R-universe search: topic:statistical-inference

Showing 15 of total 15 results (show query)

kingaa

pomp:Statistical Inference for Partially Observed Markov Processes

Tools for data analysis with partially observed Markov process (POMP) models (also known as stochastic dynamical systems, hidden Markov models, and nonlinear, non-Gaussian, state-space models). The package provides facilities for implementing POMP models, simulating them, and fitting them to time series data by a variety of frequentist and Bayesian methods. It is also a versatile platform for implementation of inference methods for general POMP models.

Maintained by Aaron A. King. Last updated 13 days ago.

abc b-spline differential-equations dynamical-systems iterated-filtering likelihood likelihood-free markov-chain-monte-carlo markov-model mathematical-modelling measurement-error particle-filter sequential-monte-carlo simulation-based-inference sobol-sequence state-space statistical-inference stochastic-processes time-series openblas

114 stars 11.74 score 1.3k scripts 4 dependents

spatstat

spatstat.linnet:Linear Networks Functionality of the 'spatstat' Family

Defines types of spatial data on a linear network and provides functionality for geometrical operations, data analysis and modelling of data on a linear network, in the 'spatstat' family of packages. Contains definitions and support for linear networks, including creation of networks, geometrical measurements, topological connectivity, geometrical operations such as inserting and deleting vertices, intersecting a network with another object, and interactive editing of networks. Data types defined on a network include point patterns, pixel images, functions, and tessellations. Exploratory methods include kernel estimation of intensity on a network, K-functions and pair correlation functions on a network, simulation envelopes, nearest neighbour distance and empty space distance, relative risk estimation with cross-validated bandwidth selection. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported. Parametric models can be fitted to point pattern data using the function lppm() similar to glm(). Only Poisson models are implemented so far. Models may involve dependence on covariates and dependence on marks. Models are fitted by maximum likelihood. Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots. Random point patterns on a network can be generated using a variety of models.

Maintained by Adrian Baddeley. Last updated 2 months ago.

density-estimation heat-equation kernel-density-estimation network-analysis point-processes spatial-data-analysis statistical-analysis statistical-inference statistical-models

6 stars 9.58 score 35 scripts 42 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 12 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 stars 9.09 score 6 scripts 46 dependents

mayer79

confintr:Confidence Intervals

Calculates classic and/or bootstrap confidence intervals for many parameters such as the population mean, variance, interquartile range (IQR), median absolute deviation (MAD), skewness, kurtosis, Cramer's V, odds ratio, R-squared, quantiles (incl. median), proportions, different types of correlation measures, difference in means, quantiles and medians. Many of the classic confidence intervals are described in Smithson, M. (2003, ISBN: 978-0761924999). Bootstrap confidence intervals are calculated with the R package 'boot'. Both one- and two-sided intervals are supported.

Maintained by Michael Mayer. Last updated 8 months ago.

bootstrap confidence-intervals statistical-inference statistics

16 stars 8.62 score 104 scripts 17 dependents

benkeser

drtmle:Doubly-Robust Nonparametric Estimation and Inference

Targeted minimum loss-based estimators of counterfactual means and causal effects that are doubly-robust with respect both to consistency and asymptotic normality (Benkeser et al (2017), <doi:10.1093/biomet/asx053>; MJ van der Laan (2014), <doi:10.1515/ijb-2012-0038>).

Maintained by David Benkeser. Last updated 2 years ago.

causal-inference ensemble-learning iptw statistical-inference tmle

19 stars 6.89 score 90 scripts 1 dependents

bdwilliamson

vimp:Perform Inference on Algorithm-Agnostic Variable Importance

Calculate point estimates of and valid confidence intervals for nonparametric, algorithm-agnostic variable importance measures in high and low dimensions, using flexible estimators of the underlying regression functions. For more information about the methods, please see Williamson et al. (Biometrics, 2020), Williamson et al. (JASA, 2021), and Williamson and Feng (ICML, 2020).

Maintained by Brian D. Williamson. Last updated 2 months ago.

machine-learning nonparametric-statistics statistical-inference variable-importance

23 stars 6.79 score 67 scripts

pavlakrotka

NCC:Simulation and Analysis of Platform Trials with Non-Concurrent Controls

Design and analysis of flexible platform trials with non-concurrent controls. Functions for data generation, analysis, visualization and running simulation studies are provided. The implemented analysis methods are described in: Bofill Roig et al. (2022) <doi:10.1186/s12874-022-01683-w>, Saville et al. (2022) <doi:10.1177/17407745221112013> and Schmidli et al. (2014) <doi:10.1111/biom.12242>.

