Showing 62 of total 62 results (show query)
lgnbhl
aos:Animate on Scroll Library for 'shiny'
Trigger animation effects on scroll on any HTML element of 'shiny' and 'rmarkdown', such as any text or plot, thanks to the 'AOS' Animate On Scroll jQuery library <http://michalsnik.github.io/aos/>.
Maintained by Félix Luginbuhl. Last updated 6 months ago.
72.6 match 7 stars 4.50 score 15 scripts
loelschlaeger
ao:Alternating Optimization
Alternating optimization is an iterative procedure that optimizes a function by alternately performing restricted optimization over individual parameter subsets. Instead of tackling joint optimization directly, it breaks the problem down into simpler sub-problems. This approach can make optimization feasible when joint optimization is too difficult.
Maintained by Lennart Oelschläger. Last updated 8 months ago.
59.6 match 2 stars 4.78 score 2 scripts
usaid-mozambique
sismar:Arrumar dados SISMA
Fornece um conjunto de funções para a criação de conjuntos de dados analíticos a partir de downloads do SISMA e DISA. Inclui funções que arrumam os ficheiros para um formato longo, removem variáveis desnecessárias, e criam colunas úteis para a análise.
Maintained by Joe Lara. Last updated 13 hours ago.
8.2 match 2 stars 5.24 score 9 scriptsusaid-mozambique
sonata:Interagir com MozART 2.0
Provides a set of utilities and functions for connecting, querying, and analyzing data from the Mozambique MozART 2.0 database.
Maintained by Joe Lara. Last updated 3 days ago.
6.8 match 4.08 score
cienciadedatos
dados:Translate Datasets to Portuguese
Este pacote traduz os seguintes conjuntos de dados: 'airlines', 'airports', 'ames_raw', 'AwardsManagers', 'babynames', 'Batting', 'diamonds', 'faithful', 'fueleconomy', 'Fielding', 'flights', 'gapminder', 'gss_cat', 'iris', 'Managers', 'mpg', 'mtcars', 'atmos', 'penguins', 'People, 'Pitching', 'pixarfilms','planes', 'presidential', 'table1', 'table2', 'table3', 'table4a', 'table4b', 'table5', 'vehicles', 'weather', 'who'. English: It provides a Portuguese translated version of the datasets listed above.
Maintained by Riva Quiroga. Last updated 7 months ago.
3.4 match 46 stars 7.13 score 266 scripts
ipeadata-lab
ipeaplot:Add Ipea Editorial Standards to 'ggplot2' Graphics
Convenient functions to create 'ggplot2' graphics following the editorial guidelines of the Institute for Applied Economic Research (Ipea).
Maintained by Pedro Ferreira. Last updated 20 days ago.
3.3 match 3 stars 6.49 score 17 scripts
cran
ExpImage:Analysis of Images in Experiments
Tools created for image analysis in researches. There are functions associated with image editing, segmentation, and obtaining biometric measurements (Este pacote foi idealizado para para a analise de imagens em pesquisas. Ha funcoes associadas a edicao de imagens, segmentacao, e obtencao de medidas biometricas) <https://www.expstat.com/pacotes-do-r/expimage>.
Maintained by Alcinei Mistico Azevedo. Last updated 10 months ago.
4.8 match 3.08 score
marco-geraci
Qtools:Utilities for Quantiles
Functions for unconditional and conditional quantiles. These include methods for transformation-based quantile regression, quantile-based measures of location, scale and shape, methods for quantiles of discrete variables, quantile-based multiple imputation, restricted quantile regression, directional quantile classification, and quantile ratio regression. A vignette is given in Geraci (2016, The R Journal) <doi:10.32614/RJ-2016-037> and included in the package.
Maintained by Marco Geraci. Last updated 1 years ago.
3.3 match 4.10 score 33 scripts 2 dependents
brunomssmelo
RcextTools:Analytical Procedures in Support of Brazilian Public Sector External Auditing
Set of analytical procedures based on advanced data analysis in support of Brazil's public sector external control activity.
