R-universe search: insurance

spedygiorgio

lifecontingencies:Financial and Actuarial Mathematics for Life Contingencies

Classes and methods that allow the user to manage life table, actuarial tables (also multiple decrements tables). Moreover, functions to easily perform demographic, financial and actuarial mathematics on life contingencies insurances calculations are contained therein. See Spedicato (2013) <doi:10.18637/jss.v055.i10>.

Maintained by Giorgio Alfredo Spedicato. Last updated 6 months ago.

actuarial financial life-contingencies life-insurance cpp

24.9 match 62 stars 7.09 score 156 scripts

joaquinauza

DetLifeInsurance:Life Insurance Premium and Reserves Valuation

Methods for valuation of life insurance premiums and reserves (including variable-benefit and fractional coverage) based on "Actuarial Mathematics" by Bowers, H.U. Gerber, J.C. Hickman, D.A. Jones and C.J. Nesbitt (1997, ISBN: 978-0938959465), "Actuarial Mathematics for Life Contingent Risks" by Dickson, David C. M., Hardy, Mary R. and Waters, Howard R (2009) <doi:10.1017/CBO9780511800146> and "Life Contingencies" by Jordan, C. W (1952) <doi:10.1017/S002026810005410X>. It also contains functions for equivalent interest and discount rate calculation, present and future values of annuities, and loan amortization schedule.

Maintained by Joaquin Auza. Last updated 5 years ago.

actuarial actuarial-science life-insurance

31.3 match 10 stars 4.70 score 3 scripts

julianfaraway

faraway:Datasets and Functions for Books by Julian Faraway

Books are "Linear Models with R" published 1st Ed. August 2004, 2nd Ed. July 2014, 3rd Ed. February 2025 by CRC press, ISBN 9781439887332, and "Extending the Linear Model with R" published by CRC press in 1st Ed. December 2005 and 2nd Ed. March 2016, ISBN 9781584884248 and "Practical Regression and ANOVA in R" contributed documentation on CRAN (now very dated).

Maintained by Julian Faraway. Last updated 1 months ago.

data

15.4 match 29 stars 9.43 score 1.7k scripts 1 dependents

kainhofer

LifeInsureR:Modelling Traditional Life Insurance Contracts

R6 classes to model traditional life insurance contracts like annuities, whole life insurances or endowments. Such life insurance contracts provide a guaranteed interest and are not directly linked to the performance of a particular investment vehicle, but they typically provide (discretionary) profit participation. This package provides a framework to model such contracts in a very generic (cash-flow-based) way and includes modelling profit participation schemes, dynamic increases or more general contract layers, as well as contract changes (like sum increases or premium waivers). All relevant quantities like premium decomposition, reserves and benefits over the whole contract period are calculated and potentially exported to 'Excel'. Mortality rates are given using the 'MortalityTables' package.

Maintained by Reinhold Kainhofer. Last updated 1 years ago.

29.6 match 2 stars 3.48 score 9 scripts 1 dependents

kjhealy

gssrdoc:Document General Social Survey Variable

The General Social Survey (GSS) is a long-running, mostly annual survey of US households. It is administered by the National Opinion Research Center (NORC). This package contains the a tibble with information on the survey variables, together with every variable documented as an R help page. For more information on the GSS see \url{http://gss.norc.org}.

Maintained by Kieran Healy. Last updated 11 months ago.

44.9 match 2.28 score 38 scripts

sleire

etrm:Energy Trading and Risk Management

Provides a collection of functions to perform core tasks within Energy Trading and Risk Management (ETRM). Calculation of maximum smoothness forward price curves for electricity and natural gas contracts with flow delivery, as presented in F. E. Benth, S. Koekebakker, and F. Ollmar (2007) <doi:10.3905/jod.2007.694791> and F. E. Benth, J. S. Benth, and S. Koekebakker (2008) <doi:10.1142/6811>. Portfolio insurance trading strategies for price risk management in the forward market, see F. Black (1976) <doi:10.1016/0304-405X(76)90024-6>, T. Bjork (2009) <https://EconPapers.repec.org/RePEc:oxp:obooks:9780199574742>, F. Black and R. W. Jones (1987) <doi:10.3905/jpm.1987.409131> and H. E. Leland (1980) <http://www.jstor.org/stable/2327419>.

Maintained by Anders D. Sleire. Last updated 2 years ago.

commodities energy-trading risk-management trading-strategies

17.7 match 33 stars 5.52 score 10 scripts

mharinga

insurancerating:Analytic Insurance Rating Techniques

Functions to build, evaluate, and visualize insurance rating models. It simplifies the process of modeling premiums, and allows to analyze insurance risk factors effectively. The package employs a data-driven strategy for constructing insurance tariff classes, drawing on the work of Antonio and Valdez (2012) <doi:10.1007/s10182-011-0152-7>.

Maintained by Martin Haringa. Last updated 5 months ago.

actuarial actuarial-science insurance pricing

15.9 match 70 stars 5.89 score 28 scripts

mharinga

spatialrisk:Calculating Spatial Risk

Methods for spatial risk calculations. It offers an efficient approach to determine the sum of all observations within a circle of a certain radius. This might be beneficial for insurers who are required (by a recent European Commission regulation) to determine the maximum value of insured fire risk policies of all buildings that are partly or fully located within a circle of a radius of 200m. See Church (1974) <doi:10.1007/BF01942293> for a description of the problem.

Maintained by Martin Haringa. Last updated 7 months ago.

actuarial-science insurance solvency-ii spatial cpp

14.3 match 19 stars 5.06 score 30 scripts

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-inference brms multilevel-models stan statistical-models

3.8 match 1.3k stars 16.61 score 13k scripts 34 dependents

mages

ChainLadder:Statistical Methods and Models for Claims Reserving in General Insurance

Various statistical methods and models which are typically used for the estimation of outstanding claims reserves in general insurance, including those to estimate the claims development result as required under Solvency II.

Maintained by Markus Gesmann. Last updated 1 months ago.

6.0 match 82 stars 10.04 score 196 scripts 2 dependents

jimbrig

lossrx:Actuarial Loss Development and Reserving with R

Actuarial Loss Development and Reserving Helper Functions and ShinyApp.

Maintained by Jimmy Briggs. Last updated 3 months ago.

actuarial-science claims-data claims-reserving data-science insurance modelling property-casualty reserving rshiny workflow

10.0 match 14 stars 5.89 score 7 scripts

gpitt71

clmplus:Tool-Box of Chain Ladder Plus Models

Implementation of the age-period-cohort models for the claim development presented in the manuscript 'Replicating and extending chain-ladder via an age-period-cohort structure on the claim development in a run-off triangle' <doi:10.48550/arXiv.2301.03858>.

Maintained by Gabriele Pittarello. Last updated 5 months ago.

age-period-cohort-models insurance non-life reserving

10.0 match 4 stars 5.30 score 8 scripts

sbgraves237

Ecdat:Data Sets for Econometrics

Data sets for econometrics, including political science.

Maintained by Spencer Graves. Last updated 4 months ago.

6.8 match 2 stars 7.25 score 740 scripts 3 dependents

alexkz

kernlab:Kernel-Based Machine Learning Lab

Kernel-based machine learning methods for classification, regression, clustering, novelty detection, quantile regression and dimensionality reduction. Among other methods 'kernlab' includes Support Vector Machines, Spectral Clustering, Kernel PCA, Gaussian Processes and a QP solver.

Maintained by Alexandros Karatzoglou. Last updated 7 months ago.

openblas cpp

4.0 match 21 stars 12.26 score 7.8k scripts 487 dependents

treynkens

ReIns:Functions from "Reinsurance: Actuarial and Statistical Aspects"

Functions from the book "Reinsurance: Actuarial and Statistical Aspects" (2017) by Hansjoerg Albrecher, Jan Beirlant and Jef Teugels <https://www.wiley.com/en-us/Reinsurance%3A+Actuarial+and+Statistical+Aspects-p-9780470772683>.

