| Title: | Data Sets for Discrete Probability Models |
|---|---|
| Description: | 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. |
| Authors: | Christophe Chesneau [aut], Muhammad Imran [aut, cre], M.H Tahir [aut], Farrukh Jamal [aut] |
| Maintainer: | Muhammad Imran <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 0.1.0 |
| Built: | 2025-03-09 02:39:25 UTC |
| Source: | https://github.com/cran/DDPM |
The function gives the observed number of pap smear tests a female took in the last six years for females aged more than 18 years.
data_papdata_pap
data_pap |
A vector of (non-negative integer) count values. |
The data show the observed number of pap smear tests a female took in the last six years for females aged more than 18 years. They were used by Arora and Chaganty (2021) and fitted by the zero-and-k-inflated Poisson distribution.
data_pap gives the observed number of pap smear tests a female took in the last six years for females aged more than 18 years.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Arora, M., & Chaganty, N. R. (2021). EM estimation for zero-and k-inflated Poisson regression Model. Computation, 9(9), 94.
x<-data_pap summary(x) table (x)x<-data_pap summary(x) table (x)
The function gives the frequency distribution of decayed, missing, and filled teeth of children aged 12 years old.
data_teethdata_teeth
data_teeth |
A vector of (non-negative integer) count values. |
The data show the frequency distribution of decayed, missing, and filled teeth of children aged 12 years old. They were used by Moghimbeigi et al. (2008) and fitted by the zero-inflated negative binomial regression modeling for over-dispersed count data with extra zeros.
data_teeth gives the frequency distribution of decayed, missing, and filled teeth of children aged 12 years old.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Moghimbeigi, A., Eshraghian, M. R., Mohammad, K., & Mcardle, B. (2008). Multilevel zero-inflated negative binomial regression modeling for over-dispersed count data with extra zeros. Journal of Applied Statistics, 35(10), 1193-1202.
x<-data_teeth summary(x) table (x)x<-data_teeth summary(x) table (x)
A wide range 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 as follows: 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.
| Package: | DDPM |
| Type: | Package |
| Version: | 0.1.0 |
| Date: | 2023-06-14 |
| License: | GPL-2 |
Muhammad Imran <[email protected]>
Christophe Chesneau <[email protected]>, Muhammad Imran <[email protected]>, M.H Tahir <[email protected]> and Farrukh Jamal <[email protected]>.
The function gives the number of absences of individuals for studying absence proneness.
data_absendata_absen
data_absen |
A vector of (non-negative integer) count values. |
The data show the number of absences of individuals for studying absence proneness. They were used by Sichel (1951) and fitted by the negative binomial distribution.
data_absen gives the number of absences of individuals.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Sichel, H. S. (1951). The estimation of the parameters of a negative binomial distribution with special reference to psychological data. Psychometrika, 16(1), 107-127.
x<-data_absen summary(x) table (x)x<-data_absen summary(x) table (x)
The function gives the number of accident insurance claims based on 16760 policies.
data_claimsdata_claims
data_claims |
A vector of (non-negative integer) count values. |
The data consist of the number of accident insurance claims based on 16760 policies in Mazandaran Province. Recently, they were used by Alshkaki (2016) and fitted by the zero-and-one inflated Poisson distribution.
data_claims gives the number of accident insurance claims based on 16760 policies.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Alshkaki, R. S. A. (2016). On the zero-one inflated Poisson distribution. International Journal of Statistical Distributions and Applications, 2(4), 42-8.
Momeni, F. (2011). The generalized power series distribution and their application. The Journal of Mathematics and Computer Science, 2(4), 691-697.
data_claims, data_claim1, data_claim2, data_claim3, data_claim6, data_claim7
x<-data_claims summary(x) table (x)x<-data_claims summary(x) table (x)
The function gives the number of accidents of women working on Shells for 5 weeks.
data_waccidata_wacci
data_wacci |
A vector of (non-negative integer) count values. |
The data show the number of accidents of women working on Shells for 5 weeks. They were used by Nekoukhou et al. (2013) and fitted by the discrete generalized exponential distribution of a second type.
data_wacci gives the number of accidents of women working on Shells for 5 weeks.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Nekoukhou, V., Alamatsaz, M. H., & Bidram, H. (2013). Discrete generalized exponential distribution of a second type. Statistics, 47(4), 876-887.
x<-data_wacci summary(x) table (x)x<-data_wacci summary(x) table (x)
The function gives the number of accident proneness of individuals.
data_accidata_acci
data_acci |
A vector of (non-negative integer) count values. |
The data show the number of accident proneness of individuals. They were used by Sichel (1951) and fitted by the negative binomial distribution.
data_acci gives the number of accident proneness of individuals.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Sichel, H. S. (1951). The estimation of the parameters of a negative binomial distribution with special reference to psychological data. Psychometrika, 16(1), 107-127.
x<-data_acci summary(x) table (x)x<-data_acci summary(x) table (x)
The function gives the observed number of accidents in a 60-lb shrapnel shop.
data_accidedata_accide
data_accide |
A vector of (non-negative integer) count values. |
The data show the observed number of accidents in a 60-lb shrapnel shop. They were used by Greenwood and Yule (1920) and underlined an inquiry into the nature of frequency distributions.
data_accide gives the observed number of accidents in a 60-lb shrapnel shop.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Greenwood, M., & Yule, G. U. (1920). An inquiry into the nature of frequency distributions representative of multiple happenings with particular reference to the occurrence of multiple attacks of disease or of repeated accidents. Journal of the Royal Statistical Society, 83(2), 255-279.
x<-data_accide summary(x) table (x)x<-data_accide summary(x) table (x)
The function gives the frequency distribution of accidents to Belfast Corporation Transport bus drivers.