Maintained by Bruno Melo. Last updated 8 years ago.
3.2 match 2.70 score 9 scripts
brunomioto
reservatoriosBR:Get Brazilian reservoirs data
Download all the historic data from Brazilian reservoir.
Maintained by Bruno Mioto. Last updated 3 years ago.
1.7 match 28 stars 3.15 score 8 scripts
pchausse
gmm:Generalized Method of Moments and Generalized Empirical Likelihood
It is a complete suite to estimate models based on moment conditions. It includes the two step Generalized method of moments (Hansen 1982; <doi:10.2307/1912775>), the iterated GMM and continuous updated estimator (Hansen, Eaton and Yaron 1996; <doi:10.2307/1392442>) and several methods that belong to the Generalized Empirical Likelihood family of estimators (Smith 1997; <doi:10.1111/j.0013-0133.1997.174.x>, Kitamura 1997; <doi:10.1214/aos/1069362388>, Newey and Smith 2004; <doi:10.1111/j.1468-0262.2004.00482.x>, and Anatolyev 2005 <doi:10.1111/j.1468-0262.2005.00601.x>).
Maintained by Pierre Chausse. Last updated 1 years ago.
0.5 match 2 stars 9.28 score 304 scripts 66 dependents
stephenmilborrow
earth:Multivariate Adaptive Regression Splines
Build regression models using the techniques in Friedman's papers "Fast MARS" and "Multivariate Adaptive Regression Splines" <doi:10.1214/aos/1176347963>. (The term "MARS" is trademarked and thus not used in the name of the package.)
Maintained by Stephen Milborrow. Last updated 5 months ago.
0.5 match 5 stars 8.40 score 3.9k scripts 26 dependents
jonasmoss
kdensity:Kernel Density Estimation with Parametric Starts and Asymmetric Kernels
Handles univariate non-parametric density estimation with parametric starts and asymmetric kernels in a simple and flexible way. Kernel density estimation with parametric starts involves fitting a parametric density to the data before making a correction with kernel density estimation, see Hjort & Glad (1995) <doi:10.1214/aos/1176324627>. Asymmetric kernels make kernel density estimation more efficient on bounded intervals such as (0, 1) and the positive half-line. Supported asymmetric kernels are the gamma kernel of Chen (2000) <doi:10.1023/A:1004165218295>, the beta kernel of Chen (1999) <doi:10.1016/S0167-9473(99)00010-9>, and the copula kernel of Jones & Henderson (2007) <doi:10.1093/biomet/asm068>. User-supplied kernels, parametric starts, and bandwidths are supported.
Maintained by Jonas Moss. Last updated 14 days ago.
asymmetric-kernelsdensity-estimationkernel-density-estimationnon-parametric
0.5 match 16 stars 6.87 score 153 scripts 1 dependents
iagomn
CustosAscensor:Costs Allocation for the Installation of an Elevator
Calculate the distribution of costs for the installation of an elevator based on the different distribution rules.
Maintained by Iago Montero Nunez. Last updated 5 years ago.
3.3 match 1.00 score
dhersz
aopint:Funções de Conveniência para o AOP
Funções de conveniência para agilizar a vida dos membros do AOP.
Maintained by Daniel Herszenhut. Last updated 4 years ago.
1.9 match 1.70 score 1 scripts
fabrice-rossi
mixvlmc:Variable Length Markov Chains with Covariates
Estimates Variable Length Markov Chains (VLMC) models and VLMC with covariates models from discrete sequences. Supports model selection via information criteria and simulation of new sequences from an estimated model. See Bühlmann, P. and Wyner, A. J. (1999) <doi:10.1214/aos/1018031204> for VLMC and Zanin Zambom, A., Kim, S. and Lopes Garcia, N. (2022) <doi:10.1111/jtsa.12615> for VLMC with covariates.