Maintained by Tom Reynkens. Last updated 4 months ago.

extremes reinsurance risk-analysis cpp

7.2 match 22 stars 6.31 score 186 scripts

r-forge

copula:Multivariate Dependence with Copulas

Classes (S4) of commonly used elliptical, Archimedean, extreme-value and other copula families, as well as their rotations, mixtures and asymmetrizations. Nested Archimedean copulas, related tools and special functions. Methods for density, distribution, random number generation, bivariate dependence measures, Rosenblatt transform, Kendall distribution function, perspective and contour plots. Fitting of copula models with potentially partly fixed parameters, including standard errors. Serial independence tests, copula specification tests (independence, exchangeability, radial symmetry, extreme-value dependence, goodness-of-fit) and model selection based on cross-validation. Empirical copula, smoothed versions, and non-parametric estimators of the Pickands dependence function.

Maintained by Martin Maechler. Last updated 11 days ago.

3.8 match 11.83 score 1.2k scripts 86 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.

5.6 match 7.04 score 108 scripts 49 dependents

cran

MASS:Support Functions and Datasets for Venables and Ripley's MASS

Functions and datasets to support Venables and Ripley, "Modern Applied Statistics with S" (4th edition, 2002).

Maintained by Brian Ripley. Last updated 16 days ago.

3.8 match 19 stars 10.53 score 11k dependents

r-forge

Sleuth3:Data Sets from Ramsey and Schafer's "Statistical Sleuth (3rd Ed)"

Data sets from Ramsey, F.L. and Schafer, D.W. (2013), "The Statistical Sleuth: A Course in Methods of Data Analysis (3rd ed)", Cengage Learning.

Maintained by Berwin A Turlach. Last updated 1 years ago.

6.0 match 6.38 score 522 scripts

vigou3

actuar:Actuarial Functions and Heavy Tailed Distributions

Functions and data sets for actuarial science: modeling of loss distributions; risk theory and ruin theory; simulation of compound models, discrete mixtures and compound hierarchical models; credibility theory. Support for many additional probability distributions to model insurance loss size and frequency: 23 continuous heavy tailed distributions; the Poisson-inverse Gaussian discrete distribution; zero-truncated and zero-modified extensions of the standard discrete distributions. Support for phase-type distributions commonly used to compute ruin probabilities. Main reference: <doi:10.18637/jss.v025.i07>. Implementation of the Feller-Pareto family of distributions: <doi:10.18637/jss.v103.i06>.

Maintained by Vincent Goulet. Last updated 2 months ago.

openblas

3.8 match 12 stars 9.44 score 732 scripts 35 dependents

r-forge

Sleuth2:Data Sets from Ramsey and Schafer's "Statistical Sleuth (2nd Ed)"

Data sets from Ramsey, F.L. and Schafer, D.W. (2002), "The Statistical Sleuth: A Course in Methods of Data Analysis (2nd ed)", Duxbury.

Maintained by Berwin A Turlach. Last updated 1 years ago.

6.0 match 5.70 score 191 scripts

robjhyndman

fpp2:Data for "Forecasting: Principles and Practice" (2nd Edition)

All data sets required for the examples and exercises in the book "Forecasting: principles and practice" (2nd ed, 2018) by Rob J Hyndman and George Athanasopoulos <https://otexts.com/fpp2/>. All packages required to run the examples are also loaded.

Maintained by Rob Hyndman. Last updated 2 years ago.

data forecasting

3.8 match 106 stars 8.57 score 1.8k scripts 1 dependents

robjhyndman

fpp3:Data for "Forecasting: Principles and Practice" (3rd Edition)

All data sets required for the examples and exercises in the book "Forecasting: principles and practice" by Rob J Hyndman and George Athanasopoulos <https://OTexts.com/fpp3/>. All packages required to run the examples are also loaded. Additional data sets not used in the book are also included.

Maintained by Rob Hyndman. Last updated 6 months ago.

data forecasting

3.8 match 142 stars 8.47 score 2.5k scripts

alanarnholt

BSDA:Basic Statistics and Data Analysis

Data sets for book "Basic Statistics and Data Analysis" by Larry J. Kitchens.

Maintained by Alan T. Arnholt. Last updated 2 years ago.

3.4 match 7 stars 9.11 score 1.3k scripts 6 dependents

kainhofer

MortalityTables:A Framework for Various Types of Mortality / Life Tables

Classes to implement, analyze and plot cohort life tables for actuarial calculations. Birth-year dependent cohort mortality tables using a yearly trend to extrapolate from a base year are implemented, as well as period life table, cohort life tables using an age shift, and merged life tables. Additionally, several data sets from various countries are included to provide widely-used tables out of the box.

Maintained by Reinhold Kainhofer. Last updated 1 years ago.

5.3 match 1 stars 5.70 score 84 scripts 2 dependents

trevorhastie

ISLR:Data for an Introduction to Statistical Learning with Applications in R

We provide the collection of data-sets used in the book 'An Introduction to Statistical Learning with Applications in R'.

Maintained by Trevor Hastie. Last updated 4 years ago.

3.8 match 4 stars 7.58 score 10k scripts 2 dependents

cran

bnlearn:Bayesian Network Structure Learning, Parameter Learning and Inference

Bayesian network structure learning, parameter learning and inference. This package implements constraint-based (PC, GS, IAMB, Inter-IAMB, Fast-IAMB, MMPC, Hiton-PC, HPC), pairwise (ARACNE and Chow-Liu), score-based (Hill-Climbing and Tabu Search) and hybrid (MMHC, RSMAX2, H2PC) structure learning algorithms for discrete, Gaussian and conditional Gaussian networks, along with many score functions and conditional independence tests. The Naive Bayes and the Tree-Augmented Naive Bayes (TAN) classifiers are also implemented. Some utility functions (model comparison and manipulation, random data generation, arc orientation testing, simple and advanced plots) are included, as well as support for parameter estimation (maximum likelihood and Bayesian) and inference, conditional probability queries, cross-validation, bootstrap and model averaging. Development snapshots with the latest bugfixes are available from <https://www.bnlearn.com/>.

Maintained by Marco Scutari. Last updated 2 months ago.

openblas

3.5 match 57 stars 7.72 score 32 dependents

nickch-k

causaldata:Example Data Sets for Causal Inference Textbooks

Example data sets to run the example problems from causal inference textbooks. Currently, contains data sets for Huntington-Klein, Nick (2021) "The Effect" <https://theeffectbook.net>, first and second edition, Cunningham, Scott (2021, ISBN-13: 978-0-300-25168-5) "Causal Inference: The Mixtape", and Hernán, Miguel and James Robins (2020) "Causal Inference: What If" <https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/>.

Maintained by Nick Huntington-Klein. Last updated 4 months ago.

3.6 match 136 stars 7.43 score 144 scripts 1 dependents

cran

insuranceData:A Collection of Insurance Datasets Useful in Risk Classification in Non-life Insurance.

Insurance datasets, which are often used in claims severity and claims frequency modelling. It helps testing new regression models in those problems, such as GLM, GLMM, HGLM, non-linear mixed models etc. Most of the data sets are applied in the project "Mixed models in ratemaking" supported by grant NN 111461540 from Polish National Science Center.

Maintained by Alicja Wolny--Dominiak. Last updated 11 years ago.

17.6 match 2 stars 1.42 score

daphnegiorgi

IBMPopSim:Individual Based Model Population Simulation

Simulation of the random evolution of heterogeneous populations using stochastic Individual-Based Models (IBMs) <doi:10.48550/arXiv.2303.06183>. The package enables users to simulate population evolution, in which individuals are characterized by their age and some characteristics, and the population is modified by different types of events, including births/arrivals, death/exit events, or changes of characteristics. The frequency at which an event can occur to an individual can depend on their age and characteristics, but also on the characteristics of other individuals (interactions). Such models have a wide range of applications. For instance, IBMs can be used for simulating the evolution of a heterogeneous insurance portfolio with selection or for validating mortality forecasts. This package overcomes the limitations of time-consuming IBMs simulations by implementing new efficient algorithms based on thinning methods, which are compiled using the 'Rcpp' package while providing a user-friendly interface.