data_belfastdata_belfast
data_belfast |
A vector of (non-negative integer) count values. |
The data show the frequency distribution of accidents to Belfast Corporation Transport bus drivers. They were used by Xekalaki (1984) and fitted by the bivariate generalized Waring distribution.
data_belfast gives the frequency distribution of accidents to Belfast Corporation Transport bus drivers.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Xekalaki, E. (1984). The bivariate generalized Waring distribution and its application to accident theory. Journal of the Royal Statistical Society: Series A (General), 147(3), 488-498.
x<-data_belfast summary(x) table (x)x<-data_belfast summary(x) table (x)
The function gives the frequency distribution of accidents to Connecticut general drivers.
data_connecticutdata_connecticut
data_connecticut |
A vector of (non-negative integer) count values. |
The data show the frequency distribution of accidents to Connecticut general drivers. They were used by Xekalaki (1984) and fitted by the bivariate generalized Waring distribution.
data_connecticut gives the frequency distribution of accidents to Connecticut general drivers.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Xekalaki, E. (1984). The bivariate generalized Waring distribution and its application to accident theory. Journal of the Royal Statistical Society: Series A (General), 147(3), 488-498.
x<-data_connecticut summary(x) table (x)x<-data_connecticut summary(x) table (x)
The function gives the number of adult female European red mites on each leaf.
data_mitesdata_mites
data_mites |
A vector of (non-negative integer) count values. |
Twenty-five leaves were selected at random from each of six similar apple trees in an orchard, and the adult female European red mites on each leaf were counted. They were used by Ross and Preece (1985) and studied by the negative binomial distribution.
data_mites gives the number of adult female European red mites on each leaf.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Ross, G. J. S., & Preece, D. A. (1985). The negative binomial distribution. Journal of the Royal Statistical Society: Series D (The Statistician), 34(3), 323-335.
x<-data_mites summary(x) table (x)x<-data_mites summary(x) table (x)
The function gives the number of observed count of accidents of 647 female workers in an ammunition factory.
data_ammunitiondata_ammunition
data_ammunition |
A vector of (non-negative integer) count values. |
The data consists of the number of accidents of 647 female workers in an ammunition factory. Recently, they were used by Zhang et al. (2016) and fitted by the zero-and-one inflated Poisson distribution.
data_ammunition gives the number of observed count of accidents of 647 female workers in an ammunition factory.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Zhang, C., Tian, G. L., & Ng, K. W. (2016). Properties of the zero-and-one inflated Poisson distribution and likelihood-based inference methods. Statistics and its Interface, 9(1), 11-32.
Greenwood, M., & Yule, G. U. (1920). An inquiry into the nature of frequency distributions representative of multiple happenings with particular reference to the occurrence of multiple attacks of disease or repeated accidents. Journal of the Royal Statistical Society, 83(2), 255-279.
Bohning, D. (1998). Zero-inflated Poisson models and CA MAN: A tutorial collection of evidence. Biometrical Journal: Journal of Mathematical Methods in Biosciences, 40(7), 833-843.
x<-data_ammunition summary(x) table (x)x<-data_ammunition summary(x) table (x)
The function gives the frequency distribution of the number of antenatal care service visits of 6450 women surveyed in EDHS 2016.
data_antenataldata_antenatal
data_antenatal |
A vector of (non-negative integer) count values. |
The data set consists of the number of antenatal care service visit of 6450 women surveyed in EDHS 2016. Recently, they were used by Bekalo and Kebede (2021) and fitted by the zero-inflated models for count data.
data_antenatal gives the observed frequencies of the number of antenatal care service visits.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Bekalo, D. B., & Kebede, D. T. (2021). Zero-inflated models for count data: an application to the number of antenatal care service visits. Annals of Data Science, 8, 683-708.
x<-data_antenatal summary(x) table (x)x<-data_antenatal summary(x) table (x)
The function gives the frequency distribution of the use of antenatal care services in 2011 in Ethiopia.
data_antendata_anten
data_anten |
A vector of (non-negative integer) count values. |
The data contain the frequency distribution of the use of antenatal care services in 2011 in Ethiopia. They were used by Assefa and Tadesse (2017) and fitted by the zero-inflated negative binomial model.
data_anten gives the frequency distribution of the use of antenatal care services in 2011 in Ethiopia.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Assefa, E., & Tadesse, M. (2017). Factors related to the use of antenatal care services in Ethiopia: application of the zero-inflated negative binomial model. Women & Health, 57(7), 804-821.
x<-data_anten summary(x) table (x)x<-data_anten summary(x) table (x)
The function gives the frequency distributions of the number of roots produced by 270 shoots of the apple cultivar Trajan.
data_rootdata_root
data_root |
A vector of (non-negative integer) count values. |
The data show the frequency distributions of the number of roots produced by 270 shoots of the apple cultivar Trajan. They were used by Rodrigues (2003) and fitted in the context of the Bayesian analysis of zero-inflated distributions.
data_root gives the frequency distributions of the number of roots.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Rodrigues, J. (2003). Bayesian analysis of zero-inflated distributions. Communications in Statistics-Theory and Methods, 32(2), 281-289.
x<-data_root summary(x) table (x)x<-data_root summary(x) table (x)
The function gives the daily number of COVID-19 new cases in Argentina.
data_argcoviddata_argcovid
data_argcovid |
A vector of (non-negative integer) count values. |
The data show the daily COVID-19 new cases of Argentina of 80 days, that is recorded from 12 March to 30 May 2020. Recently, they were used by Ibrahim and Almetwally (2021) and fitted by the discrete marshall-Olkin Lomax distribution.
data_argcovid gives the daily number of COVID-19 new cases in Argentina.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Ibrahim, G. M., & Almetwally, E. M. (2021). Discrete marshall-Olkin lomax distribution application of covid-19. Biomedical journal of Scientific & Technical Research, 32(5), 2021.