Maintained by Fabrice Rossi. Last updated 11 months ago.
machine-learningmarkov-chainmarkov-modelstatisticstime-seriescpp
0.5 match 2 stars 6.23 score 20 scripts
mayer79
flashlight:Shed Light on Black Box Machine Learning Models
Shed light on black box machine learning models by the help of model performance, variable importance, global surrogate models, ICE profiles, partial dependence (Friedman J. H. (2001) <doi:10.1214/aos/1013203451>), accumulated local effects (Apley D. W. (2016) <arXiv:1612.08468>), further effects plots, interaction strength, and variable contribution breakdown (Gosiewska and Biecek (2019) <arxiv:1903.11420>). All tools are implemented to work with case weights and allow for stratified analysis. Furthermore, multiple flashlights can be combined and analyzed together.
Maintained by Michael Mayer. Last updated 8 months ago.
interpretabilityinterpretable-machine-learningmachine-learningxai
0.5 match 22 stars 6.25 score 54 scripts 1 dependentscran
cmprsk:Subdistribution Analysis of Competing Risks
Estimation, testing and regression modeling of subdistribution functions in competing risks, as described in Gray (1988), A class of K-sample tests for comparing the cumulative incidence of a competing risk, Ann. Stat. 16:1141-1154 <DOI:10.1214/aos/1176350951>, and Fine JP and Gray RJ (1999), A proportional hazards model for the subdistribution of a competing risk, JASA, 94:496-509, <DOI:10.1080/01621459.1999.10474144>.
Maintained by Bob Gray. Last updated 10 months ago.
0.5 match 3 stars 6.07 score 65 dependents
rasmusab
bayesboot:An Implementation of Rubin's (1981) Bayesian Bootstrap
Functions for performing the Bayesian bootstrap as introduced by Rubin (1981) <doi:10.1214/aos/1176345338> and for summarizing the result. The implementation can handle both summary statistics that works on a weighted version of the data and summary statistics that works on a resampled data set.
Maintained by Rasmus Bååth. Last updated 7 years ago.
0.5 match 49 stars 5.52 score 45 scripts
kangy10
DRIP:Discontinuous Regression and Image Processing
A collection of functions that perform jump regression and image analysis such as denoising, deblurring and jump detection. The implemented methods are based on the following research: Qiu, P. (1998) <doi:10.1214/aos/1024691468>, Qiu, P. and Yandell, B. (1997) <doi: 10.1080/10618600.1997.10474746>, Qiu, P. (2009) <doi: 10.1007/s10463-007-0166-9>, Kang, Y. and Qiu, P. (2014) <doi: 10.1080/00401706.2013.844732>, Qiu, P. and Kang, Y. (2015) <doi: 10.5705/ss.2014.054>, Kang, Y., Mukherjee, P.S. and Qiu, P. (2018) <doi: 10.1080/00401706.2017.1415975>, Kang, Y. (2020) <doi: 10.1080/10618600.2019.1665536>.
Maintained by Yicheng Kang. Last updated 4 months ago.
0.5 match 5.49 score 31 scriptsmayer79
effectplots:Effect Plots
High-performance implementation of various effect plots useful for regression and probabilistic classification tasks. The package includes partial dependence plots (Friedman, 2021, <doi:10.1214/aos/1013203451>), accumulated local effect plots and M-plots (both from Apley and Zhu, 2016, <doi:10.1111/rssb.12377>), as well as plots that describe the statistical associations between model response and features. It supports visualizations with either 'ggplot2' or 'plotly', and is compatible with most models, including 'Tidymodels', models wrapped in 'DALEX' explainers, or models with case weights.
Maintained by Michael Mayer. Last updated 9 days ago.
machine-learningregressionxaicpp
0.5 match 19 stars 5.18 score 8 scripts
chengjunhou
xgb2sql:Convert Trained 'XGBoost' Model to SQL Query
This tool enables in-database scoring of 'XGBoost' models built in R, by translating trained model objects into SQL query. 'XGBoost' <https://github.com/dmlc/xgboost> provides parallel tree boosting (also known as gradient boosting machine, or GBM) algorithms in a highly efficient, flexible and portable way. GBM algorithm is introduced by Friedman (2001) <doi:10.1214/aos/1013203451>, and more details on 'XGBoost' can be found in Chen & Guestrin (2016) <doi:10.1145/2939672.2939785>.