Maintained by Daphné Giorgi. Last updated 5 months ago.

cpp

4.3 match 8 stars 5.83 score 14 scripts

peterkdunn

GLMsData:Generalized Linear Model Data Sets

Data sets from the book Generalized Linear Models with Examples in R by Dunn and Smyth.

Maintained by Peter K. Dunn. Last updated 3 years ago.

9.1 match 2.61 score 220 scripts

netsimanalytics

NetSimR:Actuarial Functions for Non-Life Insurance Modelling

Assists actuaries and other insurance modellers in pricing, reserving and capital modelling for non-life insurance and reinsurance modelling. Provides functions that help model excess levels, capping and pure Incurred but not reported claims (pure IBNR). Includes capped mean, exposure curves and increased limit factor curves (ILFs) for LogNormal, Gamma, Pareto, Sliced LogNormal-Pareto and Sliced Gamma-Pareto distributions. Includes mean, probability density function (pdf), cumulative probability function (cdf) and inverse cumulative probability function for Sliced LogNormal-Pareto and Sliced Gamma-Pareto distributions. Includes calculating pure IBNR exposure with LogNormal and Gamma distribution for reporting delay. Includes three shiny tools, one to simulate insurance claims applying reinsurance structures, fit generalised linear models and fit claims frequency or severity distributions. Methods used in the package refer to Free for All by Yiannis Parizas (2023) <https://www.theactuary.com/2023/03/02/free-all>; Escaping the triangle by Yiannis Parizas (2019) <https://www.theactuary.com/features/2019/06/2019/06/05/escaping-triangle>; Take to excess by Yiannis Parizas (2019) <https://www.theactuary.com/features/2019/03/2019/03/06/taken-excess>.

Maintained by Yiannis Parizas. Last updated 1 years ago.

7.1 match 1 stars 3.33 score

agi-lab

SynthETIC:Synthetic Experience Tracking Insurance Claims

Creation of an individual claims simulator which generates various features of non-life insurance claims. An initial set of test parameters, designed to mirror the experience of an Auto Liability portfolio, were set up and applied by default to generate a realistic test data set of individual claims (see vignette). The simulated data set then allows practitioners to back-test the validity of various reserving models and to prove and/or disprove certain actuarial assumptions made in claims modelling. The distributional assumptions used to generate this data set can be easily modified by users to match their experiences. Reference: Avanzi B, Taylor G, Wang M, Wong B (2020) "SynthETIC: an individual insurance claim simulator with feature control" <arXiv:2008.05693>.

Maintained by Melantha Wang. Last updated 1 years ago.

3.8 match 12 stars 6.22 score 23 scripts 2 dependents

cran

erer:Empirical Research in Economics with R

Several functions, datasets, and sample codes related to empirical research in economics are included. They cover the marginal effects for binary or ordered choice models, static and dynamic Almost Ideal Demand System (AIDS) models, and a typical event analysis in finance.

Maintained by Changyou Sun. Last updated 6 months ago.

6.9 match 3 stars 3.34 score 211 scripts 1 dependents

arne-henningsen

sampleSelection:Sample Selection Models

Two-step and maximum likelihood estimation of Heckman-type sample selection models: standard sample selection models (Tobit-2), endogenous switching regression models (Tobit-5), sample selection models with binary dependent outcome variable, interval regression with sample selection (only ML estimation), and endogenous treatment effects models. These methods are described in the three vignettes that are included in this package and in econometric textbooks such as Greene (2011, Econometric Analysis, 7th edition, Pearson).

Maintained by Arne Henningsen. Last updated 4 years ago.

3.8 match 6.10 score 311 scripts 5 dependents

friendly

nestedLogit:Nested Dichotomy Logistic Regression Models

Provides functions for specifying and fitting nested dichotomy logistic regression models for a multi-category response and methods for summarising and plotting those models. Nested dichotomies are statistically independent, and hence provide an additive decomposition of tests for the overall 'polytomous' response. When the dichotomies make sense substantively, this method can be a simpler alternative to the standard 'multinomial' logistic model which compares response categories to a reference level. See: J. Fox (2016), "Applied Regression Analysis and Generalized Linear Models", 3rd Ed., ISBN 1452205663.

Maintained by Michael Friendly. Last updated 10 months ago.

logistic-regression multinomial-logistic-regression polytomous-variables

3.8 match 10 stars 6.04 score 33 scripts

bpfaff

evir:Extreme Values in R

Functions for extreme value theory, which may be divided into the following groups; exploratory data analysis, block maxima, peaks over thresholds (univariate and bivariate), point processes, gev/gpd distributions.

Maintained by Bernhard Pfaff. Last updated 8 years ago.

3.8 match 2 stars 5.89 score 211 scripts 6 dependents

gavinrozzi

njtr1:Download, Analyze & Clean New Jersey Car Crash Data

Download and analyze motor vehicle crash data released by the New Jersey Department of Transportation (NJDOT). The data in this package is collected through the filing of NJTR-1 form by police officers, which provide a standardized way of documenting a motor vehicle crash that occurred in New Jersey. 3 different data tables containing data on crashes, vehicles & pedestrians released from 2001 to the present can be downloaded & cleaned using this package.

Maintained by Gavin Rozzi. Last updated 1 years ago.

njtr1 new-jersey road-safety car-crashes car-accidents data

4.8 match 5 stars 4.40 score 7 scripts

alanarnholt

PASWR:Probability and Statistics with R

Functions and data sets for the text Probability and Statistics with R.

Maintained by Alan T. Arnholt. Last updated 3 years ago.

4.5 match 2 stars 4.70 score 241 scripts

svmiller

stevedata:Steve's Toy Data for Teaching About a Variety of Methodological, Social, and Political Topics

This is a collection of various kinds of data with broad uses for teaching. My students, and academics like me who teach the same topics I teach, should find this useful if their teaching workflow is also built around the R programming language. The applications are multiple but mostly cluster on topics of statistical methodology, international relations, and political economy.

Maintained by Steve Miller. Last updated 4 days ago.

3.5 match 8 stars 5.97 score 178 scripts

trevorhastie

ISLR2:Introduction to Statistical Learning, Second Edition

We provide the collection of data-sets used in the book 'An Introduction to Statistical Learning with Applications in R, Second Edition'. These include many data-sets that we used in the first edition (some with minor changes), and some new datasets.

Maintained by Trevor Hastie. Last updated 2 years ago.

3.8 match 2 stars 5.49 score 2.2k scripts

hanmingwu1103

dataSDA:Data Sets for Symbolic Data Analysis

Collects a diverse range of symbolic data and offers a comprehensive set of functions that facilitate the conversion of traditional data into the symbolic data format.

Maintained by Han-Ming Wu. Last updated 2 years ago.

7.6 match 2.70 score 2 scripts

casact

imaginator:Simulate General Insurance Policies and Losses

Simulate general insurance policies, losses and loss emergence. The package contemplates deterministic and stochastic policy retention and growth scenarios. Retention and growth rates are percentages relative to the expiring portfolio. Claims are simulated for each policy. This is accomplished either be assuming a frequency distribution per development lag or by generating random wait times until claim emergence and settlement. Loss simulation uses standard loss distributions for claim amounts.

Maintained by Brian Fannin. Last updated 3 years ago.

3.5 match 14 stars 5.51 score 23 scripts

imranshakoor

DDPM:Data Sets for Discrete Probability Models

A wide collection of univariate discrete data sets from various applied domains related to distribution theory. The functions allow quick, easy, and efficient access to 100 univariate discrete data sets. The data are related to different applied domains, including medical, reliability analysis, engineering, manufacturing, occupational safety, geological sciences, terrorism, psychology, agriculture, environmental sciences, road traffic accidents, demography, actuarial science, law, and justice. The documentation, along with associated references for further details and uses, is presented.

Maintained by Muhammad Imran. Last updated 2 years ago.

