Showing 38 of total 38 results (show query)
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.
210.6 match 16 stars 11.23 score 2.0k scripts 49 dependentsgamlss-dev
gamlss.dist:Distributions for Generalized Additive Models for Location Scale and Shape
A set of distributions which can be used for modelling the response variables in Generalized Additive Models for Location Scale and Shape, Rigby and Stasinopoulos (2005), <doi:10.1111/j.1467-9876.2005.00510.x>. The distributions can be continuous, discrete or mixed distributions. Extra distributions can be created, by transforming, any continuous distribution defined on the real line, to a distribution defined on ranges 0 to infinity or 0 to 1, by using a 'log' or a 'logit' transformation respectively.
Maintained by Mikis Stasinopoulos. Last updated 21 days ago.
195.2 match 4 stars 10.50 score 346 scripts 71 dependents
mstasinopoulos
gamlss.data:Data for Generalised Additive Models for Location Scale and Shape
Data used as examples in the current two books on Generalised Additive Models for Location Scale and Shape introduced by Rigby and Stasinopoulos (2005), <doi:10.1111/j.1467-9876.2005.00510.x>.
Maintained by Mikis Stasinopoulos. Last updated 1 years ago.
82.1 match 7.04 score 108 scripts 49 dependentsgamlss-dev
gamlss.add:Extra Additive Terms for Generalized Additive Models for Location Scale and Shape
Interface for extra smooth functions including tensor products, neural networks and decision trees.
Maintained by Mikis Stasinopoulos. Last updated 12 months ago.
72.5 match 4.98 score 64 scripts 1 dependentsgamlss-dev
gamlss.ggplots:Plotting Functions for Generalized Additive Model for Location Scale and Shape
Functions for plotting Generalized Additive Models for Location Scale and Shape from the 'gamlss' package, Stasinopoulos and Rigby (2007) <doi:10.18637/jss.v023.i07>, using the graphical methods from 'ggplot2'.
Maintained by Mikis Stasinopoulos. Last updated 3 months ago.
63.9 match 2 stars 5.58 score 24 scripts
boost-r
gamboostLSS:Boosting Methods for 'GAMLSS'
Boosting models for fitting generalized additive models for location, shape and scale ('GAMLSS') to potentially high dimensional data.
Maintained by Benjamin Hofner. Last updated 19 days ago.
boosting-algorithmsgamboostlssgamlssmachine-learningr-languagevariable-selection
24.9 match 26 stars 8.52 score 163 scripts 1 dependentsgamlss-dev
gamlss.spatial:Spatial Terms in Generalized Additive Models for Location Scale and Shape
The packages enables fitting Gaussian Markov Random Fields within the Generalized Additive Models for Location Scale and Shape algorithms.
Maintained by Fernanda De Bastiani. Last updated 1 years ago.
56.5 match 3.74 score 11 scriptsmstasinopoulos
gamlss.tr:Generating and Fitting Truncated `gamlss.family' Distributions
This is an add on package to GAMLSS. The purpose of this package is to allow users to defined truncated distributions in GAMLSS models. The main function gen.trun() generates truncated version of an existing GAMLSS family distribution.
Maintained by Mikis Stasinopoulos. Last updated 1 years ago.
53.5 match 3.68 score 76 scripts 2 dependentsgamlss-dev
gamlss2:GAMLSS Infrastructure for Flexible Distributional Regression
Next generation infrastructure for generalized additive models for location, scale, and shape (GAMLSS) and distributional regression more generally. The package provides a fresh reimplementaton of the classic 'gamlss' package while being more modular and facilitating the creation of advanced terms and models.
Maintained by Nikolaus Umlauf. Last updated 18 days ago.
32.6 match 7 stars 5.23 score 4 scripts 1 dependents
cran
gamlss.countKinf:Generating and Fitting K-Inflated 'discrete gamlss.family' Distributions
This is an add on package to 'GAMLSS'. The main purpose of this package is generating and fitting inflated distributions at any desired point (0, 1, 2, ...). The function gen.Kinf() generates K-inflated version of an existing discrete 'GAMLSS' family distribution.
Maintained by Saeed Mohammadpour. Last updated 6 years ago.
71.6 match 2.26 score 18 scriptsmstasinopoulos
gamlss.cens:Fitting an Interval Response Variable Using `gamlss.family' Distributions
This is an add-on package to GAMLSS. The purpose of this package is to allow users to fit interval response variables in GAMLSS models. The main function gen.cens() generates a censored version of an existing GAMLSS family distribution.