Maintained by Chengjun Hou. Last updated 3 years ago.
0.5 match 22 stars 5.04 score 7 scriptspchausse
momentfit:Methods of Moments
Several classes for moment-based models are defined. The classes are defined for moment conditions derived from a single equation or a system of equations. The conditions can also be expressed as functions or formulas. Several methods are also offered to facilitate the development of different estimation techniques. The methods that are currently provided are the Generalized method of moments (Hansen 1982; <doi:10.2307/1912775>), for single equations and systems of equation, and the Generalized Empirical Likelihood (Smith 1997; <doi:10.1111/j.0013-0133.1997.174.x>, Kitamura 1997; <doi:10.1214/aos/1069362388>, Newey and Smith 2004; <doi:10.1111/j.1468-0262.2004.00482.x>, and Anatolyev 2005 <doi:10.1111/j.1468-0262.2005.00601.x>).
Maintained by Pierre Chausse. Last updated 1 years ago.
0.5 match 4.80 score 21 scripts 1 dependents
abdisalammuse
AmoudSurv:Tractable Parametric Odds-Based Regression Models
Fits tractable fully parametric odds-based regression models for survival data, including proportional odds (PO), accelerated failure time (AFT), accelerated odds (AO), and General Odds (GO) models in overall survival frameworks. Given at least an R function specifying the survivor, hazard rate and cumulative distribution functions, any user-defined parametric distribution can be fitted. We applied and evaluated a minimum of seventeen (17) various baseline distributions that can handle different failure rate shapes for each of the four different proposed odds-based regression models. For more information see Bennet et al., (1983) <doi:10.1002/sim.4780020223>, and Muse et al., (2022) <doi:10.1016/j.aej.2022.01.033>.
Maintained by Abdisalam Hassan Muse. Last updated 3 years ago.
2.4 match 1.00 score
baolong281
MonotonicityTest:Nonparametric Bootstrap Test for Regression Monotonicity
Implements nonparametric bootstrap tests for detecting monotonicity in regression functions from Hall, P. and Heckman, N. (2000) <doi:10.1214/aos/1016120363> Includes tools for visualizing results using Nadaraya-Watson kernel regression and supports efficient computation with 'C++'.
Maintained by Dylan Huynh. Last updated 11 days ago.
0.5 match 4.08 score 2 scripts 1 dependents
meganheyman
lmboot:Bootstrap in Linear Models
Various efficient and robust bootstrap methods are implemented for linear models with least squares estimation. Functions within this package allow users to create bootstrap sampling distributions for model parameters, test hypotheses about parameters, and visualize the bootstrap sampling or null distributions. Methods implemented for linear models include the wild bootstrap by Wu (1986) <doi:10.1214/aos/1176350142>, the residual and paired bootstraps by Efron (1979, ISBN:978-1-4612-4380-9), the delete-1 jackknife by Quenouille (1956) <doi:10.2307/2332914>, and the Bayesian bootstrap by Rubin (1981) <doi:10.1214/aos/1176345338>.
Maintained by Megan Heyman. Last updated 5 years ago.
0.8 match 2 stars 2.70 score 25 scriptscran
logspline:Routines for Logspline Density Estimation
Contains routines for logspline density estimation. The function oldlogspline() uses the same algorithm as the logspline package version 1.0.x; i.e. the Kooperberg and Stone (1992) algorithm (with an improved interface). The recommended routine logspline() uses an algorithm from Stone et al (1997) <DOI:10.1214/aos/1031594728>.
Maintained by Charles Kooperberg. Last updated 10 months ago.