data_COVIDd, data_Algeriacovid, data_Bosniacovid
x<-data_argcovid summary(x) table (x)x<-data_argcovid summary(x) table (x)
The function gives the observed number of asynaptic in onion plants.
data_as1data_as1
data_as1 |
A vector of (non-negative integer) count values. |
The data represent the observed number of asynaptic in onion plants. They were used by Jain (1959) and fitted by the negative binomial distribution.
data_as1 gives the observed number of asynaptic in onion plants.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Jain, S. K. (1959). Fitting the negative binomial distribution to some data on asynaptic behavior of chromosomes. Genetica, 30(1), 108-122.
data_p806_7, data_p806_8, data_p806_9
x<-data_as1 summary(x) table (x)x<-data_as1 summary(x) table (x)
The function gives the number of major Atlantic hurricanes.
data_hurricanesdata_hurricanes
data_hurricanes |
A vector of (non-negative integer) count values. |
The data show the number of major Atlantic hurricanes per year to have made landfall in the US from 1987 through 2012. Recently, they were used by Beckett et al. (2014) and fitted by the zero-inflated Poisson (ZIP) distribution.
data_hurricanes gives the number of major Atlantic hurricanes per year to have made landfall in the US from 1987 through 2012.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Beckett, S., Jee, J., Ncube, T., Pompilus, S., Washington, Q., Singh, A., & Pal, N. (2014). Zero-inflated Poisson (ZIP) distribution: Parameter estimation and applications to model data from natural calamities. Involve, a Journal of Mathematics, 7(6), 751-767.
x<-data_hurricanes summary(x) table (x)x<-data_hurricanes summary(x) table (x)
The function gives the number of automobile insurance third-party liability portfolios of Belgium in 1958.
data_claim3data_claim3
data_claim3 |
A vector of (non-negative integer) count values. |
The data show the number of automobile insurance third party liability portfolios of Belgium 1958. They were used by Denuit (1997) and fitted by the zero-inflated Poisson (ZIP) distribution.
data_claim3 gives the number of automobile insurance third-party liability portfolios in Belgium in 1958.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Denuit, M. (1997). A new distribution of Poisson-type for the number of claims. ASTIN Bulletin: The Journal of the IAA, 27(2), 229-242.
data_claims, data_claim1, data_claim2
x<-data_claim3 summary(x) table (x)x<-data_claim3 summary(x) table (x)
The function gives the number of automobile insurance third-party liability portfolios.
data_claim1data_claim1
data_claim1 |
A vector of (non-negative integer) count values. |
The data show the number of automobile insurance third-party liability portfolios in Belgium 1975-76. Recently, they were used by Denuit (1997) and fitted by the zero-inflated Poisson (ZIP) distribution.
data_claim1 gives the number of automobile insurance third-party liability portfolios.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Denuit, M. (1997). A new distribution of Poisson-type for the number of claims. ASTIN Bulletin: The Journal of the IAA, 27(2), 229-242.
x<-data_claim1 summary(x) table (x)x<-data_claim1 summary(x) table (x)
The function gives the number of automobile insurance third-party liability portfolios in Great Britain 1968.
data_claim4data_claim4
data_claim4 |
A vector of (non-negative integer) count values. |
The data show the number of automobile insurance third party liability portfolios in Great Britain 1968. They were used by Denuit (1997) and fitted by the zero-inflated Poisson (ZIP) distribution.
data_claim4 gives the number of automobile insurance third-party liability portfolios in Great Britain 1968.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Denuit, M. (1997). A new distribution of Poisson-type for the number of claims. ASTIN Bulletin: The Journal of the IAA, 27(2), 229-242.
data_claims, data_claim1, data_claim2, data_claim3
x<-data_claim4 summary(x) table (x)x<-data_claim4 summary(x) table (x)
The function gives the number of automobile insurance third-party liability portfolios in Belgium 1993.
data_claim7data_claim7
data_claim7 |
A vector of (non-negative integer) count values. |
The data show the number of automobile insurance third-party liability portfolios in Belgium 1993. They were used by Denuit (1997) and fitted by the zero-inflated Poisson (ZIP) distribution.
data_claim7 gives the number of automobile insurance third-party liability portfolios in Belgium 1993.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Denuit, M. (1997). A new distribution of Poisson-type for the number of claims. ASTIN Bulletin: The Journal of the IAA, 27(2), 229-242.
data_claims, data_claim1, data_claim2, data_claim3, data_claim4, data_claim5, data_claim6
x<-data_claim7 summary(x) table (x)x<-data_claim7 summary(x) table (x)
The function gives the number of automobile insurance third-party liability portfolios in Belgium 1994.
data_claim8data_claim8
data_claim8 |
A vector of (non-negative integer) count values. |
The data show the number of automobile insurance third-party liability portfolios in Belgium 1994. They were used by Denuit (1997) and fitted by the zero-inflated Poisson (ZIP) distribution.
data_claim8 gives the number of automobile insurance third-party liability portfolios in Belgium 1994.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Denuit, M. (1997). A new distribution of Poisson-type for the number of claims. ASTIN Bulletin: The Journal of the IAA, 27(2), 229-242.
data_claims, data_claim1, data_claim2, data_claim3, data_claim6, data_claim7
x<-data_claim8 summary(x) table (x)x<-data_claim8 summary(x) table (x)
The function gives the number of automobile insurance third-party liability portfolios in Germany 1960.
data_claim6data_claim6
data_claim6 |
A vector of (non-negative integer) count values. |
The data show the number of automobile insurance third-party liability portfolios in Germany 1960. They were used by Denuit (1997) and fitted by the zero-inflated Poisson (ZIP) distribution.