19.2 match 1.00 score

alanarnholt

PASWR2:Probability and Statistics with R, Second Edition

Functions and data sets for the text Probability and Statistics with R, Second Edition.

Maintained by Alan T. Arnholt. Last updated 3 years ago.

4.5 match 1 stars 4.24 score 260 scripts

cran

BayesGOF:Bayesian Modeling via Frequentist Goodness-of-Fit

A Bayesian data modeling scheme that performs four interconnected tasks: (i) characterizes the uncertainty of the elicited parametric prior; (ii) provides exploratory diagnostic for checking prior-data conflict; (iii) computes the final statistical prior density estimate; and (iv) executes macro- and micro-inference. Primary reference is Mukhopadhyay, S. and Fletcher, D. 2018 paper "Generalized Empirical Bayes via Frequentist Goodness of Fit" (<https://www.nature.com/articles/s41598-018-28130-5 >).

Maintained by Doug Fletcher. Last updated 6 years ago.

7.5 match 2.48 score 1 dependents

lightbluetitan

crimedatasets:A Comprehensive Collection of Crime-Related Datasets

A comprehensive collection of datasets exclusively focused on crimes, criminal activities, and related topics. This package serves as a valuable resource for researchers, analysts, and students interested in crime analysis, criminology, social and economic studies related to criminal behavior. Datasets span global and local contexts, with a mix of tabular and spatial data.

Maintained by Renzo Caceres Rossi. Last updated 3 months ago.

3.8 match 8 stars 4.90 score 3 scripts

enricoschumann

NMOF:Numerical Methods and Optimization in Finance

Functions, examples and data from the first and the second edition of "Numerical Methods and Optimization in Finance" by M. Gilli, D. Maringer and E. Schumann (2019, ISBN:978-0128150658). The package provides implementations of optimisation heuristics (Differential Evolution, Genetic Algorithms, Particle Swarm Optimisation, Simulated Annealing and Threshold Accepting), and other optimisation tools, such as grid search and greedy search. There are also functions for the valuation of financial instruments such as bonds and options, for portfolio selection and functions that help with stochastic simulations.

Maintained by Enrico Schumann. Last updated 30 days ago.

black-scholes differential-evolution genetic-algorithm grid-search heuristics implied-volatility local-search optimization particle-swarm-optimization simulated-annealing threshold-accepting

1.9 match 36 stars 9.56 score 101 scripts 4 dependents

kosukeimai

experiment:R Package for Designing and Analyzing Randomized Experiments

Provides various statistical methods for designing and analyzing randomized experiments. One functionality of the package is the implementation of randomized-block and matched-pair designs based on possibly multivariate pre-treatment covariates. The package also provides the tools to analyze various randomized experiments including cluster randomized experiments, two-stage randomized experiments, randomized experiments with noncompliance, and randomized experiments with missing data.

Maintained by Kosuke Imai. Last updated 3 years ago.

openblas

3.4 match 14 stars 5.29 score 23 scripts

statmanrobin

Stat2Data:Datasets for Stat2

Datasets for the textbook Stat2: Modeling with Regression and ANOVA (second edition). The package also includes data for the first edition, Stat2: Building Models for a World of Data and a few functions for plotting diagnostics.

Maintained by Robin Lock. Last updated 6 years ago.

3.6 match 5 stars 4.94 score 544 scripts

ovgu-sh

desk:Didactic Econometrics Starter Kit

Written to help undergraduate as well as graduate students to get started with R for basic econometrics without the need to import specific functions and datasets from many different sources. Primarily, the package is meant to accompany the German textbook Auer, L.v., Hoffmann, S., Kranz, T. (2024, ISBN: 978-3-662-68263-0) from which the exercises cover all the topics from the textbook Auer, L.v. (2023, ISBN: 978-3-658-42699-6).

Maintained by Soenke Hoffmann. Last updated 11 months ago.

4.0 match 4.30 score 10 scripts

spedygiorgio

markovchain:Easy Handling Discrete Time Markov Chains

Functions and S4 methods to create and manage discrete time Markov chains more easily. In addition functions to perform statistical (fitting and drawing random variates) and probabilistic (analysis of their structural proprieties) analysis are provided. See Spedicato (2017) <doi:10.32614/RJ-2017-036>. Some functions for continuous times Markov chains depend on the suggested ctmcd package.

Maintained by Giorgio Alfredo Spedicato. Last updated 4 months ago.

ctmc dtmc markov-chain markov-model r-programming rcpp openblas cpp

1.3 match 104 stars 12.78 score 712 scripts 4 dependents

steffenmoritz

imputeR:A General Multivariate Imputation Framework

Multivariate Expectation-Maximization (EM) based imputation framework that offers several different algorithms. These include regularisation methods like Lasso and Ridge regression, tree-based models and dimensionality reduction methods like PCA and PLS.

Maintained by Steffen Moritz. Last updated 4 years ago.

missing-data

3.4 match 16 stars 4.94 score 54 scripts

ycroissant

pglm:Panel Generalized Linear Models

Estimation of panel models for glm-like models: this includes binomial models (logit and probit), count models (poisson and negbin) and ordered models (logit and probit), as described in: Baltagi (2013) Econometric Analysis of Panel Data <doi:10.1007/978-3-030-53953-5> Hsiao (2014) Analysis of Panel Data <doi:10.1017/CBO9781139839327> and Croissant and Millo (2018), Panel Data Econometrics with R <doi:10.1002/9781119504641>.

Maintained by Yves Croissant. Last updated 1 years ago.

3.8 match 4.34 score 158 scripts 1 dependents

statimagcoll

RESI:Robust Effect Size Index (RESI) Estimation

Summarize model output using a robust effect size index. The index is introduced in Vandekar, Tao, & Blume (2020) <doi:10.1007/s11336-020-09698-2>.

Maintained by Megan Jones. Last updated 13 days ago.

3.8 match 4.30 score 20 scripts

rezamoammadi

liver:"Eating the Liver of Data Science"

Offers a suite of helper functions to simplify various data science techniques for non-experts. This package aims to enable individuals with only a minimal level of coding knowledge to become acquainted with these techniques in an accessible manner. Inspired by an ancient Persian idiom, we liken this process to "eating the liver of data science," suggesting a deep and intimate engagement with the field of data science. This package includes functions for tasks such as data partitioning for out-of-sample testing, calculating Mean Squared Error (MSE) to assess prediction accuracy, and data transformations (z-score and min-max). In addition to these helper functions, the 'liver' package also features several intriguing datasets valuable for multivariate analysis.

Maintained by Reza Mohammadi. Last updated 4 months ago.

4.0 match 4.00 score 67 scripts

fsbmat-ufv

ssmodels:Sample Selection Models

In order to facilitate the adjustment of the sample selection models existing in the literature, we created the 'ssmodels' package. Our package allows the adjustment of the classic Heckman model (Heckman (1976), Heckman (1979) <doi:10.2307/1912352>), and the estimation of the parameters of this model via the maximum likelihood method and two-step method, in addition to the adjustment of the Heckman-t models, introduced in the literature by Marchenko and Genton (2012) <doi:10.1080/01621459.2012.656011> and the Heckman-Skew model introduced in the literature by Ogundimu and Hutton (2016) <doi:10.1111/sjos.12171>. We also implemented functions to adjust the generalized version of the Heckman model, introduced by Bastos, Barreto-Souza, and Genton (2021) <doi:10.5705/ss.202021.0068>, that allows the inclusion of covariables to the dispersion and correlation parameters and a function to adjust the Heckman-BS model introduced by Bastos and Barreto-Souza (2020) <doi:10.1080/02664763.2020.1780570> that uses the Birnbaum-Saunders distribution as a joint distribution of the selection and primary regression variables.

Maintained by Fernando de Souza Bastos. Last updated 2 years ago.

3.8 match 1 stars 4.00 score 5 scripts

keithmcnulty

peopleanalyticsdata:Data Sets for Keith McNulty's Handbook of Regression Modeling in People Analytics

Data sets for statistical inference modeling related to People Analytics. Contains various data sets from the book 'Handbook of Regression Modeling in People Analytics' by Keith McNulty (2020).