0.5 match 4.03 score 13 dependents
cdalitz
moonboot:m-Out-of-n Bootstrap Functions
Functions and examples based on the m-out-of-n bootstrap suggested by Politis, D.N. and Romano, J.P. (1994) <doi:10.1214/aos/1176325770>. Additionally there are functions to estimate the scaling factor tau and the subsampling size m. For a detailed description and a full list of references, see Dalitz, C. and Lögler, F. (2024) <doi:10.48550/arXiv.2412.05032>.
Maintained by Christoph Dalitz. Last updated 25 days ago.
0.5 match 2 stars 3.78 score 1 scripts
hippolyteboucher
SpeTestNP:Non-Parametric Tests of Parametric Specifications
Performs non-parametric tests of parametric specifications. Five tests are available. Specific bandwidth and kernel methods can be chosen along with many other options. Allows parallel computing to quickly compute p-values based on the bootstrap. Methods implemented in the package are H.J. Bierens (1982) <doi:10.1016/0304-4076(82)90105-1>, J.C. Escanciano (2006) <doi:10.1017/S0266466606060506>, P.L. Gozalo (1997) <doi:10.1016/S0304-4076(97)86571-2>, P. Lavergne and V. Patilea (2008) <doi:10.1016/j.jeconom.2007.08.014>, P. Lavergne and V. Patilea (2012) <doi:10.1198/jbes.2011.07152>, J.H. Stock and M.W. Watson (2006) <doi:10.1111/j.1538-4616.2007.00014.x>, C.F.J. Wu (1986) <doi:10.1214/aos/1176350142>, J. Yin, Z. Geng, R. Li, H. Wang (2010) <https://www.jstor.org/stable/24309002> and J.X. Zheng (1996) <doi:10.1016/0304-4076(95)01760-7>.
Maintained by Hippolyte Boucher. Last updated 2 years ago.
0.5 match 3.70 score 2 scriptscran
MultipleRegression:Multiple Regression Analysis
Tools to analysis of experiments having two or more quantitative explanatory variables and one quantitative dependent variable. Experiments can be without repetitions or with a statistical design (Hair JF, 2016) <ISBN: 13: 978-0138132637>. Pacote para uma analise de experimentos havendo duas ou mais variaveis explicativas quantitativas e uma variavel dependente quantitativa. Os experimentos podem ser sem repeticoes ou com delineamento estatistico (Hair JF, 2016) <ISBN: 13: 978-0138132637>.
Maintained by Alcinei Mistico Azevedo. Last updated 3 years ago.
1.7 match 1.00 score
boehringer-ingelheim
BPrinStratTTE:Causal Effects in Principal Strata Defined by Antidrug Antibodies
Bayesian models to estimate causal effects of biological treatments on time-to-event endpoints in clinical trials with principal strata defined by the occurrence of antidrug antibodies. The methodology is based on Frangakis and Rubin (2002) <doi:10.1111/j.0006-341x.2002.00021.x> and Imbens and Rubin (1997) <doi:10.1214/aos/1034276631>, and here adapted to a specific time-to-event setting.
Maintained by Christian Stock. Last updated 11 months ago.
bayesian-methodscausal-inferenceclinical-trialestimandmcmc-methodspharmaceutical-developmentprincipal-stratificationsimulationstantime-to-eventcpp
0.5 match 3.18 score
asmahani
MfUSampler:Multivariate-from-Univariate (MfU) MCMC Sampler
Convenience functions for multivariate MCMC using univariate samplers including: slice sampler with stepout and shrinkage (Neal (2003) <DOI:10.1214/aos/1056562461>), adaptive rejection sampler (Gilks and Wild (1992) <DOI:10.2307/2347565>), adaptive rejection Metropolis (Gilks et al (1995) <DOI:10.2307/2986138>), and univariate Metropolis with Gaussian proposal.
Maintained by Alireza S. Mahani. Last updated 2 years ago.