Maintained by Pavla Krotka. Last updated 23 days ago.

clinical-trials platform-trials simulation statistical-inference jags cpp

5 stars 6.64 score 29 scripts

dylanb95

statespacer:State Space Modelling in 'R'

A tool that makes estimating models in state space form a breeze. See "Time Series Analysis by State Space Methods" by Durbin and Koopman (2012, ISBN: 978-0-19-964117-8) for details about the algorithms implemented.

Maintained by Dylan Beijers. Last updated 2 years ago.

cpp dynamic-linear-model forecasting gaussian-models kalman-filter mathematical-modelling state-space statistical-inference statistical-models structural-analysis time-series openblas cpp openmp

15 stars 6.14 score 37 scripts

fchamroukhi

samurais:Statistical Models for the Unsupervised Segmentation of Time-Series ('SaMUraiS')

Provides a variety of original and flexible user-friendly statistical latent variable models and unsupervised learning algorithms to segment and represent time-series data (univariate or multivariate), and more generally, longitudinal data, which include regime changes. 'samurais' is built upon the following packages, each of them is an autonomous time-series segmentation approach: Regression with Hidden Logistic Process ('RHLP'), Hidden Markov Model Regression ('HMMR'), Multivariate 'RHLP' ('MRHLP'), Multivariate 'HMMR' ('MHMMR'), Piece-Wise regression ('PWR'). For the advantages/differences of each of them, the user is referred to our mentioned paper references. These models are originally introduced and written in 'Matlab' by Faicel Chamroukhi <https://github.com/fchamroukhi?&tab=repositories&q=time-series&type=public&language=matlab>.

Maintained by Florian Lecocq. Last updated 5 years ago.

artificial-intelligence change-point-detection data-science dynamic-programming em-algorithm hidden-markov-models hidden-process-regression human-activity-recognition latent-variable-models model-selection multivariate-timeseries newton-raphson piecewise-regression statistical-inference statistical-learning time-series-analysis time-series-clustering openblas cpp

11 stars 6.14 score 28 scripts

paulnorthrop

chandwich:Chandler-Bate Sandwich Loglikelihood Adjustment

Performs adjustments of a user-supplied independence loglikelihood function using a robust sandwich estimator of the parameter covariance matrix, based on the methodology in Chandler and Bate (2007) <doi:10.1093/biomet/asm015>. This can be used for cluster correlated data when interest lies in the parameters of the marginal distributions or for performing inferences that are robust to certain types of model misspecification. Functions for profiling the adjusted loglikelihoods are also provided, as are functions for calculating and plotting confidence intervals, for single model parameters, and confidence regions, for pairs of model parameters. Nested models can be compared using an adjusted likelihood ratio test.

Maintained by Paul J. Northrop. Last updated 2 years ago.

clustered-data clusters composite-likelihood independence-loglikelihood mle robust sandwich statistical-inference

4 stars 5.88 score 18 scripts 7 dependents

fchamroukhi

meteorits:Mixture-of-Experts Modeling for Complex Non-Normal Distributions

Provides a unified mixture-of-experts (ME) modeling and estimation framework with several original and flexible ME models to model, cluster and classify heterogeneous data in many complex situations where the data are distributed according to non-normal, possibly skewed distributions, and when they might be corrupted by atypical observations. Mixtures-of-Experts models for complex and non-normal distributions ('meteorits') are originally introduced and written in 'Matlab' by Faicel Chamroukhi. The references are mainly the following ones. The references are mainly the following ones. Chamroukhi F., Same A., Govaert, G. and Aknin P. (2009) <doi:10.1016/j.neunet.2009.06.040>. Chamroukhi F. (2010) <https://chamroukhi.com/FChamroukhi-PhD.pdf>. Chamroukhi F. (2015) <arXiv:1506.06707>. Chamroukhi F. (2015) <https://chamroukhi.com/FChamroukhi-HDR.pdf>. Chamroukhi F. (2016) <doi:10.1109/IJCNN.2016.7727580>. Chamroukhi F. (2016) <doi:10.1016/j.neunet.2016.03.002>. Chamroukhi F. (2017) <doi:10.1016/j.neucom.2017.05.044>.

