Showing 17 of total 17 results (show query)
cran
flexmix:Flexible Mixture Modeling
A general framework for finite mixtures of regression models using the EM algorithm is implemented. The E-step and all data handling are provided, while the M-step can be supplied by the user to easily define new models. Existing drivers implement mixtures of standard linear models, generalized linear models and model-based clustering.
Maintained by Bettina Gruen. Last updated 30 days ago.
5 stars 8.42 score 113 dependentscran
boot:Bootstrap Functions (Originally by Angelo Canty for S)
Functions and datasets for bootstrapping from the book "Bootstrap Methods and Their Application" by A. C. Davison and D. V. Hinkley (1997, CUP), originally written by Angelo Canty for S.
Maintained by Alessandra R. Brazzale. Last updated 7 months ago.
2 stars 8.21 score 2.3k dependents
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.
24 stars 6.86 score 60 scripts
husson
SensoMineR:Sensory Data Analysis
Statistical Methods to Analyse Sensory Data. SensoMineR: A package for sensory data analysis. S. Le and F. Husson (2008).
Maintained by Francois Husson. Last updated 1 years ago.
5.72 score 108 scripts 3 dependents
albertofranzin
bnstruct:Bayesian Network Structure Learning from Data with Missing Values
Bayesian Network Structure Learning from Data with Missing Values. The package implements the Silander-Myllymaki complete search, the Max-Min Parents-and-Children, the Hill-Climbing, the Max-Min Hill-climbing heuristic searches, and the Structural Expectation-Maximization algorithm. Available scoring functions are BDeu, AIC, BIC. The package also implements methods for generating and using bootstrap samples, imputed data, inference.
Maintained by Alberto Franzin. Last updated 1 years ago.
1 stars 5.40 score 111 scripts 3 dependents
hriebl
lmls:Gaussian Location-Scale Regression
The Gaussian location-scale regression model is a multi-predictor model with explanatory variables for the mean (= location) and the standard deviation (= scale) of a response variable. This package implements maximum likelihood and Markov chain Monte Carlo (MCMC) inference (using algorithms from Girolami and Calderhead (2011) <doi:10.1111/j.1467-9868.2010.00765.x> and Nesterov (2009) <doi:10.1007/s10107-007-0149-x>), a parametric bootstrap algorithm, and diagnostic plots for the model class.
Maintained by Hannes Riebl. Last updated 5 months ago.
3 stars 4.65 score 15 scripts
marco-geraci
lqmm:Linear Quantile Mixed Models
Functions to fit quantile regression models for hierarchical data (2-level nested designs) as described in Geraci and Bottai (2014, Statistics and Computing) <doi:10.1007/s11222-013-9381-9>. A vignette is given in Geraci (2014, Journal of Statistical Software) <doi:10.18637/jss.v057.i13> and included in the package documents. The packages also provides functions to fit quantile models for independent data and for count responses.
Maintained by Marco Geraci. Last updated 3 years ago.
4.38 score 75 scripts 5 dependents
wahani
saeRobust:Robust Small Area Estimation
Methods to fit robust alternatives to commonly used models used in Small Area Estimation. The methods here used are based on best linear unbiased predictions and linear mixed models. At this time available models include area level models incorporating spatial and temporal correlation in the random effects.
Maintained by Sebastian Warnholz. Last updated 1 years ago.
1 stars 4.03 score 12 scripts 3 dependents
stephenrho
pmcalibration:Calibration Curves for Clinical Prediction Models
Fit calibrations curves for clinical prediction models and calculate several associated metrics (Eavg, E50, E90, Emax). Ideally predicted probabilities from a prediction model should align with observed probabilities. Calibration curves relate predicted probabilities (or a transformation thereof) to observed outcomes via a flexible non-linear smoothing function. 'pmcalibration' allows users to choose between several smoothers (regression splines, generalized additive models/GAMs, lowess, loess). Both binary and time-to-event outcomes are supported. See Van Calster et al. (2016) <doi:10.1016/j.jclinepi.2015.12.005>; Austin and Steyerberg (2019) <doi:10.1002/sim.8281>; Austin et al. (2020) <doi:10.1002/sim.8570>.
Maintained by Stephen Rhodes. Last updated 1 months ago.
3.91 score 54 scripts 1 dependents
aloy
CarletonStats:Functions for Statistics Classes at Carleton College
Includes commands for bootstrapping and permutation tests, a command for created grouped bar plots, and a demo of the quantile-normal plot for data drawn from different distributions.