Maintained by Mikis Stasinopoulos. Last updated 1 years ago.
51.4 match 2.98 score 32 scripts 1 dependentscran
gamlss.foreach:Parallel Computations for Distributional Regression
Computational intensive calculations for Generalized Additive Models for Location Scale and Shape, <doi:10.1111/j.1467-9876.2005.00510.x>.
Maintained by Mikis Stasinopoulos. Last updated 3 years ago.
50.8 match 2.56 score 12 scripts 1 dependentsmstasinopoulos
gamlss.mx:Fitting Mixture Distributions with GAMLSS
The main purpose of this package is to allow fitting of mixture distributions with generalised additive models for location scale and shape models see Chapter 7 of Stasinopoulos et al. (2017) <doi:10.1201/b21973-4>.
Maintained by Mikis Stasinopoulos. Last updated 1 years ago.
53.1 match 2.41 score 26 scriptscran
gamlss.inf:Fitting Mixed (Inflated and Adjusted) Distributions
This is an add-on package to 'gamlss'. The purpose of this package is to allow users to fit GAMLSS (Generalised Additive Models for Location Scale and Shape) models when the response variable is defined either in the intervals [0,1), (0,1] and [0,1] (inflated at zero and/or one distributions), or in the positive real line including zero (zero-adjusted distributions). The mass points at zero and/or one are treated as extra parameters with the possibility to include a linear predictor for both. The package also allows transformed or truncated distributions from the GAMLSS family to be used for the continuous part of the distribution. Standard methods and GAMLSS diagnostics can be used with the resulting fitted object.
Maintained by Marco Enea. Last updated 6 years ago.
45.2 match 2.48 score 1 dependents
flziel
gamlss.lasso:Extra Lasso-Type Additive Terms for GAMLSS
Interface for extra high-dimensional smooth functions for Generalized Additive Models for Location Scale and Shape (GAMLSS) including (adaptive) lasso, ridge, elastic net and least angle regression.
Maintained by Florian Ziel. Last updated 4 years ago.
50.9 match 2.00 score 7 scriptscran
gamlss.demo:Demos for GAMLSS
Demos for smoothing and gamlss.family distributions.
Maintained by Mikis Stasinopoulos. Last updated 10 years ago.
49.6 match 1 stars 2.00 score
bioc
scDesign3:A unified framework of realistic in silico data generation and statistical model inference for single-cell and spatial omics
We present a statistical simulator, scDesign3, to generate realistic single-cell and spatial omics data, including various cell states, experimental designs, and feature modalities, by learning interpretable parameters from real data. Using a unified probabilistic model for single-cell and spatial omics data, scDesign3 infers biologically meaningful parameters; assesses the goodness-of-fit of inferred cell clusters, trajectories, and spatial locations; and generates in silico negative and positive controls for benchmarking computational tools.
Maintained by Dongyuan Song. Last updated 14 days ago.
softwaresinglecellsequencinggeneexpressionspatial
10.5 match 89 stars 7.59 score 25 scripts
giampmarra
GJRM:Generalised Joint Regression Modelling
Routines for fitting various joint (and univariate) regression models, with several types of covariate effects, in the presence of equations' errors association, endogeneity, non-random sample selection or partial observability.
Maintained by Giampiero Marra. Last updated 5 months ago.
17.8 match 4 stars 4.10 score 67 scripts 5 dependentscran
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.
4.5 match 32 stars 12.71 score 17k scripts 7.8k dependents
stan125
distreg.vis:Framework for the Visualization of Distributional Regression Models
Functions for visualizing distributional regression models fitted using the 'gamlss', 'bamlss' or 'betareg' R package. The core of the package consists of a 'shiny' application, where the model results can be interactively explored and visualized.
Maintained by Stanislaus Stadlmann. Last updated 1 years ago.
bamlssdistributional-regressiongamlssshinyvisualization
11.5 match 3 stars 3.35 score 15 scripts
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 4 days ago.
distributional-regressiongamgammgeneralized-additive-mixed-modelsgeneralized-additive-modelsggplot2glmlmmgcvpenalized-splinerandom-effectssmoothingsplines
2.9 match 216 stars 12.68 score 1.6k scripts 1 dependentsgamlss-dev
WeatherGermany:Weather Data for Germany from the Deutscher Wetterdienst (DWD)
Weather data for all stations in Germany as provided by the Deutscher Wetterdienst (DWD). The data is pre-processed and only the observations with the highest quality flags are provided. In addition, elevation data of Germany is provided as stars object.