Maintained by Keith McNulty. Last updated 4 years ago.

4.0 match 6 stars 3.71 score 17 scripts

rudjer

REBayes:Empirical Bayes Estimation and Inference

Kiefer-Wolfowitz maximum likelihood estimation for mixture models and some other density estimation and regression methods based on convex optimization. See Koenker and Gu (2017) REBayes: An R Package for Empirical Bayes Mixture Methods, Journal of Statistical Software, 82, 1--26, <DOI:10.18637/jss.v082.i08>.

Maintained by Roger Koenker. Last updated 9 months ago.

3.8 match 3 stars 3.90 score 27 scripts 1 dependents

agbarnett

dobson:Data from the GLM Book by Dobson and Barnett

Example datasets from the book "An Introduction to Generalised Linear Models" (Year: 2018, ISBN:9781138741515) by Dobson and Barnett.

Maintained by Adrian Barnett. Last updated 6 years ago.

datasets generalized-linear-models

3.6 match 3 stars 4.05 score 74 scripts

oskarallerslev

lifepack:Insurance Reserve Calculations

Calculates insurance reserves and equivalence premiums using advanced numerical methods, including the Runge-Kutta algorithm and product integrals for transition probabilities. This package is useful for actuarial analyses and life insurance modeling, facilitating accurate financial projections.

Maintained by Oskar Allerslev. Last updated 2 months ago.

4.1 match 3.30 score

harrison4192

TidyConsultant:Tidy Consultant Universe

Loads the 5 packages in the Tidy Consultant Universe. This collection of packages is useful for anyone doing data science, data analysis, or quantitative consulting. The functions in these packages range from data cleaning, data validation, data binning, statistical modeling, and file exporting.

Maintained by Harrison Tietze. Last updated 9 months ago.

3.8 match 8 stars 3.60 score 2 scripts

sunsmiling

PPtreeregViz:Projection Pursuit Regression Tree Visualization

It was developed as a tool for exploring 'PPTreereg' (Projection Pursuit TREE of REGression). It uses various projection pursuit indexes and 'XAI' (eXplainable Artificial Intelligence) methods to help understand the model by finding connections between the input variables and prediction values of the model. The 'KernelSHAP' (Aas, Jullum and Løland (2019) <arXiv:1903.10464>) algorithm was modified to fit ‘PPTreereg’, and some codes were modified from the 'shapr' package (Sellereite, Nikolai, and Martin Jullum (2020) <doi:10.21105/joss.02027>). The implemented methods help to explore the model at the single instance level as well as at the whole dataset level. Users can compare with other machine learning models by applying it to the 'DALEX' package of 'R'.

Maintained by HyunSun Cho. Last updated 1 years ago.

openblas cpp

4.5 match 2 stars 3.00 score 3 scripts

dsy109

HoRM:Supplemental Functions and Datasets for "Handbook of Regression Methods"

Supplement for the book "Handbook of Regression Methods" by D. S. Young. Some datasets used in the book are included and documented. Wrapper functions are included that simplify the examples in the textbook, such as code for constructing a regressogram and expanding ANOVA tables to reflect the total sum of squares.

Maintained by Derek S. Young. Last updated 9 months ago.

regression-analysis regression-models shiny-apps

3.8 match 3.56 score 73 scripts

arbrazzale

cond:Approximate Conditional Inference for Logistic and Loglinear Models

Higher order likelihood-based inference for logistic and loglinear models.

Maintained by Alessandra R. Brazzale. Last updated 7 years ago.

3.6 match 3.51 score 27 scripts 2 dependents

d-acharya

Dowd:Functions Ported from 'MMR2' Toolbox Offered in Kevin Dowd's Book Measuring Market Risk

'Kevin Dowd's' book Measuring Market Risk is a widely read book in the area of risk measurement by students and practitioners alike. As he claims, 'MATLAB' indeed might have been the most suitable language when he originally wrote the functions, but, with growing popularity of R it is not entirely valid. As 'Dowd's' code was not intended to be error free and were mainly for reference, some functions in this package have inherited those errors. An attempt will be made in future releases to identify and correct them. 'Dowd's' original code can be downloaded from www.kevindowd.org/measuring-market-risk/. It should be noted that 'Dowd' offers both 'MMR2' and 'MMR1' toolboxes. Only 'MMR2' was ported to R. 'MMR2' is more recent version of 'MMR1' toolbox and they both have mostly similar function. The toolbox mainly contains different parametric and non parametric methods for measurement of market risk as well as backtesting risk measurement methods.

Maintained by Dinesh Acharya. Last updated 9 years ago.

3.9 match 3.19 score 154 scripts

mwaldstein

edgarWebR:SEC Filings Access

A set of methods to access and parse live filing information from the U.S. Securities and Exchange Commission (SEC - <https://www.sec.gov/>) including company and fund filings along with all associated metadata.

Maintained by Micah J Waldstein. Last updated 4 years ago.

edgar fund-search xbrl

1.9 match 78 stars 6.53 score 43 scripts

cran

unifed:The Unifed Distribution

Probability functions, family for glm() and Stan code for working with the unifed distribution (Quijano Xacur, 2019; <doi:10.1186/s40488-019-0102-6>).

Maintained by Oscar Alberto Quijano Xacur. Last updated 3 years ago.

4.0 match 2.70 score

cran

HDtweedie:The Lasso for Tweedie's Compound Poisson Model Using an IRLS-BMD Algorithm

The Tweedie lasso model implements an iteratively reweighed least square (IRLS) strategy that incorporates a blockwise majorization decent (BMD) method, for efficiently computing solution paths of the (grouped) lasso and the (grouped) elastic net methods.

Maintained by Wei Qian. Last updated 3 years ago.

fortran openblas

4.0 match 3 stars 2.59 score 43 scripts 1 dependents

cran

splm:Econometric Models for Spatial Panel Data

ML and GM estimation and diagnostic testing of econometric models for spatial panel data.

Maintained by Giovanni Millo. Last updated 1 years ago.

3.4 match 10 stars 3.01 score

hrbrmstr

epidata:Tools to Retrieve Economic Policy Institute Data Library Extracts

The Economic Policy Institute (<http://www.epi.org/>) provides researchers, media, and the public with easily accessible, up-to-date, and comprehensive historical data on the American labor force. It is compiled from Economic Policy Institute analysis of government data sources. Use it to research wages, inequality, and other economic indicators over time and among demographic groups. Data is usually updated monthly.

Maintained by Bob Rudis. Last updated 5 years ago.

1.9 match 20 stars 5.45 score 28 scripts

juvlac

camerondata:Datasets from "Microeconometrics: Methods and Applications" by Cameron and Trivedi

Quick and easy access to datasets that let you replicate the empirical examples in Cameron and Trivedi (2005) "Microeconometrics: Methods and Applications" (ISBN: 9780521848053).The data are available as soon as you install and load the package (lazy-loading) as data frames. The documentation includes reference to chapter sections and page numbers where the datasets are used.

Maintained by Juliana Vega-Lacorte. Last updated 3 years ago.

3.8 match 2.70 score 1 scripts

blunde1

agtboost:Adaptive and Automatic Gradient Boosting Computations

Fast and automatic gradient tree boosting designed to avoid manual tuning and cross-validation by utilizing an information theoretic approach. This makes the algorithm adaptive to the dataset at hand; it is completely automatic, and with minimal worries of overfitting. Consequently, the speed-ups relative to state-of-the-art implementations can be in the thousands while mathematical and technical knowledge required on the user are minimized.

Maintained by Berent Ånund Strømnes Lunde. Last updated 3 years ago.

cpp

5.6 match 1.72 score 52 scripts

gl-li

ExamPAData:Data Sets for Predictive Analytics Exam

Contains all data sets for Exam PA: Predictive Analytics at <https://exampa.net/>.

Maintained by Guanglai Li. Last updated 3 years ago.

