Examples: visualization, C++, networks, data cleaning, html widgets, ropensci.

Found 495 packages in 0.01 seconds

CLA — by Martin Maechler, 9 months ago

Critical Line Algorithm in Pure R

Implements 'Markowitz' Critical Line Algorithm ('CLA') for classical mean-variance portfolio optimization, see Markowitz (1952) . Care has been taken for correctness in light of previous buggy implementations.

VLMC — by Martin Maechler, 9 months ago

Variable Length Markov Chains ('VLMC') Models

Functions, Classes & Methods for estimation, prediction, and simulation (bootstrap) of Variable Length Markov Chain ('VLMC') Models.

classGraph — by Martin Maechler, 10 days ago

Construct Graphs of S4 Class Hierarchies

Construct directed graphs of S4 class hierarchies and visualize them. In general, these graphs typically are DAGs (directed acyclic graphs), often simple trees in practice.

round — by Martin Maechler, a year ago

Rounding to Decimal Digits

Decimal rounding is non-trivial in binary arithmetic. ISO standard round to even is more rare than typically assumed as most decimal fractions are not exactly representable in binary. Our 'roundX()' versions explore differences between current and potential future versions of round() in R. Further, provides (some partly related) C99 math lib functions not in base R.

sca — by Martin Maechler, a year ago

Simple Component Analysis

Simple Component Analysis (SCA) often provides much more interpretable components than Principal Components (PCA) while still representing much of the variability in the data.

supclust — by Martin Maechler, 4 years ago

Supervised Clustering of Predictor Variables Such as Genes

Methodology for supervised grouping aka "clustering" of potentially many predictor variables, such as genes etc, implementing algorithms 'PELORA' and 'WILMA'.

plugdensity — by Martin Maechler, a year ago

Plug-in Kernel Density Estimation

Kernel density estimation with global bandwidth selection via "plug-in".

glmmTMB — by Mollie Brooks, a month ago

Generalized Linear Mixed Models using Template Model Builder

Fit linear and generalized linear mixed models with various extensions, including zero-inflation. The models are fitted using maximum likelihood estimation via 'TMB' (Template Model Builder). Random effects are assumed to be Gaussian on the scale of the linear predictor and are integrated out using the Laplace approximation. Gradients are calculated using automatic differentiation.

GLDEX — by Steve Su, 2 years ago

Fitting Single and Mixture of Generalised Lambda Distributions

The fitting algorithms considered in this package have two major objectives. One is to provide a smoothing device to fit distributions to data using the weight and unweighted discretised approach based on the bin width of the histogram. The other is to provide a definitive fit to the data set using the maximum likelihood and quantile matching estimation. Other methods such as moment matching, starship method, L moment matching are also provided. Diagnostics on goodness of fit can be done via qqplots, KS-resample tests and comparing mean, variance, skewness and kurtosis of the data with the fitted distribution. References include the following: Karvanen and Nuutinen (2008) "Characterizing the generalized lambda distribution by L-moments" , King and MacGillivray (1999) "A starship method for fitting the generalised lambda distributions" , Su (2005) "A Discretized Approach to Flexibly Fit Generalized Lambda Distributions to Data" , Su (2007) "Nmerical Maximum Log Likelihood Estimation for Generalized Lambda Distributions" , Su (2007) "Fitting Single and Mixture of Generalized Lambda Distributions to Data via Discretized and Maximum Likelihood Methods: GLDEX in R" , Su (2009) "Confidence Intervals for Quantiles Using Generalized Lambda Distributions" , Su (2010) "Chapter 14: Fitting GLDs and Mixture of GLDs to Data using Quantile Matching Method" , Su (2010) "Chapter 15: Fitting GLD to data using GLDEX 1.0.4 in R" , Su (2015) "Flexible Parametric Quantile Regression Model" , Su (2021) "Flexible parametric accelerated failure time model".

blme — by Vincent Dorie, 8 months ago

Bayesian Linear Mixed-Effects Models

Maximum a posteriori estimation for linear and generalized linear mixed-effects models in a Bayesian setting, implementing the methods of Chung, et al. (2013) . Extends package 'lme4' (Bates, Maechler, Bolker, and Walker (2015) ).