Showing 36 of total 36 results (show query)
poissonconsulting
extras:Helper Functions for Bayesian Analyses
Functions to 'numericise' 'R' objects (coerce to numeric objects), summarise 'MCMC' (Monte Carlo Markov Chain) samples and calculate deviance residuals as well as 'R' translations of some 'BUGS' (Bayesian Using Gibbs Sampling), 'JAGS' (Just Another Gibbs Sampler), 'STAN' and 'TMB' (Template Model Builder) functions.
Maintained by Nicole Hill. Last updated 2 months ago.
48.6 match 9 stars 8.49 score 15 scripts 16 dependents
cran
sn:The Skew-Normal and Related Distributions Such as the Skew-t and the SUN
Build and manipulate probability distributions of the skew-normal family and some related ones, notably the skew-t and the SUN families. For the skew-normal and the skew-t distributions, statistical methods are provided for data fitting and model diagnostics, in the univariate and the multivariate case.
Maintained by Adelchi Azzalini. Last updated 2 years ago.
53.8 match 3 stars 7.44 score 92 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-intelligenceclusteringem-algorithmmixture-of-expertsneural-networksnon-linear-regressionpredictionrobust-learningskew-normalskew-tskewed-datastatistical-inferencestatistical-learningt-distributionunsupervised-learningopenblascpp
49.3 match 3 stars 5.12 score 11 scripts
sistm
NPflow:Bayesian Nonparametrics for Automatic Gating of Flow-Cytometry Data
Dirichlet process mixture of multivariate normal, skew normal or skew t-distributions modeling oriented towards flow-cytometry data preprocessing applications. Method is detailed in: Hejblum, Alkhassimn, Gottardo, Caron & Thiebaut (2019) <doi: 10.1214/18-AOAS1209>.
Maintained by Boris P Hejblum. Last updated 1 years ago.
51.9 match 4 stars 4.45 score 47 scripts 1 dependents
paul-buerkner
brms:Bayesian Regression Models using 'Stan'
Fit Bayesian generalized (non-)linear multivariate multilevel models using 'Stan' for full Bayesian inference. A wide range of distributions and link functions are supported, allowing users to fit -- among others -- linear, robust linear, count data, survival, response times, ordinal, zero-inflated, hurdle, and even self-defined mixture models all in a multilevel context. Further modeling options include both theory-driven and data-driven non-linear terms, auto-correlation structures, censoring and truncation, meta-analytic standard errors, and quite a few more. In addition, all parameters of the response distribution can be predicted in order to perform distributional regression. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their prior knowledge. Models can easily be evaluated and compared using several methods assessing posterior or prior predictions. References: Bürkner (2017) <doi:10.18637/jss.v080.i01>; Bürkner (2018) <doi:10.32614/RJ-2018-017>; Bürkner (2021) <doi:10.18637/jss.v100.i05>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.
Maintained by Paul-Christian Bürkner. Last updated 3 days ago.
bayesian-inferencebrmsmultilevel-modelsstanstatistical-models
9.8 match 1.3k stars 16.61 score 13k scripts 34 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 21 hours ago.
25.2 match 3.18 score 7 scripts 1 dependents
theussl
fMultivar:Rmetrics - Modeling of Multivariate Financial Return Distributions
A collection of functions inspired by Venables and Ripley (2002) <doi:10.1007/978-0-387-21706-2> and Azzalini and Capitanio (1999) <arXiv:0911.2093> to manage, investigate and analyze bivariate and multivariate data sets of financial returns.
Maintained by Stefan Theussl. Last updated 2 years ago.
18.6 match 3.69 score 99 scripts 7 dependents
chedgala
MomTrunc:Moments of Folded and Doubly Truncated Multivariate Distributions
It computes arbitrary products moments (mean vector and variance-covariance matrix), for some double truncated (and folded) multivariate distributions. These distributions belong to the family of selection elliptical distributions, which includes well known skewed distributions as the unified skew-t distribution (SUT) and its particular cases as the extended skew-t (EST), skew-t (ST) and the symmetric student-t (T) distribution. Analogous normal cases unified skew-normal (SUN), extended skew-normal (ESN), skew-normal (SN), and symmetric normal (N) are also included. Density, probabilities and random deviates are also offered for these members.