data_claim6 gives the number of automobile insurance third-party liability portfolios in Germany 1960.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Denuit, M. (1997). A new distribution of Poisson-type for the number of claims. ASTIN Bulletin: The Journal of the IAA, 27(2), 229-242.
data_claims, data_claim1, data_claim2, data_claim3, data_claim4, data_claim5
x<-data_claim6 summary(x) table (x)x<-data_claim6 summary(x) table (x)
The function gives the number of automobile insurance third-party liability portfolios in Switzerland 1961.
data_claim5data_claim5
data_claim5 |
A vector of (non-negative integer) count values. |
The data show the number of automobile insurance third-party liability portfolios in Switzerland 1961. They were used by Denuit (1997) and fitted by the zero-inflated Poisson (ZIP) distribution.
data_claim5 gives the number of automobile insurance third-party liability portfolios in Switzerland 1961.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Denuit, M. (1997). A new distribution of Poisson-type for the number of claims. ASTIN Bulletin: The Journal of the IAA, 27(2), 229-242.
data_claims, data_claim1, data_claim2, data_claim3, data_claim4
x<-data_claim5 summary(x) table (x)x<-data_claim5 summary(x) table (x)
The function gives the number of automobile insurance third-party liability portfolios in Zaire 1974.
data_claim2data_claim2
data_claim2 |
A vector of (non-negative integer) count values. |
The data show the number of automobile insurance third-party liability portfolios in Zaire 1974. They were used by Denuit (1997) and fitted by the zero-inflated Poisson (ZIP) distribution.
data_claim2 gives the number of automobile insurance third-party liability portfolios in Zaire 1974.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Denuit, M. (1997). A new distribution of Poisson-type for the number of claims. ASTIN Bulletin: The Journal of the IAA, 27(2), 229-242.
x<-data_claim2 summary(x) table (x)x<-data_claim2 summary(x) table (x)
The function gives the observed number of births of female children.
data_bfemaledata_bfemale
data_bfemale |
A vector of (non-negative integer) count values. |
The data show the observed number of births of female children studied with mothers of parity 2. They were used by Rahman et al. (2021) and fitted by the one inflated binomial distribution.
data_bfemale gives the observed number of births of female children.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Rahman, T., Hazarika, P. J., & Barman, M. P. (2021). One inflated binomial distribution and its real-life applications. Indian Journal of Science and Technology, 14(22), 1839-1854.
x<-data_bfemale summary(x) table (x)x<-data_bfemale summary(x) table (x)
The function gives the observed number of births male children.
data_bmaledata_bmale
data_bmale |
A vector of (non-negative integer) count values. |
The data show the observed number of births male children studied with mothers of parity 2. They were used by Rahman et al. (2021) and fitted by the one inflated binomial distribution.
data_bmale gives the observed number of births male children.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Rahman, T., Hazarika, P. J., & Barman, M. P. (2021). One inflated binomial distribution and its real-life applications. Indian Journal of Science and Technology, 14(22), 1839-1854.
x<-data_bmale summary(x) table (x)x<-data_bmale summary(x) table (x)
The function gives the number of lightning fatalities in Louisiana caused by boats.
data_bfatalitydata_bfatality
data_bfatality |
A vector of (non-negative integer) count values. |
The data show the number of lightning fatalities in Louisiana caused by boats per year from 1995 through 2012. Recently, they were used by Beckett et al. (2014) and fitted by the zero-inflated Poisson (ZIP) distribution.
data_bfatality gives the number of lightning fatalities in Louisiana caused by boats.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Beckett, S., Jee, J., Ncube, T., Pompilus, S., Washington, Q., Singh, A., & Pal, N. (2014). Zero-inflated Poisson (ZIP) distribution: Parameter estimation and applications to model data from natural calamities. Involve, a Journal of Mathematics, 7(6), 751-767.
x<-data_bfatality summary(x) table (x)x<-data_bfatality summary(x) table (x)
The function gives the observed number of cancer houses.
data_candata_can
data_can |
A vector of (non-negative integer) count values. |
The data show the observed number of cancer houses. They were used by Greenwood and Yule (1920) and underlined an inquiry into the nature of frequency distributions.
data_can gives the observed number of cancer houses.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Greenwood, M., & Yule, G. U. (1920). An inquiry into the nature of frequency distributions representative of multiple happenings with particular reference to the occurrence of multiple attacks of disease or of repeated accidents. Journal of the Royal Statistical Society, 83(2), 255-279.
x<-data_can summary(x) table (x)x<-data_can summary(x) table (x)
The function gives the frequency distribution of the number of carious teeth among the four deciduous molars.
data_cariousdata_carious
data_carious |
A vector of (non-negative integer) count values. |
The data show the frequency distribution of the number of carious teeth among the four deciduous molars. Recently, They were used by Morshedy et al. (2020) and fitted by the discrete Burr-Hatke distribution.
data_carious gives the frequency distribution of the number of carious teeth among the four deciduous molars.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
El-Morshedy, M., Eliwa, M. S., & Altun, E. (2020). Discrete Burr-Hatke distribution with properties, estimation methods, and regression model. IEEE Access, 8, 74359-74370.
x<-data_carious summary(x) table (x)x<-data_carious summary(x) table (x)
The function gives the frequency distribution of the traffic accidents in Changhua City.
data_taccidata_tacci
data_tacci |
A vector of (non-negative integer) count values. |
The data show the traffic accidents that were collected in Changhua city (mainly rural) locates in the central part of Taiwan from 2011-2013 by the Taiwan National Police Agency (NPA). Recently, they were used by Lukusa and Phoa (2020) and fitted by the zero-inflated Poisson model.
