R-universe search: topic:nonparametric

Showing 11 of total 11 results (show query)

ovvo-financial

NNS:Nonlinear Nonparametric Statistics

Nonlinear nonparametric statistics using partial moments. Partial moments are the elements of variance and asymptotically approximate the area of f(x). These robust statistics provide the basis for nonlinear analysis while retaining linear equivalences. NNS offers: Numerical integration, Numerical differentiation, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modeling, Normalization, Stochastic dominance and Advanced Monte Carlo sampling. All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995).

Maintained by Fred Viole. Last updated 5 days ago.

clustering econometrics machine-learning nonlinear nonparametric partial-moments statistics time-series cpp

72 stars 10.77 score 66 scripts 3 dependents

icasas

tvReg:Time-Varying Coefficient for Single and Multi-Equation Regressions

Fitting time-varying coefficient models for single and multi-equation regressions, using kernel smoothing techniques.

Maintained by Isabel Casas. Last updated 2 years ago.

autoregressive nonparametric regression sure vectorautoregressive

19 stars 6.25 score 62 scripts

sestelo

npregfast:Nonparametric Estimation of Regression Models with Factor-by-Curve Interactions

A method for obtaining nonparametric estimates of regression models with or without factor-by-curve interactions using local polynomial kernel smoothers or splines. Additionally, a parametric model (allometric model) can be estimated.

Maintained by Marta Sestelo. Last updated 3 months ago.

allometric barnacle critical-points curve-interactions factor-by-curve fortran interaction nonparametric regression-models testing

5 stars 5.73 score 89 scripts 2 dependents

mrcieu

bpbounds:Nonparametric Bounds for the Average Causal Effect Due to Balke and Pearl and Extensions

Implementation of the nonparametric bounds for the average causal effect under an instrumental variable model by Balke and Pearl (Bounds on Treatment Effects from Studies with Imperfect Compliance, JASA, 1997, 92, 439, 1171-1176, <doi:10.2307/2965583>). The package can calculate bounds for a binary outcome, a binary treatment/phenotype, and an instrument with either 2 or 3 categories. The package implements bounds for situations where these 3 variables are measured in the same dataset (trivariate data) or where the outcome and instrument are measured in one study and the treatment/phenotype and instrument are measured in another study (bivariate data).

Maintained by Tom Palmer. Last updated 2 days ago.

ace average-causal-effect bounds instrumental-variable iv mendelian mendelian-randomisation mendelian-randomization mendelianrandomisation mendelianrandomization mr nonparametric nonparametric-bounds pearl shiny

4.95 score 12 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

12 stars 4.78 score 5 scripts

mightymetrika

npboottprm:Nonparametric Bootstrap Test with Pooled Resampling

Addressing crucial research questions often necessitates a small sample size due to factors such as distinctive target populations, rarity of the event under study, time and cost constraints, ethical concerns, or group-level unit of analysis. Many readily available analytic methods, however, do not accommodate small sample sizes, and the choice of the best method can be unclear. The 'npboottprm' package enables the execution of nonparametric bootstrap tests with pooled resampling to help fill this gap. Grounded in the statistical methods for small sample size studies detailed in Dwivedi, Mallawaarachchi, and Alvarado (2017) <doi:10.1002/sim.7263>, the package facilitates a range of statistical tests, encompassing independent t-tests, paired t-tests, and one-way Analysis of Variance (ANOVA) F-tests. The nonparboot() function undertakes essential computations, yielding detailed outputs which include test statistics, effect sizes, confidence intervals, and bootstrap distributions. Further, 'npboottprm' incorporates an interactive 'shiny' web application, nonparboot_app(), offering intuitive, user-friendly data exploration.

Maintained by Mackson Ncube. Last updated 7 months ago.

datascience nonparametric statistics

1 stars 4.26 score 5 scripts 2 dependents

klebermsousa

jackstrap:Correcting Nonparametric Frontier Measurements for Outliers

Provides method used to check whether data have outlier in efficiency measurement of big samples with data envelopment analysis (DEA). In this jackstrap method, the package provides two criteria to define outliers: heaviside and k-s test. The technique was developed by Sousa and Stosic (2005) "Technical Efficiency of the Brazilian Municipalities: Correcting Nonparametric Frontier Measurements for Outliers." <doi:10.1007/s11123-005-4702-4>.

Maintained by Kleber Morais de Sousa. Last updated 5 years ago.

dea jackstrap nonparametric outlier-detection

1 stars 3.85 score 14 scripts

happma

pseudorank:Pseudo-Ranks

Efficient calculation of pseudo-ranks and (pseudo)-rank based test statistics. In case of equal sample sizes, pseudo-ranks and mid-ranks are equal. When used for inference mid-ranks may lead to paradoxical results. Pseudo-ranks are in general not affected by such a problem. See Happ et al. (2020, <doi:10.18637/jss.v095.c01>) for details.

Maintained by Martin Happ. Last updated 1 months ago.

cpp nonparametric nonparametric-statistics pseudo-rank pseudo-ranks rank rank-tests trend-test cpp

3 stars 3.71 score 17 scripts

euanmcgonigle

CptNonPar:Nonparametric Change Point Detection for Multivariate Time Series

Implements the nonparametric moving sum procedure for detecting changes in the joint characteristic function (NP-MOJO) for multiple change point detection in multivariate time series. See McGonigle, E. T., Cho, H. (2023) <doi:10.48550/arXiv.2305.07581> for description of the NP-MOJO methodology.

Maintained by Euan T. McGonigle. Last updated 11 months ago.

change-point-detection moving-sum nonparametric segmentation time-series cpp

4 stars 3.60 score 4 scripts

sestelo

FWDselect:Selecting Variables in Regression Models

A simple method to select the best model or best subset of variables using different types of data (binary, Gaussian or Poisson) and applying it in different contexts (parametric or non-parametric).

Maintained by Marta Sestelo. Last updated 9 years ago.

feature-engineering feature-selection machine-learning-algorithms nonparametric regresssion variable-importance variable-selection

2 stars 2.78 score 30 scripts

blakemoya

copre:Tools for Nonparametric Martingale Posterior Sampling

Performs Bayesian nonparametric density estimation using Martingale posterior distributions including the Copula Resampling (CopRe) algorithm. Also included are a Gibbs sampler for the marginal Gibbs-type mixture model and an extension to include full uncertainty quantification via a predictive sequence resampling (SeqRe) algorithm. The CopRe and SeqRe samplers generate random nonparametric distributions as output, leading to complete nonparametric inference on posterior summaries. Routines for calculating arbitrary functionals from the sampled distributions are included as well as an important algorithm for finding the number and location of modes, which can then be used to estimate the clusters in the data using, for example, k-means. Implements work developed in Moya B., Walker S. G. (2022). <doi:10.48550/arxiv.2206.08418>, Fong, E., Holmes, C., Walker, S. G. (2021) <doi:10.48550/arxiv.2103.15671>, and Escobar M. D., West, M. (1995) <doi:10.1080/01621459.1995.10476550>.

Maintained by Blake Moya. Last updated 11 months ago.

bayesian dirichlet-process nonparametric cpp openmp

1 stars 2.70 score 2 scripts