Maintained by Florian Lecocq. Last updated 5 years ago.

artificial-intelligence clustering em-algorithm mixture-of-experts neural-networks non-linear-regression prediction robust-learning skew-normal skew-t skewed-data statistical-inference statistical-learning t-distribution unsupervised-learning openblas cpp

3 stars 5.12 score 11 scripts

fchamroukhi

flamingos:Functional Latent Data Models for Clustering Heterogeneous Curves ('FLaMingos')

Provides a variety of original and flexible user-friendly statistical latent variable models for the simultaneous clustering and segmentation of heterogeneous functional data (i.e time series, or more generally longitudinal data, fitted by unsupervised algorithms, including EM algorithms. Functional Latent Data Models for Clustering heterogeneous curves ('FLaMingos') are originally introduced and written in 'Matlab' by Faicel Chamroukhi <https://github.com/fchamroukhi?utf8=?&tab=repositories&q=mix&type=public&language=matlab>. The references are mainly the following ones. Chamroukhi F. (2010) <https://chamroukhi.com/FChamroukhi-PhD.pdf>. Chamroukhi F., Same A., Govaert, G. and Aknin P. (2010) <doi:10.1016/j.neucom.2009.12.023>. Chamroukhi F., Same A., Aknin P. and Govaert G. (2011) <doi:10.1109/IJCNN.2011.6033590>. Same A., Chamroukhi F., Govaert G. and Aknin, P. (2011) <doi:10.1007/s11634-011-0096-5>. Chamroukhi F., and Glotin H. (2012) <doi:10.1109/IJCNN.2012.6252818>. Chamroukhi F., Glotin H. and Same A. (2013) <doi:10.1016/j.neucom.2012.10.030>. Chamroukhi F. (2015) <https://chamroukhi.com/FChamroukhi-HDR.pdf>. Chamroukhi F. and Nguyen H-D. (2019) <doi:10.1002/widm.1298>.

Maintained by Florian Lecocq. Last updated 5 years ago.

artificial-intelligence baum-welch-algorithm curve-clustering data-science dynamic-programming em-algorithm functional-data-analysis functional-data-clustering hidden-markov-models hidden-process-regression mixture-models piecewise-regression statistical-analysis statistical-inference statistical-learning time-series-analysis unsupervised-learning openblas cpp

6 stars 4.95 score 9 scripts

kasselhingee

scorematchingad:Score Matching Estimation by Automatic Differentiation

Hyvärinen's score matching (Hyvärinen, 2005) <https://jmlr.org/papers/v6/hyvarinen05a.html> is a useful estimation technique when the normalising constant for a probability distribution is difficult to compute. This package implements score matching estimators using automatic differentiation in the 'CppAD' library <https://github.com/coin-or/CppAD> and is designed for quickly implementing score matching estimators for new models. Also available is general robustification (Windham, 1995) <https://www.jstor.org/stable/2346159>. Already in the package are estimators for directional distributions (Mardia, Kent and Laha, 2016) <doi:10.48550/arXiv.1604.08470> and the flexible Polynomially-Tilted Pairwise Interaction model for compositional data. The latter estimators perform well when there are zeros in the compositions (Scealy and Wood, 2023) <doi:10.1080/01621459.2021.2016422>, even many zeros (Scealy, Hingee, Kent, and Wood, 2024) <doi:10.1007/s11222-024-10412-w>. A partial interface to CppAD's ADFun objects is also available.

Maintained by Kassel Liam Hingee. Last updated 3 months ago.

automatic-differentiation score-matching statistical-inference cpp

3.98 score 1 scripts

fweber144

shinybrms:Graphical User Interface ('shiny' App) for 'brms'

A graphical user interface (GUI) for fitting Bayesian regression models using the package 'brms' which in turn relies on 'Stan' (<https://mc-stan.org/>). The 'shinybrms' GUI is a 'shiny' app.

Maintained by Frank Weber. Last updated 12 months ago.

bayes bayesian bayesian-data-analysis bayesian-inference bayesian-statistics brms cmdstanr gui mcmc rstan shiny shiny-app stan statistical-analysis statistical-inference statistical-models statistics

10 stars 3.70 score 3 scripts

sciviews

inferit:Hypothesis Tests and Statistical Distributions for 'SciViews::R'

Statistical distributions (including their visual representation) and hypothesis tests with rich-formatted tabular outputs for the 'SciViews::R' dialect.

Maintained by Philippe Grosjean. Last updated 10 months ago.

sciviews statistical-inference statistical-tests

3.00 score 6 scripts