data_tacci gives the frequency distribution of the traffic accidents in Changhua city.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Lukusa, M. T., & Phoa, F. K. H. (2020). A Horvitz-type estimation on incomplete traffic accident data analyzed via a zero-inflated Poisson model. Accident Analysis & Prevention, 134, 105235.
x<-data_tacci summary(x) table (x)x<-data_tacci summary(x) table (x)
The function gives the frequency distribution of child deaths in the Bundelkhand region of Uttar Pradesh.
data_bregiondata_bregion
data_bregion |
A vector of (non-negative integer) count values. |
The data show the frequency distribution of child deaths in the Bundelkhand region of Uttar Pradesh. They were used by Singh et al. (2012) and fitted by a probabilistic study of variation in the number of child deaths.
data_bregion gives the frequency distribution of child deaths in the Bundelkhand region of Uttar Pradesh.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Singh, K. K., Singh, B. P., & Singh, N. (2012). A probabilistic study of variation in number of child deaths. Journal of Rajasthan Statistical Association, 1(1), 54-67.
data_hregion, data_cregion, data_eregion
x<-data_bregion summary(x) table (x)x<-data_bregion summary(x) table (x)
The function gives the frequency distribution of child deaths in the Central region of Uttar Pradesh.
data_cregiondata_cregion
data_cregion |
A vector of (non-negative integer) count values. |
The data show the frequency distribution of child deaths in the Central region of Uttar Pradesh. They were used by Singh et al. (2012) and fitted by a probabilistic study of variation in the number of child deaths.
data_cregion gives the frequency distribution of child deaths in the Central region of Uttar Pradesh.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Singh, K. K., Singh, B. P., & Singh, N. (2012). A probabilistic study of variation in number of child deaths. Journal of Rajasthan Statistical Association, 1(1), 54-67.
x<-data_cregion summary(x) table (x)x<-data_cregion summary(x) table (x)
The function gives the frequency distribution of child deaths in the Eastern region of Uttar Pradesh.
data_eregiondata_eregion
data_eregion |
A vector of (non-negative integer) count values. |
The data show the frequency distribution of child deaths in the Eastern region of Uttar Pradesh. They were used by Singh et al. (2012) and fitted by a probabilistic study of variation in the number of child deaths.
data_eregion gives the frequency distribution of child deaths in the Eastern region of Uttar Pradesh.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Singh, K. K., Singh, B. P., & Singh, N. (2012). A probabilistic study of variation in number of child deaths. Journal of Rajasthan Statistical Association, 1(1), 54-67.
x<-data_eregion summary(x) table (x)x<-data_eregion summary(x) table (x)
The function gives the frequency distribution of child deaths in the Hill region of Uttar Pradesh.
data_hregiondata_hregion
data_hregion |
A vector of (non-negative integer) count values. |
The data show the frequency distribution of child deaths in the Hill region of Uttar Pradesh. They were used by Singh et al. (2012) and fitted by a probabilistic study of variation in the number of child deaths.
data_hregion gives the frequency distribution of child deaths in the Hill region of Uttar Pradesh.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Singh, K. K., Singh, B. P., & Singh, N. (2012). A probabilistic study of variation in number of child deaths. Journal of Rajasthan Statistical Association, 1(1), 54-67.
data_argcovid, data_inj2, data_inj3
x<-data_hregion summary(x) table (x)x<-data_hregion summary(x) table (x)
The function gives the frequency distribution of child deaths in rural females of Uttar Pradesh.
data_rfemaledata_rfemale
data_rfemale |
A vector of (non-negative integer) count values. |
The data show the frequency distribution of child deaths in a rural female of Uttar Pradesh. They were used by Singh et al. (2012) and fitted by a probabilistic study of variation in the number of child deaths.
data_rfemale gives the frequency distribution of child deaths in a rural female of Uttar Pradesh.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Singh, K. K., Singh, B. P., & Singh, N. (2012). A probabilistic study of variation in number of child deaths. Journal of Rajasthan Statistical Association, 1(1), 54-67.
x<-data_rfemale summary(x) table (x)x<-data_rfemale summary(x) table (x)
The function gives the frequency distribution of child deaths in the age group 30-39 of Uttar Pradesh.
data_age_30data_age_30
data_age_30 |
A vector of (non-negative integer) count values. |
The data show the frequency distribution of child deaths in the age group 30-39 of Uttar Pradesh. They were used by Singh et al. (2012) and fitted by a probabilistic study of variation in the number of child deaths.
data_age_30 gives the frequency distribution of child deaths in the age group 30-39 of Uttar Pradesh.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Singh, K. K., Singh, B. P., & Singh, N. (2012). A probabilistic study of variation in number of child deaths. Journal of Rajasthan Statistical Association, 1(1), 54-67.
data_age_40, data_cregion, data_eregion
x<-data_age_30 summary(x) table (x)x<-data_age_30 summary(x) table (x)
The function gives the frequency distribution of child deaths in the age group 40-49 of Uttar Pradesh.
data_age_40data_age_40
data_age_40 |
A vector of (non-negative integer) count values. |
The data show the frequency distribution of child deaths in the age group 40-49 of Uttar Pradesh. They were used by Singh et al. (2012) and fitted by a probabilistic study of variation in the number of child deaths.
data_age_40 gives the frequency distribution of child deaths in the age group 40-49 of Uttar Pradesh.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Singh, K. K., Singh, B. P., & Singh, N. (2012). A probabilistic study of variation in number of child deaths. Journal of Rajasthan Statistical Association, 1(1), 54-67.
x<-data_age_40 summary(x) table (x)x<-data_age_40 summary(x) table (x)
The function gives the frequency distribution of child deaths in urban females of Uttar Pradesh.