0.5 match 3.08 score 20 scripts 2 dependentscran
hmmm:Hierarchical Multinomial Marginal Models
Functions for specifying and fitting marginal models for contingency tables proposed by Bergsma and Rudas (2002) <doi:10.1214/aos/1015362188> here called hierarchical multinomial marginal models (hmmm) and their extensions presented by Bartolucci, Colombi and Forcina (2007) <https://www.jstor.org/stable/24307737>; multinomial Poisson homogeneous (mph) models and homogeneous linear predictor (hlp) models for contingency tables proposed by Lang (2004) <doi:10.1214/aos/1079120140> and Lang (2005) <doi:10.1198/016214504000001042>. Inequality constraints on the parameters are allowed and can be tested.
Maintained by Roberto Colombi. Last updated 10 months ago.
0.8 match 2.00 score
benbarnard
rWishart:Random Wishart Matrix Generation
An expansion of R's 'stats' random wishart matrix generation. This package allows the user to generate singular, Uhlig and Harald (1994) <doi:10.1214/aos/1176325375>, and pseudo wishart, Diaz-Garcia, et al.(1997) <doi:10.1006/jmva.1997.1689>, matrices. In addition the user can generate wishart matrices with fractional degrees of freedom, Adhikari (2008) <doi:10.1061/(ASCE)0733-9399(2008)134:12(1029)>, commonly used in volatility modeling. Users can also use this package to create random covariance matrices.
Maintained by Ben Barnard. Last updated 5 years ago.
0.5 match 1 stars 3.04 score 22 scripts
etorarza
RVCompare:Compare Real Valued Random Variables
A framework with tools to compare two random variables via stochastic dominance. See the README.md at <https://github.com/EtorArza/RVCompare> for a quick start guide. It can compute the Cp and Cd of two probability distributions and the Cumulative Difference Plot as explained in E. Arza (2022) <doi:10.1080/10618600.2022.2084405>. Uses bootstrap or DKW-bounds to compute the confidence bands of the cumulative distributions. These two methods are described in B. Efron. (1979) <doi:10.1214/aos/1176344552> and P. Massart (1990) <doi:10.1214/aop/1176990746>.
Maintained by Etor Arza. Last updated 2 months ago.
0.5 match 2 stars 3.00 score 8 scripts
manzhch
edecob:Event Detection Using Confidence Bounds
Detects sustained change in digital bio-marker data using simultaneous confidence bands. Accounts for noise using an auto-regressive model. Based on Buehlmann (1998) "Sieve bootstrap for smoothing in nonstationary time series" <doi:10.1214/aos/1030563978>.
Maintained by Zheng Chen Man. Last updated 2 years ago.
0.5 match 2.70 score 4 scripts
alex-l-m
pmultinom:One-Sided Multinomial Probabilities
Implements multinomial CDF (P(N1<=n1, ..., Nk<=nk)) and tail probabilities (P(N1>n1, ..., Nk>nk)), as well as probabilities with both constraints (P(l1<N1<=u1, ..., lk<Nk<=uk)). Uses a method suggested by Bruce Levin (1981) <doi:10.1214/aos/1176345593>.
Maintained by Alexander Davis. Last updated 7 years ago.
0.5 match 2.70 score 5 scripts
hli226
mvnimpute:Simultaneously Impute the Missing and Censored Values
Implementing a multiple imputation algorithm for multivariate data with missing and censored values under a coarsening at random assumption (Heitjan and Rubin, 1991<doi:10.1214/aos/1176348396>). The multiple imputation algorithm is based on the data augmentation algorithm proposed by Tanner and Wong (1987)<doi:10.1080/01621459.1987.10478458>. The Gibbs sampling algorithm is adopted to to update the model parameters and draw imputations of the coarse data.
Maintained by Hesen Li. Last updated 3 years ago.
