Showing 6 of total 6 results (show query)
r-lidar
lidR:Airborne LiDAR Data Manipulation and Visualization for Forestry Applications
Airborne LiDAR (Light Detection and Ranging) interface for data manipulation and visualization. Read/write 'las' and 'laz' files, computation of metrics in area based approach, point filtering, artificial point reduction, classification from geographic data, normalization, individual tree segmentation and other manipulations.
Maintained by Jean-Romain Roussel. Last updated 2 months ago.
alsforestrylaslazlidarpoint-cloudremote-sensingopenblascppopenmp
623 stars 14.47 score 844 scripts 8 dependents
alexpghayes
distributions3:Probability Distributions as S3 Objects
Tools to create and manipulate probability distributions using S3. Generics pdf(), cdf(), quantile(), and random() provide replacements for base R's d/p/q/r style functions. Functions and arguments have been named carefully to minimize confusion for students in intro stats courses. The documentation for each distribution contains detailed mathematical notes.
Maintained by Alex Hayes. Last updated 6 months ago.
102 stars 11.35 score 118 scripts 7 dependents
bachmannpatrick
CLVTools:Tools for Customer Lifetime Value Estimation
A set of state-of-the-art probabilistic modeling approaches to derive estimates of individual customer lifetime values (CLV). Commonly, probabilistic approaches focus on modelling 3 processes, i.e. individuals' attrition, transaction, and spending process. Latent customer attrition models, which are also known as "buy-'til-you-die models", model the attrition as well as the transaction process. They are used to make inferences and predictions about transactional patterns of individual customers such as their future purchase behavior. Moreover, these models have also been used to predict individuals’ long-term engagement in activities such as playing an online game or posting to a social media platform. The spending process is usually modelled by a separate probabilistic model. Combining these results yields in lifetime values estimates for individual customers. This package includes fast and accurate implementations of various probabilistic models for non-contractual settings (e.g., grocery purchases or hotel visits). All implementations support time-invariant covariates, which can be used to control for e.g., socio-demographics. If such an extension has been proposed in literature, we further provide the possibility to control for time-varying covariates to control for e.g., seasonal patterns. Currently, the package includes the following latent attrition models to model individuals' attrition and transaction process: [1] Pareto/NBD model (Pareto/Negative-Binomial-Distribution), [2] the Extended Pareto/NBD model (Pareto/Negative-Binomial-Distribution with time-varying covariates), [3] the BG/NBD model (Beta-Gamma/Negative-Binomial-Distribution) and the [4] GGom/NBD (Gamma-Gompertz/Negative-Binomial-Distribution). Further, we provide an implementation of the Gamma/Gamma model to model the spending process of individuals.
Maintained by Patrick Bachmann. Last updated 4 months ago.
clvcustomer-lifetime-valuecustomer-relationship-managementopenblasgslcppopenmp
55 stars 6.47 score 12 scripts
bdhitt
binGroup2:Identification and Estimation using Group Testing
Methods for the group testing identification problem: 1) Operating characteristics (e.g., expected number of tests) for commonly used hierarchical and array-based algorithms, and 2) Optimal testing configurations for these same algorithms. Methods for the group testing estimation problem: 1) Estimation and inference procedures for an overall prevalence, and 2) Regression modeling for commonly used hierarchical and array-based algorithms.
Maintained by Brianna Hitt. Last updated 1 years ago.
2.48 score 3 scripts 1 dependents
m-signo
ptmixed:Poisson-Tweedie Generalized Linear Mixed Model
Fits the Poisson-Tweedie generalized linear mixed model described in Signorelli et al. (2021, <doi:10.1177/1471082X20936017>). Likelihood approximation based on adaptive Gauss Hermite quadrature rule.
Maintained by Mirko Signorelli. Last updated 3 years ago.
2.15 score 14 scripts