data_ufemaledata_ufemale
data_ufemale |
A vector of (non-negative integer) count values. |
The data show the frequency distribution of child deaths in urban females of Uttar Pradesh. They were used by Singh et al. (2012) and fitted by a probabilistic study of variation in the number of child deaths.
data_ufemale gives the frequency distribution of child deaths in urban females of Uttar Pradesh.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Singh, K. K., Singh, B. P., & Singh, N. (2012). A probabilistic study of variation in number of child deaths. Journal of Rajasthan Statistical Association, 1(1), 54-67.
x<-data_ufemale summary(x) table (x)x<-data_ufemale summary(x) table (x)
The function gives the frequency distribution of child deaths in Uttar Pradesh.
data_uttardata_uttar
data_uttar |
A vector of (non-negative integer) count values. |
The data show the frequency distribution of child deaths in Uttar Pradesh. They were used by Singh et al. (2012) and fitted by a probabilistic study of variation in the number of child deaths.
data_uttar gives the frequency distribution of child deaths in Uttar Pradesh.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Singh, K. K., Singh, B. P., & Singh, N. (2012). A probabilistic study of variation in number of child deaths. Journal of Rajasthan Statistical Association, 1(1), 54-67.
x<-data_uttar summary(x) table (x)x<-data_uttar summary(x) table (x)
The function gives the frequency distribution of child deaths in the Western region of Uttar Pradesh.
data_wregiondata_wregion
data_wregion |
A vector of (non-negative integer) count values. |
The data show the frequency distribution of child deaths in the Western region of Uttar Pradesh. They were used by Singh et al. (2012) and fitted by a probabilistic study of variation in the number of child deaths.
data_wregion gives the frequency distribution of child deaths in the Western region of Uttar Pradesh.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Singh, K. K., Singh, B. P., & Singh, N. (2012). A probabilistic study of variation in number of child deaths. Journal of Rajasthan Statistical Association, 1(1), 54-67.
x<-data_wregion summary(x) table (x)x<-data_wregion summary(x) table (x)
The function gives the observed number of children per woman.
data_childdata_child
data_child |
A vector of (non-negative integer) count values. |
The data show the observed number of children per woman. They were used by Melkersson and Rooth (2000) and fitted by the inflated count data models.
data_child gives the observed number of children per woman.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Melkersson, M., & Rooth, D. O. (2000). Modeling female fertility using inflated count data models. Journal of Population Economics, 13, 189-203.
x<-data_child summary(x) table (x)x<-data_child summary(x) table (x)
The function gives the frequency distribution of claims of the third liability vehicle insurance in a Chinese insurance company.
data_vinsurancedata_vinsurance
data_vinsurance |
A vector of (non-negative integer) count values. |
The data show the frequency distribution of claims of the third liability vehicle insurance in a Chinese insurance company. They were used by Wang (2011) and fitted by the one mixed negative binomial distribution.
data_vinsurance gives the frequency distribution of claims of the third liability vehicle insurance in a Chinese insurance company.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Wang, Z. (2011). One mixed negative binomial distribution with the application. Journal of Statistical Planning and Inference, 141(3), 1153-1160.
data_claims, data_claim1, data_claim2
x<-data_vinsurance summary(x) table (x)x<-data_vinsurance summary(x) table (x)
The function gives the frequency distribution of chromatid aberrations in human leukocytes.
data_chromatiddata_chromatid
data_chromatid |
A vector of (non-negative integer) count values. |
The data show the frequency distribution of chromatid aberrations in human leukocytes. They were used by Para and Jan (2016) and fitted by the discrete version of the log-logistic distribution.
data_chromatid gives the frequency distribution of chromatid aberrations in human leukocytes.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Para, B. A., & Jan, T. R. (2016). Discrete version of log-logistic distribution and its applications in genetics. International Journal of Mathematics and Mathematical Sciences, 14(4), 407-422.
x<-data_chromatid summary(x) table (x)x<-data_chromatid summary(x) table (x)
The function gives the number of chromosome pairing count data at I metaphase in three plants of Secale vavilovii.
data_p806_8data_p806_8
data_p806_8 |
A vector of (non-negative integer) count values. |
The data show the number of chromosome pairing at I metaphase in three plants of Secale vavilovii. They were used by Jain (1959) and fitted by the negative binomial distribution.
data_p806_8 gives the observed number of chromosome pairing count data at I metaphase in three plants of Secale vavilovii.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Jain, S. K. (1959). Fitting the negative binomial distribution to some data on asynaptic behaviour of chromosomes. Genetica, 30(1), 108-122.
x<-data_p806_8 summary(x) table (x)x<-data_p806_8 summary(x) table (x)
The function gives the number of chromosome pairing count data at I metaphase in three plants of Secale vavilovii.
data_p806_7data_p806_7
data_p806_7 |
A vector of (non-negative integer) count values. |
The data show the number of chromosome pairing count data at I metaphase in three plants of Secale vavilovii. They were used by Jain (1959) and fitted by the negative binomial distribution.
data_p806_7 gives the number of chromosome pairing count data at I metaphase in three plants of Secale vavilovii.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Jain, S. K. (1959). Fitting the negative binomial distribution to some data on asynaptic behavior of chromosomes. Genetica, 30(1), 108-122.
x<-data_p806_7 summary(x) table (x)x<-data_p806_7 summary(x) table (x)
The function gives the number of chromosome pairing count data at I metaphase in three plants of Secale vavilovii.
data_p806_9data_p806_9
data_p806_9 |
A vector of (non-negative integer) count values. |
The data represent the number of chromosome pairing at I metaphase in three plants of Secale vavilovii. They were used by Jain (1959) and fitted by the negative binomial distribution.
data_p806_9 provides the observed number of chromosome pairing count data at I metaphase in three plants of Secale vavilovii.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Jain, S. K. (1959). Fitting the negative binomial distribution to some data on asynaptic behaviour of chromosomes. Genetica, 30(1), 108-122.