0.5 match 2.70 scorecran
rbooster:AdaBoost Framework for Any Classifier
This is a simple package which provides a function that boosts pre-ready or custom-made classifiers. Package uses Discrete AdaBoost (<doi:10.1006/jcss.1997.1504>) and Real AdaBoost (<doi:10.1214/aos/1016218223>) for two class, SAMME (<doi:10.4310/SII.2009.v2.n3.a8>) and SAMME.R (<doi:10.4310/SII.2009.v2.n3.a8>) for multiclass classification.
Maintained by Fatih Saglam. Last updated 3 years ago.
0.5 match 2.70 score 6 scripts
xmengju
RRBoost:A Robust Boosting Algorithm
An implementation of robust boosting algorithms for regression in R. This includes the RRBoost method proposed in the paper "Robust Boosting for Regression Problems" (Ju X and Salibian-Barrera M. 2020) <doi:10.1016/j.csda.2020.107065> (to appear in Computational Statistics and Data Science). It also implements previously proposed boosting algorithms in the simulation section of the paper: L2Boost, LADBoost, MBoost (Friedman, J. H. (2001) <10.1214/aos/1013203451>) and Robloss (Lutz et al. (2008) <10.1016/j.csda.2007.11.006>).
Maintained by Xiaomeng Ju. Last updated 4 months ago.
0.5 match 2.70 score 3 scripts
stavakol
covsep:Tests for Determining if the Covariance Structure of 2-Dimensional Data is Separable
Functions for testing if the covariance structure of 2-dimensional data (e.g. samples of surfaces X_i = X_i(s,t)) is separable, i.e. if covariance(X) = C_1 x C_2. A complete descriptions of the implemented tests can be found in the paper Aston, John A. D.; Pigoli, Davide; Tavakoli, Shahin. Tests for separability in nonparametric covariance operators of random surfaces. Ann. Statist. 45 (2017), no. 4, 1431--1461. <doi:10.1214/16-AOS1495> <https://projecteuclid.org/euclid.aos/1498636862> <arXiv:1505.02023>.
Maintained by Shahin Tavakoli. Last updated 7 years ago.
0.5 match 2.11 score 13 scriptsasmahani
DBR:Discrete Beta Regression
Bayesian Beta Regression, adapted for bounded discrete responses, commonly seen in survey responses. Estimation is done via Markov Chain Monte Carlo sampling, using a Gibbs wrapper around univariate slice sampler (Neal (2003) <DOI:10.1214/aos/1056562461>), as implemented in the R package MfUSampler (Mahani and Sharabiani (2017) <DOI: 10.18637/jss.v078.c01>).
Maintained by Alireza Mahani. Last updated 2 years ago.
0.5 match 2.08 score 12 scripts
sscogges
noncomplyR:Bayesian Analysis of Randomized Experiments with Non-Compliance
Functions for Bayesian analysis of data from randomized experiments with non-compliance. The functions are based on the models described in Imbens and Rubin (1997) <doi:10.1214/aos/1034276631>. Currently only two types of outcome models are supported: binary outcomes and normally distributed outcomes. Models can be fit with and without the exclusion restriction and/or the strong access monotonicity assumption. Models are fit using the data augmentation algorithm as described in Tanner and Wong (1987) <doi:10.2307/2289457>.
Maintained by Scott Coggeshall. Last updated 8 years ago.
0.5 match 2.00 score 7 scripts
valentint
trimcluster:Cluster Analysis with Trimming
Trimmed k-means clustering. The method is described in Cuesta-Albertos et al. (1997) <doi:10.1214/aos/1031833664>.
Maintained by Valentin Todorov. Last updated 5 years ago.
0.5 match 1.78 score 20 scripts 1 dependentscran
varjmcm:Estimations for the Covariance of Estimated Parameters in Joint Mean-Covariance Models
The goal of the package is to equip the 'jmcm' package (current version 0.2.1) with estimations of the covariance of estimated parameters. Two methods are provided. The first method is to use the inverse of estimated Fisher's information matrix, see M. Pourahmadi (2000) <doi:10.1093/biomet/87.2.425>, M. Maadooliat, M. Pourahmadi and J. Z. Huang (2013) <doi:10.1007/s11222-011-9284-6>, and W. Zhang, C. Leng, C. Tang (2015) <doi:10.1111/rssb.12065>. The second method is bootstrap based, see Liu, R.Y. (1988) <doi:10.1214/aos/1176351062> for reference.