Maintained by Christian E. Galarza. Last updated 5 months ago.
generation-algorithmsmomentsprobability-statisticsopenblascpp
14.0 match 4.81 score 17 scripts 7 dependents
fernandalschumacher
skewlmm:Scale Mixture of Skew-Normal Linear Mixed Models
It fits scale mixture of skew-normal linear mixed models using either an expectation–maximization (EM) type algorithm or its accelerated version (Damped Anderson Acceleration with Epsilon Monotonicity, DAAREM), including some possibilities for modeling the within-subject dependence. Details can be found in Schumacher, Lachos and Matos (2021) <doi:10.1002/sim.8870>.
Maintained by Fernanda L. Schumacher. Last updated 2 months ago.
13.4 match 6 stars 4.43 score 10 scriptstingtingzhan
param2moment:Raw, Central and Standardized Moments of Parametric Distributions
To calculate the raw, central and standardized moments from distribution parameters. To solve the distribution parameters based on user-provided mean, standard deviation, skewness and kurtosis. Normal, skew-normal, skew-t and Tukey g-&-h distributions are supported, for now.
Maintained by Tingting Zhan. Last updated 21 hours ago.
14.8 match 3.48 score 2 dependents
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.
14.9 match 3.30 score 1 scripts
lbenitesanchez
ssmsn:Scale-Shape Mixtures of Skew-Normal Distributions
It provides the density and random number generator for the Scale-Shape Mixtures of Skew-Normal Distributions proposed by Jamalizadeh and Lin (2016) <doi:10.1007/s00180-016-0691-1>.
Maintained by Luis Benites. Last updated 8 years ago.
13.9 match 2 stars 3.00 score 2 scripts
ericarcher
swfscMisc:Miscellaneous Functions for Southwest Fisheries Science Center
Collection of conversion, analytical, geodesic, mapping, and plotting functions. Used to support packages and code written by researchers at the Southwest Fisheries Science Center of the National Oceanic and Atmospheric Administration.
Maintained by Eric Archer. Last updated 11 months ago.
6.0 match 2 stars 6.18 score 101 scripts 20 dependents
programgirl
PopulateR:Create Data Frames for the Micro-Simulation of Human Populations
Tools for constructing detailed synthetic human populations from frequency tables. Add ages based on age groups and sex, create households, add students to education facilities, create employers, add employers to employees, and create interpersonal networks.
Maintained by Michelle Gosse. Last updated 1 months ago.
8.6 match 1 stars 3.88 score
cran
TSMSN:Truncated Scale Mixtures of Skew-Normal Distributions
Return the first four moments, estimation of parameters and sample of the TSMSN distributions (Skew Normal, Skew t, Skew Slash or Skew Contaminated Normal).
Maintained by Eraldo B. dos Anjos Filho. Last updated 6 years ago.
18.0 match 1.00 score
elizagestrada
goft:Tests of Fit for some Probability Distributions
Goodness-of-fit tests for skew-normal, gamma, inverse Gaussian, log-normal, 'Weibull', 'Frechet', Gumbel, normal, multivariate normal, Cauchy, Laplace or double exponential, exponential and generalized Pareto distributions. Parameter estimators for gamma, inverse Gaussian and generalized Pareto distributions.
Maintained by Elizabeth Gonzalez-Estrada. Last updated 5 years ago.
9.6 match 1.86 score 72 scriptscran
bssn:Birnbaum-Saunders Model
It provides the density, distribution function, quantile function, random number generator, reliability function, failure rate, likelihood function, moments and EM algorithm for Maximum Likelihood estimators, also empirical quantile and generated envelope for a given sample, all this for the three parameter Birnbaum-Saunders model based on Skew-Normal Distribution. Also, it provides the random number generator for the mixture of Birnbaum-Saunders model based on Skew-Normal distribution. Additionally, we incorporate the EM algorithm based on the assumption that the error term follows a finite mixture of Sinh-normal distributions.