Maintained by Nikolaus Umlauf. Last updated 1 months ago.
15.0 match 2.30 score
tjmahr
wisclabmisc:Tools to Support the 'WiscLab'
A collection of 'R' functions for use (and re-use) across 'WiscLab' projects. These are analysis or presentation oriented functions--that is, they are not for data reading or data cleaning.
Maintained by Tristan Mahr. Last updated 3 days ago.
8.2 match 3.95 score 4 scripts
dsalfran
ImputeRobust:Robust Multiple Imputation with Generalized Additive Models for Location Scale and Shape
Provides new imputation methods for the 'mice' package based on generalized additive models for location, scale, and shape (GAMLSS) as described in de Jong, van Buuren and Spiess <doi:10.1080/03610918.2014.911894>.
Maintained by Daniel Salfran. Last updated 6 years ago.
imputationmissing-datamultiple-imputation
8.8 match 9 stars 3.65 score 4 scripts
ropensci
gigs:Assess Fetal, Newborn, and Child Growth with International Standards
Convert between anthropometric measures and z-scores/centiles in multiple growth standards, and classify fetal, newborn, and child growth accordingly. With a simple interface to growth standards from the World Health Organisation and International Fetal and Newborn Growth Consortium for the 21st Century, gigs makes growth assessment easy and reproducible for clinicians, researchers and policy-makers.
Maintained by Simon R Parker. Last updated 25 days ago.
anthropometrygrowth-standardsintergrowthwho
6.8 match 4 stars 4.38 score 8 scripts
bbolker
broom.mixed:Tidying Methods for Mixed Models
Convert fitted objects from various R mixed-model packages into tidy data frames along the lines of the 'broom' package. The package provides three S3 generics for each model: tidy(), which summarizes a model's statistical findings such as coefficients of a regression; augment(), which adds columns to the original data such as predictions, residuals and cluster assignments; and glance(), which provides a one-row summary of model-level statistics.
Maintained by Ben Bolker. Last updated 3 months ago.
1.9 match 231 stars 15.22 score 4.0k scripts 37 dependents
leifeld
texreg:Conversion of R Regression Output to LaTeX or HTML Tables
Converts coefficients, standard errors, significance stars, and goodness-of-fit statistics of statistical models into LaTeX tables or HTML tables/MS Word documents or to nicely formatted screen output for the R console for easy model comparison. A list of several models can be combined in a single table. The output is highly customizable. New model types can be easily implemented. Details can be found in Leifeld (2013), JStatSoft <doi:10.18637/jss.v055.i08>.)
Maintained by Philip Leifeld. Last updated 2 months ago.
html-tableslatexlatex-tablesregressionreportingtabletexreg
1.9 match 113 stars 14.09 score 1.8k scripts 67 dependents
rvlenth
emmeans:Estimated Marginal Means, aka Least-Squares Means
Obtain estimated marginal means (EMMs) for many linear, generalized linear, and mixed models. Compute contrasts or linear functions of EMMs, trends, and comparisons of slopes. Plots and other displays. Least-squares means are discussed, and the term "estimated marginal means" is suggested, in Searle, Speed, and Milliken (1980) Population marginal means in the linear model: An alternative to least squares means, The American Statistician 34(4), 216-221 <doi:10.1080/00031305.1980.10483031>.
Maintained by Russell V. Lenth. Last updated 2 days ago.
1.3 match 377 stars 19.19 score 13k scripts 187 dependents
paulnorthrop
gamlssx:Generalized Additive Extreme Value Models for Location, Scale and Shape
Fits generalized additive models for the location, scale and shape parameters of a generalized extreme value response distribution. The methodology is based on Rigby, R.A. and Stasinopoulos, D.M. (2005), <doi:10.1111/j.1467-9876.2005.00510.x> and implemented using functions from the 'gamlss' package <doi:10.32614/CRAN.package.gamlss>.
Maintained by Paul J. Northrop. Last updated 15 days ago.
extreme-value-statisticsextremesgeneralized-additive-modelsregressionregression-analysis
4.3 match 3 stars 4.18 score 3 scripts
zheng206
ComBatFamQC:Comprehensive Batch Effect Diagnostics and Harmonization
Provides a comprehensive framework for batch effect diagnostics, harmonization, and post-harmonization downstream analysis. Features include interactive visualization tools, robust statistical tests, and a range of harmonization techniques. Additionally, 'ComBatFamQC' enables the creation of life-span age trend plots with estimated age-adjusted centiles and facilitates the generation of covariate-corrected residuals for analytical purposes. Methods for harmonization are based on approaches described in Johnson et al., (2007) <doi:10.1093/biostatistics/kxj037>, Beer et al., (2020) <doi:10.1016/j.neuroimage.2020.117129>, Pomponio et al., (2020) <doi:10.1016/j.neuroimage.2019.116450>, and Chen et al., (2021) <doi:10.1002/hbm.25688>.