Maintained by Adam Loy. Last updated 7 months ago.
3.81 score 65 scriptscran
econet:Estimation of Parameter-Dependent Network Centrality Measures
Provides methods for estimating parameter-dependent network centrality measures with linear-in-means models. Both non linear least squares and maximum likelihood estimators are implemented. The methods allow for both link and node heterogeneity in network effects, endogenous network formation and the presence of unconnected nodes. The routines also compare the explanatory power of parameter-dependent network centrality measures with those of standard measures of network centrality. Benefits and features of the 'econet' package are illustrated using data from Battaglini and Patacchini (2018) and Battaglini, Patacchini, and Leone Sciabolazza (2020). For additional details, see the vignette <doi:10.18637/jss.v102.i08>.
Maintained by Valerio Leone Sciabolazza. Last updated 8 months ago.
1 stars 2.70 score
nagodem
rebmix:Finite Mixture Modeling, Clustering & Classification
Random univariate and multivariate finite mixture model generation, estimation, clustering, latent class analysis and classification. Variables can be continuous, discrete, independent or dependent and may follow normal, lognormal, Weibull, gamma, Gumbel, binomial, Poisson, Dirac, uniform or circular von Mises parametric families.
Maintained by Marko Nagode. Last updated 9 months ago.
1 stars 2.66 score 43 scripts
glamb85
qcpm:Quantile Composite Path Modeling
Implements the Quantile Composite-based Path Modeling approach (Davino and Vinzi, 2016 <doi:10.1007/s11634-015-0231-9>; Dolce et al., 2021 <doi:10.1007/s11634-021-00469-0>). The method complements the traditional PLS Path Modeling approach, analyzing the entire distribution of outcome variables and, therefore, overcoming the classical exploration of only average effects. It exploits quantile regression to investigate changes in the relationships among constructs and between constructs and observed variables.
Maintained by Giuseppe Lamberti. Last updated 3 years ago.
2.00 score 9 scriptscran
eba:Elimination-by-Aspects Models
Fitting and testing multi-attribute probabilistic choice models, especially the Bradley-Terry-Luce (BTL) model (Bradley & Terry, 1952 <doi:10.1093/biomet/39.3-4.324>; Luce, 1959), elimination-by-aspects (EBA) models (Tversky, 1972 <doi:10.1037/h0032955>), and preference tree (Pretree) models (Tversky & Sattath, 1979 <doi:10.1037/0033-295X.86.6.542>).
Maintained by Florian Wickelmaier. Last updated 4 years ago.
1.48 score 1 dependents
kdpeterson51
mlf:Machine Learning Foundations
Offers a gentle introduction to machine learning concepts for practitioners with a statistical pedigree: decomposition of model error (bias-variance trade-off), nonlinear correlations, information theory and functional permutation/bootstrap simulations. Székely GJ, Rizzo ML, Bakirov NK. (2007). <doi:10.1214/009053607000000505>. Reshef DN, Reshef YA, Finucane HK, Grossman SR, McVean G, Turnbaugh PJ, Lander ES, Mitzenmacher M, Sabeti PC. (2011). <doi:10.1126/science.1205438>.
Maintained by Kyle Peterson. Last updated 7 years ago.
1.08 score 12 scripts
bogdanpotanin
switchSelection:Endogenous Switching and Sample Selection Regression Models
Estimate the parameters of multivariate endogenous switching and sample selection models using methods described in Newey (2009) <doi:10.1111/j.1368-423X.2008.00263.x>, E. Kossova, B. Potanin (2018) <https://ideas.repec.org/a/ris/apltrx/0346.html>, E. Kossova, L. Kupriianova, B. Potanin (2020) <https://ideas.repec.org/a/ris/apltrx/0391.html> and E. Kossova, B. Potanin (2022) <https://ideas.repec.org/a/ris/apltrx/0455.html>.
Maintained by Bogdan Potanin. Last updated 6 months ago.
1 stars 1.00 score 1 scriptscran
npreg:Nonparametric Regression via Smoothing Splines
Multiple and generalized nonparametric regression using smoothing spline ANOVA models and generalized additive models, as described in Helwig (2020) <doi:10.4135/9781526421036885885>. Includes support for Gaussian and non-Gaussian responses, smoothers for multiple types of predictors (including random intercepts), interactions between smoothers of mixed types, eight different methods for smoothing parameter selection, and flexible tools for diagnostics, inference, and prediction.
Maintained by Nathaniel E. Helwig. Last updated 1 years ago.
1.00 score