Maintained by Rocio Paola Maehara. Last updated 5 years ago.
16.8 match 1.00 score 5 scriptscran
distdichoR:Distributional Method for the Dichotomisation of Continuous Outcomes
Contains a range of functions covering the present development of the distributional method for the dichotomisation of continuous outcomes. The method provides estimates with standard error of a comparison of proportions (difference, odds ratio and risk ratio) derived, with similar precision, from a comparison of means. See the URL below or <arXiv:1809.03279> for more information.
Maintained by Odile Sauzet. Last updated 6 years ago.
15.9 match 1.00 score
plambertuliege
ordgam:Additive Model for Ordinal Data using Laplace P-Splines
Additive proportional odds model for ordinal data using Laplace P-splines. The combination of Laplace approximations and P-splines enable fast and flexible inference in a Bayesian framework. Specific approximations are proposed to account for the asymmetry in the marginal posterior distributions of non-penalized parameters. For more details, see Lambert and Gressani (2023) <doi:10.1177/1471082X231181173> ; Preprint: <arXiv:2210.01668>).
Maintained by Philippe Lambert. Last updated 2 years ago.
5.1 match 3.02 score 21 scripts
oswaldogressani
blapsr:Bayesian Inference with Laplace Approximations and P-Splines
Laplace approximations and penalized B-splines are combined for fast Bayesian inference in latent Gaussian models. The routines can be used to fit survival models, especially proportional hazards and promotion time cure models (Gressani, O. and Lambert, P. (2018) <doi:10.1016/j.csda.2018.02.007>). The Laplace-P-spline methodology can also be implemented for inference in (generalized) additive models (Gressani, O. and Lambert, P. (2021) <doi:10.1016/j.csda.2020.107088>). See the associated website for more information and examples.
Maintained by Oswaldo Gressani. Last updated 3 years ago.
3.5 match 5 stars 4.40 score 4 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.
3.2 match 1 stars 4.46 score 29 scriptscran
mvst:Bayesian Inference for the Multivariate Skew-t Model
Estimates the multivariate skew-t and nested models, as described in the articles Liseo, B., Parisi, A. (2013). Bayesian inference for the multivariate skew-normal model: a population Monte Carlo approach. Comput. Statist. Data Anal. <doi:10.1016/j.csda.2013.02.007> and in Parisi, A., Liseo, B. (2017). Objective Bayesian analysis for the multivariate skew-t model. Statistical Methods & Applications <doi: 10.1007/s10260-017-0404-0>.
Maintained by Antonio Parisi. Last updated 1 years ago.
11.3 match 1.11 score 13 scriptscran
skewMLRM:Estimation for Scale-Shape Mixtures of Skew-Normal Distributions
Provide data generation and estimation tools for the multivariate scale mixtures of normal presented in Lange and Sinsheimer (1993) <doi:10.2307/1390698>, the multivariate scale mixtures of skew-normal presented in Zeller, Lachos and Vilca (2011) <doi:10.1080/02664760903406504>, the multivariate skew scale mixtures of normal presented in Louredo, Zeller and Ferreira (2021) <doi:10.1007/s13571-021-00257-y> and the multivariate scale mixtures of skew-normal-Cauchy presented in Kahrari et al. (2020) <doi:10.1080/03610918.2020.1804582>.
Maintained by Diego Gallardo. Last updated 3 years ago.
7.6 match 1.48 score 1 dependents
takayukikawa
snem:EM Algorithm for Multivariate Skew-Normal Distribution with Overparametrization
Efficient estimation of multivariate skew-normal distribution in closed form.
Maintained by Takayuki Kawashima. Last updated 5 years ago.
10.3 match 1.00 score 1 scripts
rtrepos
WACS:Multivariate Weather-State Approach Conditionally Skew-Normal Generator
A multivariate weather generator for daily climate variables based on weather-states (Flecher et al. (2010) <doi:10.1029/2009WR008098>). It uses a Markov chain for modeling the succession of weather states. Conditionally to the weather states, the multivariate variables are modeled using the family of Complete Skew-Normal distributions. Parameters are estimated on measured series. Must include the variable 'Rain' and can accept as many other variables as desired.