Maintained by Zheng Ren. Last updated 2 months ago.
diagnostic-toolharmonizationrshinyapp
2.9 match 2 stars 5.35 score 16 scripts
nhanhocu
metamicrobiomeR:an R package for analysis of microbiome relative abundance data using zero inflated beta GAMLSS and meta-analysis across studies using random effect model
The metamicrobiomeR package implements Generalized Additive Model for Location, Scale and Shape (GAMLSS) with zero inflated beta (BEZI) family for analysis of microbiome relative abundance data (with various options for data transformation/normalization to address compositional effects) and random effect meta-analysis models for meta-analysis pooling estimates across microbiome studies. Random Forest model to predict microbiome age based on relative abundances of shared bacterial genera with the Bangladesh data (Subramanian et al 2014), comparison of multiple diversity indexes using linear/linear mixed effect models and some data display/visualization are also implemented.
Maintained by Nhan Ho. Last updated 4 years ago.
3.1 match 33 stars 4.90 score 12 scriptsboost-r
FDboost:Boosting Functional Regression Models
Regression models for functional data, i.e., scalar-on-function, function-on-scalar and function-on-function regression models, are fitted by a component-wise gradient boosting algorithm. For a manual on how to use 'FDboost', see Brockhaus, Ruegamer, Greven (2017) <doi:10.18637/jss.v094.i10>.
Maintained by David Ruegamer. Last updated 3 months ago.
boostingboosting-algorithmsfunction-on-function-regressionfunction-on-scalar-regressionmachine-learningscalar-on-function-regressionvariable-selection
1.8 match 17 stars 8.00 score 98 scripts
stefvanbuuren
AGD:Analysis of Growth Data
Tools for the analysis of growth data: to extract an LMS table from a gamlss object, to calculate the standard deviation scores and its inverse, and to superpose two wormplots from different models. The package contains a some varieties of reference tables, especially for The Netherlands.
Maintained by Stef van Buuren. Last updated 11 months ago.
anthropometrycdcdutchgrowthgrowth-chartslmswhoz-score
2.3 match 1 stars 4.38 score 48 scripts
freezenik
bamlss:Bayesian Additive Models for Location, Scale, and Shape (and Beyond)
Infrastructure for estimating probabilistic distributional regression models in a Bayesian framework. The distribution parameters may capture location, scale, shape, etc. and every parameter may depend on complex additive terms (fixed, random, smooth, spatial, etc.) similar to a generalized additive model. The conceptual and computational framework is introduced in Umlauf, Klein, Zeileis (2019) <doi:10.1080/10618600.2017.1407325> and the R package in Umlauf, Klein, Simon, Zeileis (2021) <doi:10.18637/jss.v100.i04>.
Maintained by Nikolaus Umlauf. Last updated 5 months ago.
1.8 match 1 stars 5.76 score 239 scripts 5 dependentscran
BSagri:Safety Assessment in Agricultural Field Trials
Collection of functions, data sets and code examples for evaluations of field trials with the objective of equivalence assessment.
Maintained by Frank Schaarschmidt. Last updated 7 years ago.
1.7 match 1 stars 2.00 score 33 scripts
ousuga
RelDists:Estimation for some Reliability Distributions
Parameters estimation and linear regression models for Reliability distributions families reviewed by Almalki & Nadarajah (2014) <doi:10.1016/j.ress.2013.11.010> using Generalized Additive Models for Location, Scale and Shape, aka GAMLSS by Rigby & Stasinopoulos (2005) <doi:10.1111/j.1467-9876.2005.00510.x>.
Maintained by Jaime Mosquera. Last updated 8 days ago.
0.5 match 3 stars 5.76 score 19 scripts
fhernanb
DiscreteDists:Discrete Statistical Distributions
Implementation of new discrete statistical distributions. Each distribution includes the traditional functions as well as an additional function called the family function, which can be used to estimate parameters within the 'gamlss' framework.
Maintained by Freddy Hernandez-Barajas. Last updated 5 days ago.
0.5 match 3.81 score 1 scripts