Maintained by Naimin Jing. Last updated 5 years ago.
0.5 match 1.70 score
doer0
normalizeH:Normalize Hadamard Matrix
Normalize a given Hadamard matrix. A Hadamard matrix is said to be normalized when its first row and first column entries are all 1, see Hedayat, A. and Wallis, W. D. (1978) "Hadamard matrices and their applications. The Annals of Statistics, 1184-1238." <doi:10.1214/aos/1176344370>.
Maintained by Baidya Nath Mandal. Last updated 3 years ago.
0.5 match 1.00 score
jeromepcollet
ModEstM:Mode Estimation, Even in the Multimodal Case
Function ModEstM() is the only one of this package, it estimates the modes of an empirical univariate distribution. It relies on the stats::density() function, even for input control. Due to very good performance of the density estimation, computation time is not an issue. The multiple modes are handled using dplyr::group_by(). For conditions and rates of convergences, see Eddy (1980) <doi:10.1214/aos/1176345080>.
Maintained by Jerome Collet. Last updated 3 years ago.
0.5 match 1.00 scorecran
pgKDEsphere:Parametrically Guided Kernel Density Estimator for Spherical Data
Nonparametric density estimation for (hyper)spherical data by means of a parametrically guided kernel estimator (adaptation of the method of Hjort and Glad (1995) <doi:10.1214/aos/1176324627> to the spherical setting). The package also allows the data-driven selection of the smoothing parameter and the representation of the estimated density for circular and spherical data. Estimators of the density without guide can also be obtained.
Maintained by María Alonso-Pena. Last updated 1 years ago.
0.5 match 1.00 scorecran
wsbackfit:Weighted Smooth Backfitting for Structured Models
Non- and semiparametric regression for generalized additive, partial linear, and varying coefficient models as well as their combinations via smoothed backfitting. Based on Roca-Pardinas J and Sperlich S (2010) <doi:10.1007/s11222-009-9130-2>; Mammen E, Linton O and Nielsen J (1999) <doi:10.1214/aos/1017939138>; Lee YK, Mammen E, Park BU (2012) <doi:10.1214/12-AOS1026>.
Maintained by Javier Roca-Pardinas. Last updated 4 years ago.
0.5 match 1.00 scorecran
vfprogression:Visual Field (VF) Progression Analysis and Plotting Methods
Realization of published methods to analyze visual field (VF) progression. Introduction to the plotting methods (designed by author TE) for VF output visualization. A sample dataset for two eyes, each with 10 follow-ups is included. The VF analysis methods could be found in -- Musch et al. (1999) <doi:10.1016/S0161-6420(99)90147-1>, Nouri-Mahdavi et at. (2012) <doi:10.1167/iovs.11-9021>, Schell et at. (2014) <doi:10.1016/j.ophtha.2014.02.021>, Aptel et al. (2015) <doi:10.1111/aos.12788>.
Maintained by Dian Li. Last updated 6 years ago.
0.5 match 1.00 score
ikiii
saeeb:Small Area Estimation for Count Data
Provides small area estimation for count data type and gives option whether to use covariates in the estimation or not. By implementing Empirical Bayes (EB) Poisson-Gamma model, each function returns EB estimators and mean squared error (MSE) estimators for each area. The EB estimators without covariates are obtained using the model proposed by Clayton & Kaldor (1987) <doi:10.2307/2532003>, the EB estimators with covariates are obtained using the model proposed by Wakefield (2006) <doi:10.1093/biostatistics/kxl008> and the MSE estimators are obtained using Jackknife method by Jiang et. al. (2002) <doi:10.1214/aos/1043351257>.
Maintained by Rizki Ananda Fauziah. Last updated 5 years ago.
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