x<-data_p806_9 summary(x) table (x)x<-data_p806_9 summary(x) table (x)
The function gives the number of claims per accident.
data_aclaimdata_aclaim
data_aclaim |
A vector of (non-negative integer) count values. |
The data show the number of claims per accident. They were used by Willmot (1987) and fitted by the Poisson-inverse Gaussian distribution.
data_aclaim gives the number of claims per accident.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Willmot, G. E. (1987). The Poisson-inverse Gaussian distribution is an alternative to the negative binomial. Scandinavian Actuarial Journal, 1987(3-4), 113-127.
data_claims, data_claim1, data_claim2, data_claim3
x<-data_aclaim summary(x) table (x)x<-data_aclaim summary(x) table (x)
The function gives the daily newly reported COVID-19 cases.
data_Algeriacoviddata_Algeriacovid
data_Algeriacovid |
A vector of (non-negative integer) count values. |
The data show the daily newly reported COVID-19 cases from Algeria in East Africa, recorded between 13 June 2022 to 3 October 2022. They were used by Shibu et al. (2023) and fitted by the zero-truncated Katz distribution.
data_Algeriacovid gives the daily newly reported COVID-19 cases.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Shibu, D. S., Chesneau, C., Monisha, M., Maya, R., & Irshad, M. R. (2023). A novel zero-truncated Katz distribution by the Lagrange expansion of the second kind with associated inferences. Analytics, 2(2), 463-484.
data_argcovid, data_Bosniacovid
x<-data_Algeriacovid summary(x) table (x)x<-data_Algeriacovid summary(x) table (x)
The function gives the daily reported COVID-19 death cases.
data_Bosniacoviddata_Bosniacovid
data_Bosniacovid |
A vector of (non-negative integer) count values. |
The data show the daily reported COVID-19 death cases from Bosnia and Herzegovina in Europe, recorded between 2 August 2020 to 28 June 2021. They were used by Shibu et al. (2023) and fitted by the zero truncated Katz distribution.
data_Bosniacovid gives the daily reported COVID-19 death cases.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Shibu, D. S., Chesneau, C., Monisha, M., Maya, R., & Irshad, M. R. (2023). A novel zero truncated Katz distribution by the Lagrange expansion of the second kind with associated inferences. Analytics, 2(2), 463-484.
data_argcovid, data_Algeriacovid
x<-data_Bosniacovid summary(x) table (x)x<-data_Bosniacovid summary(x) table (x)
The function gives the observed number of COVID-19 daily new deaths in Luxembourg in 2020.
data_COVIDddata_COVIDd
data_COVIDd |
A vector of (non-negative integer) count values. |
The data show the observed number of COVID-19 daily new deaths in Luxembourg in 2020. Recently, they were used by Junnumtuam et al. (2022) and fitted by the zero and one inflated cosine geometric models.
data_COVIDd gives the observed number of COVID-19 daily new deaths in Luxembourg in 2020.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Junnumtuam, S., Niwitpong, S. A., & Niwitpong, S. (2022). A zero-and-one inflated cosine geometric distribution and its application. Mathematics, 10(21), 4012.
data_argcovid, data_Algeriacovid, data_Bosniacovid
x<-data_COVIDd summary(x) table (x)x<-data_COVIDd summary(x) table (x)
The function gives a sample of 4301 people with deviating behavior.
data_crimedata_crime
data_crime |
A vector of (non-negative integer) count values. |
The data set is from crime sociology consisting of a sample of 4301 people with deviating behavior. Recently, it was used by Zhang et al. (2016) and fitted by the zero-and-one inflated Poisson distribution.
data_crime gives a sample of 4301 people with deviating behavior.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Zhang, C., Tian, G. L., & Ng, K. W. (2016). Properties of the zero-and-one inflated Poisson distribution and likelihood-based inference methods. Statistics and its Interface, 9(1), 11-32.
x<-data_crime summary(x) table (x)x<-data_crime summary(x) table (x)
The function gives the frequency distribution of cysts of kidneys using steroids.
data_cystsdata_cysts
data_cysts |
A vector of (non-negative integer) count values. |
The data show the frequency distribution of cysts of kidneys using steroids. Recently, they were used by Morshedy et al. (2020) and fitted by the discrete Burr-Hatke distribution.
data_cysts gives the frequency distribution of cysts of kidneys using steroids.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
El-Morshedy, M., Eliwa, M. S., & Altun, E. (2020). Discrete Burr-Hatke distribution with properties, estimation methods, and regression model. IEEE Access, 8, 74359-74370.
Para, B. A., & Jan, T. R. (2016). On discrete three-parameter Burr type XII and discrete Lomax distributions and their applications to model count data from medical science. Biometrics and Biostatistics International Journal, 4(2), 1-15.
x<-data_cysts summary(x) table (x)x<-data_cysts summary(x) table (x)
The function gives the number of deaths from horse-kicks.
data_deathsdata_deaths
data_deaths |
A vector of (non-negative integer) count values. |
A data set of size n = 280 concerns the number of deaths from horse-kicks. It was used by Preece et al. (1988) and fitted by the generalized linear model.
data_deaths gives the number of deaths from horse-kicks.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Preece, D. A., Ross, G. J. S., & Kirby, S. P. J. (1988). Bortkewitsch's horse-kicks and the generalized linear model. Journal of the Royal Statistical Society: Series D (The Statistician), 37(3), 313-318.
x<-data_deaths summary(x) table (x)x<-data_deaths summary(x) table (x)
The function gives the number of death notices for women who are 80 years of age or older.
data_deathdata_death
data_death |
A vector of (non-negative integer) count values. |
The data show the number of death notices for women who are 80 years of age or older, appearing in the London Times on each day for three consecutive years. Recently, they were used by Zhang et al. (2016) and fitted by the zero-and-one inflated Poisson distribution.
data_death gives the number of death notices for women who are 80 years of age or older.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Zhang, C., Tian, G. L., & Ng, K. W. (2016). Properties of the zero-and-one inflated Poisson distribution and likelihood-based inference methods. Statistics and its Interface, 9(1), 11-32.