8.5 match 1.00 score

prabhanjan-tattar

ACSWR:A Companion Package for the Book "A Course in Statistics with R"

A book designed to meet the requirements of masters students. Tattar, P.N., Suresh, R., and Manjunath, B.G. "A Course in Statistics with R", J. Wiley, ISBN 978-1-119-15272-9.

Maintained by Prabhanjan Tattar. Last updated 10 years ago.

4.0 match 2.03 score 106 scripts

ropengov

kelaopendata:Access open data from National social insurance institution of Finland

Designed to simplify and speed up access to open data from National social insurance institution of Finland (KELA) published at <https://www.avoindata.fi/data/fi/organization/kela>, the kelaopendata package offers researchers and analysts a set of tools to obtain data and metadata for a wide range of applications.

Maintained by Markus Kainu. Last updated 4 months ago.

data open-data social-security-data

3.3 match 1 stars 2.40 score

torabi-uofm

SumcaVer1:Mean Square Prediction Error Estimation in Small Area Estimation

Estimation of mean squared prediction error of a small area predictor is provided. In particular, the recent method of Simple, Unified, Monte-Carlo Assisted approach for the mean squared prediction error estimation of small area predictor is provided. We also provide other existing methods of mean squared prediction error estimation such as jackknife method for the mixed logistic model.

Maintained by Mahmoud Torabi. Last updated 8 months ago.

7.4 match 1.00 score

ffqueiroz

robustbetareg:Robust Beta Regression

Robust estimators for the beta regression, useful for modeling bounded continuous data. Currently, four types of robust estimators are supported. They depend on a tuning constant which may be fixed or selected by a data-driven algorithm also implemented in the package. Diagnostic tools associated with the fitted model, such as the residuals and goodness-of-fit statistics, are implemented. Robust Wald-type tests are available. More details about robust beta regression are described in Maluf et al. (2022) <arXiv:2209.11315>.

Maintained by Felipe Queiroz. Last updated 2 years ago.

4.0 match 1.70 score 5 scripts

harryjalexander

ascentTraining:Ascent Training Datasets

Datasets to be used primarily in conjunction with Ascent training materials but also for the book 'SAMS Teach Yourself R in 24 Hours' (ISBN: 978-0-672-33848-9). Version 1.0-7 is largely for use with the book; however, version 1.1 has a much greater focus on use with training materials, whilst retaining compatibility with the book.

Maintained by Harry Alexander. Last updated 3 years ago.

6.0 match 1.00 score 2 scripts

cran

ssym:Fitting Semi-Parametric log-Symmetric Regression Models

Set of tools to fit a semi-parametric regression model suitable for analysis of data sets in which the response variable is continuous, strictly positive, asymmetric and possibly, censored. Under this setup, both the median and the skewness of the response variable distribution are explicitly modeled by using semi-parametric functions, whose non-parametric components may be approximated by natural cubic splines or P-splines. Supported distributions for the model error include log-normal, log-Student-t, log-power-exponential, log-hyperbolic, log-contaminated-normal, log-slash, Birnbaum-Saunders and Birnbaum-Saunders-t distributions.

Maintained by Luis Hernando Vanegas. Last updated 2 years ago.

4.0 match 1.48 score 1 dependents

hugometric

causalweight:Estimation Methods for Causal Inference Based on Inverse Probability Weighting

Various estimators of causal effects based on inverse probability weighting, doubly robust estimation, and double machine learning. Specifically, the package includes methods for estimating average treatment effects, direct and indirect effects in causal mediation analysis, and dynamic treatment effects. The models refer to studies of Froelich (2007) <doi:10.1016/j.jeconom.2006.06.004>, Huber (2012) <doi:10.3102/1076998611411917>, Huber (2014) <doi:10.1080/07474938.2013.806197>, Huber (2014) <doi:10.1002/jae.2341>, Froelich and Huber (2017) <doi:10.1111/rssb.12232>, Hsu, Huber, Lee, and Lettry (2020) <doi:10.1002/jae.2765>, and others.

Maintained by Hugo Bodory. Last updated 8 months ago.

3.5 match 2 stars 1.64 score 22 scripts

cran

SMPracticals:Practicals for Use with Davison (2003) Statistical Models

Contains the datasets and a few functions for use with the practicals outlined in Appendix A of the book Statistical Models (Davison, 2003, Cambridge University Press), which can be found at <doi:10.1017/CBO9780511815850>.

Maintained by Alessandra R. Brazzale. Last updated 1 years ago.

3.8 match 1.48 score 1 dependents

imranshakoor

DataSetsUni:A Collection of Univariate Data Sets

A collection of widely used univariate data sets of various applied domains on applications of distribution theory. The functions allow researchers and practitioners to quickly, easily, and efficiently access and use these data sets. The data are related to different applied domains and as follows: Bio-medical, survival analysis, medicine, reliability analysis, hydrology, actuarial science, operational research, meteorology, extreme values, quality control, engineering, finance, sports and economics. The total 100 data sets are documented along with associated references for further details and uses.

Maintained by Muhammad Imran. Last updated 2 years ago.

5.3 match 1.00 score 1 scripts

randhirbilkhu

eltr:Utilise Catastrophe Model Event Loss Table Outputs

Provides a tool to run Monte Carlo simulation of catastrophe model event loss tables, using a Poisson frequency and Beta severity distribution.

Maintained by Randhir Bilkhu. Last updated 4 years ago.

1.2 match 2 stars 4.48 score 4 scripts

kainhofer

LifeInsuranceContracts:Framework for Traditional Life Insurance Contracts

Use of this package is deprecated. It has been renamed to 'LifeInsureR'.

Maintained by Reinhold Kainhofer. Last updated 1 years ago.

3.0 match 1.70 score

hrecht

censusapi:Retrieve Data from the Census APIs

A wrapper for the U.S. Census Bureau APIs that returns data frames of Census data and metadata. Available datasets include the Decennial Census, American Community Survey, Small Area Health Insurance Estimates, Small Area Income and Poverty Estimates, Population Estimates and Projections, and more.

Maintained by Hannah Recht. Last updated 11 days ago.

census census-api census-data demographics open-data

0.5 match 174 stars 9.76 score 708 scripts 6 dependents

cran

forward:Robust Analysis using Forward Search

Robust analysis using forward search in linear and generalized linear regression models, as described in Atkinson, A.C. and Riani, M. (2000), Robust Diagnostic Regression Analysis, First Edition. New York: Springer.

Maintained by Ken Beath. Last updated 6 months ago.

4.0 match 1.18 score 15 scripts

jbhasse

SystemicR:Monitoring Systemic Risk

The past decade has demonstrated an increased need to better understand risks leading to systemic crises. This framework offers scholars, practitioners and policymakers a useful toolbox to explore such risks in financial systems. Specifically, this framework provides popular econometric and network measures to monitor systemic risk and to measure the consequences of regulatory decisions. These systemic risk measures are based on the frameworks of Adrian and Brunnermeier (2016) <doi:10.1257/aer.20120555> and Billio, Getmansky, Lo and Pelizzon (2012) <doi:10.1016/j.jfineco.2011.12.010>.

Maintained by Jean-Baptiste Hasse. Last updated 5 years ago.

3.4 match 2 stars 1.30 score 1 scripts

agi-lab

SPLICE:Synthetic Paid Loss and Incurred Cost Experience (SPLICE) Simulator

An extension to the individual claim simulator called 'SynthETIC' (on CRAN), to simulate the evolution of case estimates of incurred losses through the lifetime of an insurance claim. The transactional simulation output now comprises key dates, and both claim payments and revisions of estimated incurred losses. An initial set of test parameters, designed to mirror the experience of a real insurance portfolio, were set up and applied by default to generate a realistic test data set of incurred histories (see vignette). However, the distributional assumptions used to generate this data set can be easily modified by users to match their experiences. Reference: Avanzi B, Taylor G, Wang M (2021) "SPLICE: A Synthetic Paid Loss and Incurred Cost Experience Simulator" <arXiv:2109.04058>.

Maintained by Melantha Wang. Last updated 1 years ago.

