5.29 score 23 scripts

klainfo

ScottKnottESD:The Non-Parametric Scott-Knott Effect Size Difference (ESD) Test

The Non-Parametric Scott-Knott Effect Size Difference (ESD) test is a mean comparison approach that leverages a hierarchical clustering to partition the set of treatment means (e.g., means of variable importance scores, means of model performance) into statistically distinct groups with non-negligible difference [Tantithamthavorn et al., (2018) <doi:10.1109/TSE.2018.2794977>].

Maintained by Chakkrit Tantithamthavorn. Last updated 2 years ago.

defect-prediction-models effect-size multiple-comparisons ranking-algorithm scott-knott statistical-tests

3.3 match 43 stars 5.77 score 68 scripts