Maintained by Ronan Trépos. Last updated 5 years ago.
10.0 match 1.00 score 8 scriptscran
nlsmsn:Fitting Nonlinear Models with Scale Mixture of Skew-Normal Distributions
Fit univariate non-linear scale mixture of skew-normal(NL-SMSN) regression, details in Garay, Lachos and Abanto-Valle (2011) <doi:10.1016/j.jkss.2010.08.003> and Lachos, Bandyopadhyay and Garay (2011) <doi:10.1016/j.spl.2011.03.019>.
Maintained by Marcos Prates. Last updated 4 years ago.
6.7 match 2 stars 1.30 score 3 scriptscran
csn:Closed Skew-Normal Distribution
Provides functions for computing the density and the log-likelihood function of closed-skew normal variates, and for generating random vectors sampled from this distribution. See Gonzalez-Farias, G., Dominguez-Molina, J., and Gupta, A. (2004). The closed skew normal distribution, Skew-elliptical distributions and their applications: a journey beyond normality, Chapman and Hall/CRC, Boca Raton, FL, pp. 25-42.
Maintained by Dmitry Pavlyuk. Last updated 10 years ago.
8.1 match 1.00 scorecran
mixsmsn:Fitting Finite Mixture of Scale Mixture of Skew-Normal Distributions
Functions to fit finite mixture of scale mixture of skew-normal (FM-SMSN) distributions, details in Prates, Lachos and Cabral (2013) <doi: 10.18637/jss.v054.i12>, Cabral, Lachos and Prates (2012) <doi:10.1016/j.csda.2011.06.026> and Basso, Lachos, Cabral and Ghosh (2010) <doi:10.1016/j.csda.2009.09.031>.
Maintained by Marcos Prates. Last updated 3 years ago.
6.6 match 1 stars 1.00 scorecran
NPCirc:Nonparametric Circular Methods
Nonparametric smoothing methods for density and regression estimation involving circular data, including the estimation of the mean regression function and other conditional characteristics.
Maintained by Maria Alonso-Pena. Last updated 2 years ago.
3.6 match 1.78 score 2 dependents
franciscoalencar
CensMFM:Finite Mixture of Multivariate Censored/Missing Data
It fits finite mixture models for censored or/and missing data using several multivariate distributions. Point estimation and asymptotic inference (via empirical information matrix) are offered as well as censored data generation. Pairwise scatter and contour plots can be generated. Possible multivariate distributions are the well-known normal, Student-t and skew-normal distributions. This package is an complement of Lachos, V. H., Moreno, E. J. L., Chen, K. & Cabral, C. R. B. (2017) <doi:10.1016/j.jmva.2017.05.005> for the multivariate skew-normal case.
Maintained by Francisco H. C. de Alencar. Last updated 10 months ago.
6.3 match 1.00 score
apedrods
MAINT.Data:Model and Analyse Interval Data
Implements methodologies for modelling interval data by Normal and Skew-Normal distributions, considering appropriate parameterizations of the variance-covariance matrix that takes into account the intrinsic nature of interval data, and lead to four different possible configuration structures. The Skew-Normal parameters can be estimated by maximum likelihood, while Normal parameters may be estimated by maximum likelihood or robust trimmed maximum likelihood methods.
Maintained by Pedro Duarte Silva. Last updated 2 years ago.
5.3 match 1.15 score 14 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.
1.0 match 2.73 score 36 scripts 1 dependentscran
abms:Augmented Bayesian Model Selection for Regression Models
Tools to perform model selection alongside estimation under Linear, Logistic, Negative binomial, Quantile, and Skew-Normal regression. Under the spike-and-slab method, a probability for each possible model is estimated with the posterior mean, credibility interval, and standard deviation of coefficients and parameters under the most probable model.
Maintained by Francisco Segovia. Last updated 3 days ago.
1.0 match 1.00 score