0.8 match 6 stars 5.58 score 14 scripts

vanzanden

ggsolvencyii:A 'ggplot2'-Plot of Composition of Solvency II SCR: SF and IM

An implementation of 'ggplot2'-methods to present the composition of Solvency II Solvency Capital Requirement (SCR) as a series of concentric circle-parts. Solvency II (Solvency 2) is European insurance legislation, coming in force by the delegated acts of October 10, 2014. <https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=OJ%3AL%3A2015%3A012%3ATOC>. Additional files, defining the structure of the Standard Formula (SF) method of the SCR-calculation are provided. The structure files can be adopted for localization or for insurance companies who use Internal Models (IM). Options are available for combining smaller components, horizontal and vertical scaling, rotation, and plotting only some circle-parts. With outlines and connectors several SCR-compositions can be compared, for example in ORSA-scenarios (Own Risk and Solvency Assessment).

Maintained by Marco van Zanden. Last updated 6 years ago.

0.8 match 3 stars 5.58 score 63 scripts

cran

datarobot:'DataRobot' Predictive Modeling API

For working with the 'DataRobot' predictive modeling platform's API <https://www.datarobot.com/>.

Maintained by AJ Alon. Last updated 1 years ago.

1.2 match 2 stars 3.48 score

p0bs

p0bservations:Assorted Functions and Observations by P0bs

Provides assorted functions by p0bs.

Maintained by Robin Penfold. Last updated 4 months ago.

1.8 match 2.30 score 1 scripts

rainers48

tsapp:Time Series, Analysis and Application

Accompanies the book Rainer Schlittgen and Cristina Sattarhoff (2020) <https://www.degruyter.com/view/title/575978> "Angewandte Zeitreihenanalyse mit R, 4. Auflage" . The package contains the time series and functions used therein. It was developed over many years teaching courses about time series analysis.

Maintained by Rainer Schlittgen. Last updated 3 years ago.

4.0 match 1.00 score 1 scripts

cran

tea:Threshold Estimation Approaches

Different approaches for selecting the threshold in generalized Pareto distributions. Most of them are based on minimizing the AMSE-criterion or at least by reducing the bias of the assumed GPD-model. Others are heuristically motivated by searching for stable sample paths, i.e. a nearly constant region of the tail index estimator with respect to k, which is the number of data in the tail. The third class is motivated by graphical inspection. In addition, a sequential testing procedure for GPD-GoF-tests is also implemented here.

Maintained by Johannes Ossberger. Last updated 5 years ago.

3.8 match 1.00 score

spedygiorgio

mbbefd:Maxwell Boltzmann Bose Einstein Fermi Dirac Distribution and Destruction Rate Modelling

Distributions that are typically used for exposure rating in general insurance, in particular to price reinsurance contracts. The vignette shows code snippets to fit the distribution to empirical data. See, e.g., Bernegger (1997) <doi:10.2143/AST.27.1.563208> freely available on-line.

Maintained by Christophe Dutang. Last updated 22 days ago.

actuarial destruction-rate-modeling reinsurance cpp

0.5 match 15 stars 7.05 score 99 scripts

extremestats

DATAstudio:The Research Data Warehouse of Miguel de Carvalho

Pulls together a collection of datasets from Miguel de Carvalho research articles. Including, for example: - de Carvalho (2012) <doi:10.1016/j.jspi.2011.08.016>; - de Carvalho et al (2012) <doi:10.1080/03610926.2012.709905>; - de Carvalho et al (2012) <doi:10.1016/j.econlet.2011.09.007>); - de Carvalho and Davison (2014) <doi:10.1080/01621459.2013.872651>; - de Carvalho and Rua (2017) <doi:10.1016/j.ijforecast.2015.09.004>.

Maintained by Miguel de Carvalho. Last updated 3 years ago.

3.6 match 1.00 score 2 scripts

extremestats

spearmanCI:Jackknife Euclidean / Empirical Likelihood Inference for Spearman's Rho

Functions for conducting jackknife Euclidean / empirical likelihood inference for Spearman's rho (de Carvalho and Marques (2012) <doi:10.1080/10920277.2012.10597644>).

Maintained by Miguel de Carvalho. Last updated 10 months ago.

3.6 match 1.00 score 2 scripts

mattheaphy

offsetreg:An Extension of 'Tidymodels' Supporting Offset Terms

Extend the 'tidymodels' ecosystem <https://www.tidymodels.org/> to enable the creation of predictive models with offset terms. Models with offsets are most useful when working with count data or when fitting an adjustment model on top of an existing model with a prior expectation. The former situation is common in insurance where data is often weighted by exposures. The latter is common in life insurance where industry mortality tables are often used as a starting point for setting assumptions.

Maintained by Matt Heaphy. Last updated 21 days ago.

0.8 match 2 stars 4.48 score 4 scripts

visbanking

fdicdata:Accessing FDIC Bank Data

A system provides a set of functions for working with data from the Federal Deposit Insurance Corporation (FDIC), including retrieving financial data for FDIC-insured institutions and accessing the data taxonomy.

Maintained by Ugur Dar. Last updated 2 years ago.

0.8 match 9 stars 3.65 score 3 scripts

guillaumebiessy

WH:Enhanced Implementation of Whittaker-Henderson Smoothing

An enhanced implementation of Whittaker-Henderson smoothing for the gradation of one-dimensional and two-dimensional actuarial tables used to quantify Life Insurance risks. 'WH' is based on the methods described in Biessy (2023) <doi:10.48550/arXiv.2306.06932>. Among other features, it generalizes the original smoothing algorithm to maximum likelihood estimation, automatically selects the smoothing parameter(s) and extrapolates beyond the range of data.

Maintained by Guillaume Biessy. Last updated 4 months ago.

actuarial-science bayesian-methods mortality-estimation smoothing-methods

0.5 match 9 stars 5.35 score 6 scripts

ashesitr

reservr:Fit Distributions and Neural Networks to Censored and Truncated Data

Define distribution families and fit them to interval-censored and interval-truncated data, where the truncation bounds may depend on the individual observation. The defined distributions feature density, probability, sampling and fitting methods as well as efficient implementations of the log-density log f(x) and log-probability log P(x0 <= X <= x1) for use in 'TensorFlow' neural networks via the 'tensorflow' package. Allows training parametric neural networks on interval-censored and interval-truncated data with flexible parameterization. Applications include Claims Development in Non-Life Insurance, e.g. modelling reporting delay distributions from incomplete data, see Bücher, Rosenstock (2022) <doi:10.1007/s13385-022-00314-4>.

Maintained by Alexander Rosenstock. Last updated 9 months ago.

openblas cpp openmp

0.5 match 5 stars 5.35 score 9 scripts

eeethb

edgedata:Datasets that Support the EDGE Server DIY Logic

Datasets from most recent Center for Consumer Information and Insurance Oversight (CCIIO) DIY entry in a tidy format. These support the Centers for Medicare and Medicaid Services' (CMS) risk adjustment Do-It-Yourself (DIY) process, which allows health insurance issuers to calculate member risk profiles under the Health and Human Services-Hierarchical Condition Categories (HHS-HCC) regression model. This regression model is used to calculate risk adjustment transfers. Risk adjustment is a selection mitigation program implemented under the Patient Protection and Affordable Care Act (ACA or Obamacare) in the USA. Under the ACA, health insurance issuers submit claims data to CMS in order for CMS to calculate a risk score under the HHS-HCC regression model. However, CMS does not inform issuers of their average risk score until after the data submission deadline. These data sets can be used by issuers to calculate their average risk score mid-year. More information about risk adjustment and the HHS-HCC model can be found here: <https://www.cms.gov/mmrr/Articles/A2014/MMRR2014_004_03_a03.html>.

Maintained by Ethan Brockmann. Last updated 3 years ago.

0.9 match 1 stars 2.70 score 1 scripts

ropensci

rfema:Access the openFEMA API

`rfema` allows users to access The Federal Emergency Management Agency's (FEMA) publicly available data through their API. The package provides a set of functions to easily navigate and access data from the National Flood Insurance Program along with FEMA's various disaster aid programs, including the Hazard Mitigation Grant Program, the Public Assistance Grant Program, and the Individual Assistance Grant Program.

Maintained by Dylan Turner. Last updated 24 days ago.

0.5 match 9 stars 4.73 score 5 scripts

windwill

cascsim:Casualty Actuarial Society Individual Claim Simulator

It is an open source insurance claim simulation engine sponsored by the Casualty Actuarial Society. It generates individual insurance claims including open claims, reopened claims, incurred but not reported claims and future claims. It also includes claim data fitting functions to help set simulation assumptions. It is useful for claim level reserving analysis. Parodi (2013) <https://www.actuaries.org.uk/documents/triangle-free-reserving-non-traditional-framework-estimating-reserves-and-reserve-uncertainty>.

Maintained by Kailan Shang. Last updated 5 years ago.

0.8 match 2.99 score 98 scripts

cran

qlifetable:Managing and Building of Quarterly Life Tables

Manages, builds and computes statistics and datasets for the construction of quarterly (sub-annual) life tables by exploiting micro-data from either a general or an insured population. References: Pavía and Lledó (2022) <doi:10.1111/rssa.12769>. Pavía and Lledó (2023) <doi:10.1017/asb.2023.16>. Acknowledgements: The authors wish to thank Consellería de Educación, Universidades y Empleo, Generalitat Valenciana (grant AICO/2021/257), Ministerio de Ciencia e Innovación (grant PID2021-128228NB-I00) and Fundación Mapfre (grant 'Modelización espacial e intra-anual de la mortalidad en España. Una herramienta automática para el cálculo de productos de vida') for supporting this research.

Maintained by Jose M. Pavía. Last updated 9 months ago.

2.2 match 1.00 score

techtonique

esgtoolkit:Toolkit for Monte Carlo Simulations

A toolkit for Monte Carlo Simulations in Finance, Economics, Insurance, Physics. Multiple simulation models can be created by combining building blocks provided in the package.

Maintained by T. Moudiki. Last updated 1 months ago.

diffusion-model diffusion-models monte-carlo-methods monte-carlo-simulation montecarlo-simulation scenario-analysis scenario-creator scenario-generation simulation stochastic-differential-equations stochastic-processes stochastic-simulation cpp

0.5 match 11 stars 3.97 score 28 scripts

cran

ADLP:Accident and Development Period Adjusted Linear Pools for Actuarial Stochastic Reserving

Loss reserving generally focuses on identifying a single model that can generate superior predictive performance. However, different loss reserving models specialise in capturing different aspects of loss data. This is recognised in practice in the sense that results from different models are often considered, and sometimes combined. For instance, actuaries may take a weighted average of the prediction outcomes from various loss reserving models, often based on subjective assessments. This package allows for the use of a systematic framework to objectively combine (i.e. ensemble) multiple stochastic loss reserving models such that the strengths offered by different models can be utilised effectively. Our framework is developed in Avanzi et al. (2023). Firstly, our criteria model combination considers the full distributional properties of the ensemble and not just the central estimate - which is of particular importance in the reserving context. Secondly, our framework is that it is tailored for the features inherent to reserving data. These include, for instance, accident, development, calendar, and claim maturity effects. Crucially, the relative importance and scarcity of data across accident periods renders the problem distinct from the traditional ensemble techniques in statistical learning. Our framework is illustrated with a complex synthetic dataset. In the results, the optimised ensemble outperforms both (i) traditional model selection strategies, and (ii) an equally weighted ensemble. In particular, the improvement occurs not only with central estimates but also relevant quantiles, such as the 75th percentile of reserves (typically of interest to both insurers and regulators). Reference: Avanzi B, Li Y, Wong B, Xian A (2023) "Ensemble distributional forecasting for insurance loss reserving" <doi:10.48550/arXiv.2206.08541>.

Maintained by Yanfeng Li. Last updated 11 months ago.

0.8 match 2.70 score

aadler

MBBEFDLite:Statistical Functions for the Maxwell-Boltzmann-Bose-Einstein-Fermi-Dirac (MBBEFD) Family of Distributions

Provides probability mass, distribution, quantile, random variate generation, and method-of-moments parameter fitting for the MBBEFD family of distributions used in insurance modeling as described in Bernegger (1997) <doi:10.2143/AST.27.1.563208> without any external dependencies.

Maintained by Avraham Adler. Last updated 1 months ago.

0.5 match 1 stars 3.65 score

mehdichelh

eiopaR:Access to RFR (Risk-Free Rate) Curves Produced by the EIOPA

Provides EIOPA (European Insurance And Occupational Pensions Authority) risk-free rates. Please note that the author of this package is not affiliated with EIOPA. The data is accessed through a REST API available at <https://mehdiechchelh.com/api/>.

Maintained by Mehdi Echchelh. Last updated 4 years ago.

0.5 match 3 stars 3.18 score 2 scripts

zangt2

obcost:Obesity Cost Database

This database contains necessary data relevant to medical costs on obesity throughout the United States. This database, in form of an R package, could output necessary data frames relevant to obesity costs, where the clients could easily manipulate the output using difference parameters, e.g. relative risks for each illnesses. This package contributes to parts of our published journal named "Modeling the Economic Cost of Obesity Risk and Its Relation to the Health Insurance Premium in the United States: A State Level Analysis". Please use the following citation for the journal: Woods Thomas, Tatjana Miljkovic (2022) "Modeling the Economic Cost of Obesity Risk and Its Relation to the Health Insurance Premium in the United States: A State Level Analysis" <doi:10.3390/risks10100197>. The database is composed of the following main tables: 1. Relative_Risks: (constant) Relative risks for a given disease group with a risk factor of obesity; 2. Disease_Cost: (obesity_cost_disease) Supplementary output with all variables related to individual disease groups in a given state and year; 3. Full_Cost: (obesity_cost_full) Complete output with all variables used to make cost calculations, as well as cost calculations in a given state and year; 4. National_Summary: (obesity_cost_national_summary) National summary cost calculations in a given year. Three functions are included to assist users in calling and adjusting the mentioned tables and they are data_load(), data_produce(), and rel_risk_fun().

Maintained by Tianyue Zang. Last updated 2 years ago.

0.8 match 1.70 score

ambarish-chattopadhyay

FSM:Finite Selection Model

Randomized and balanced allocation of units to treatment groups using the Finite Selection Model (FSM). The FSM was originally proposed and developed at the RAND corporation by Carl Morris to enhance the experimental design for the now famous Health Insurance Experiment. See Morris (1979) <doi:10.1016/0304-4076(79)90053-8> for details on the original version of the FSM.

Maintained by Ambarish Chattopadhyay. Last updated 4 years ago.

0.5 match 2.00 score 3 scripts

pleasedontdothat

glmMisrep:Generalized Linear Models Adjusting for Misrepresentation

Fit Generalized Linear Models to continuous and count outcomes, as well as estimate the prevalence of misrepresentation of an important binary predictor. Misrepresentation typically arises when there is an incentive for the binary factor to be misclassified in one direction (e.g., in insurance settings where policy holders may purposely deny a risk status in order to lower the insurance premium). This is accomplished by treating a subset of the response variable as resulting from a mixture distribution. Model parameters are estimated via the Expectation Maximization algorithm and standard errors of the estimates are obtained from closed forms of the Observed Fisher Information. For an introduction to the models and the misrepresentation framework, see Xia et. al., (2023) <https://variancejournal.org/article/73151-maximum-likelihood-approaches-to-misrepresentation-models-in-glm-ratemaking-model-comparisons>.

Maintained by Patrick Rafael. Last updated 11 months ago.

0.8 match 1.30 score

cran

DCL:Claims Reserving under the Double Chain Ladder Model

Statistical modelling and forecasting in claims reserving in non-life insurance under the Double Chain Ladder framework by Martinez-Miranda, Nielsen and Verrall (2012).

Maintained by Maria Dolores Martinez-Miranda. Last updated 3 years ago.

0.5 match 1 stars 1.00 score