Gupta, P. L., Gupta, R. C., & Tripathi, R. C. (1996). Analysis of zero-adjusted count data. Computational Statistics & Data Analysis, 23(2), 207-218.
Hasselblad, V. (1969). Estimation of finite mixtures of distributions from the exponential family. Journal of the American Statistical Association, 64(328), 1459-1471.
Schilling, W. (1947). A frequency distribution is represented as the sum of two Poisson distributions. Journal of the American Statistical Association, 42(239), 407-424.
x<-data_death summary(x) table (x)x<-data_death summary(x) table (x)
The function gives the number of dentists visiting data from Swedish Level of Living Surveys.
data_dentistdata_dentist
data_dentist |
A vector of (non-negative integer) count values. |
The data set represents a panel data from Swedish Level of Living Surveys in 1974 and 1991. To examine the long-term impact of frequent dental checkups during adolescents and childhood. Recently, it was used by Zhang (2016) and fitted by the zero-and-one inflated Poisson distribution.
data_dentist gives the number of dentists visiting data from Swedish Level of Living Surveys.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Zhang, C., Tian, G. L., & Ng, K. W. (2016). Properties of the zero-and-one inflated Poisson distribution and likelihood-based inference methods. Statistics and its Interface, 9(1), 11-32.
Erikson, R., & Åberg, R. (Eds.) (1987). Welfare in transition: A survey of living conditions in Sweden, 1968-1981. Oxford University Press.
x<-data_dentist summary(x) table (x)x<-data_dentist summary(x) table (x)
The function gives the number of lightning fatalities in Louisiana caused by a tree.
data_tfatalitydata_tfatality
data_tfatality |
A vector of (non-negative integer) count values. |
The data show the number of lightning fatalities in Louisiana caused by a tree per year from 1995 through 2012. Recently, they were used by Beckett et al. (2014) and fitted by the zero-inflated Poisson (ZIP) distribution.
data_tfatality gives the number of lightning fatalities in Louisiana caused by a tree.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Beckett, S., Jee, J., Ncube, T., Pompilus, S., Washington, Q., Singh, A., & Pal, N. (2014). Zero-inflated Poisson (ZIP) distribution: Parameter estimation and applications to model data from natural calamities. Involve, a Journal of Mathematics, 7(6), 751-767.
x<-data_tfatality summary(x) table (x)x<-data_tfatality summary(x) table (x)
The function gives the number of lightning fatalities in Louisiana caused out in the open.
data_ofatalitydata_ofatality
data_ofatality |
A vector of (non-negative integer) count values. |
The data show the number of lightning fatalities in Louisiana caused out in the open per year from 1995 through 2012. They were used by Beckett et al. (2014) and fitted by the zero-inflated Poisson (ZIP) distribution.
data_ofatality gives the number of lightning fatalities in Louisiana caused by out in the open.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Beckett, S., Jee, J., Ncube, T., Pompilus, S., Washington, Q., Singh, A., & Pal, N. (2014). Zero-inflated Poisson (ZIP) distribution: Parameter estimation and applications to model data from natural calamities. Involve, a Journal of Mathematics, 7(6), 751-767.
x<-data_ofatality summary(x) table (x)x<-data_ofatality summary(x) table (x)
The function gives the number of lightning fatalities in Louisiana caused by golf courses.
data_gfatalitydata_gfatality
data_gfatality |
A vector of (non-negative integer) count values. |
The data show the number of lightning fatalities in Louisiana caused by golf courses per year from 1995 through 2012. Recently, they were used by Beckett et al. (2014) and fitted by the zero-inflated Poisson (ZIP) distribution.
data_gfatality gives the number of lightning fatalities in Louisiana caused by golf courses.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Beckett, S., Jee, J., Ncube, T., Pompilus, S., Washington, Q., Singh, A., & Pal, N. (2014). Zero-inflated Poisson (ZIP) distribution: Parameter estimation and applications to model data from natural calamities. Involve, a Journal of Mathematics, 7(6), 751-767.
x<-data_gfatality summary(x) table (x)x<-data_gfatality summary(x) table (x)
The function gives the frequency distribution of female childbirth in Bihar.
data_bihardata_bihar
data_bihar |
A vector of (non-negative integer) count values. |
The data show the frequency distribution of female childbirth in Bihar. Recently, they were used by Kumar (2020) and fitted by a probability model for the number of female childbirths.
data_bihar gives the frequency distribution of female childbirth in Bihar.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Kumar, A. (2020). A probability model for the number of female childbirths. Journal of Statistics Applications & Probability. 9(3), 525-534.
x<-data_bihar summary(x) table (x)x<-data_bihar summary(x) table (x)
The function gives the frequency distribution of female childbirth in Orissa.
data_orissadata_orissa
data_orissa |
A vector of (non-negative integer) count values. |
The data show the frequency distribution of female childbirth in Orissa. Recently, they were used by Kumar (2020) and fitted by a probability model for the number of female childbirths.
data_orissa gives the frequency distribution of female childbirth in Orissa.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Kumar, A. (2020). A probability model for the number of female childbirths. Journal of Statistics Applications & Probability, 9 (3), 525-534.
x<-data_orissa summary(x) table (x)x<-data_orissa summary(x) table (x)
The function gives the frequency distribution of female childbirth in Rajasthan.
data_rajasthandata_rajasthan
data_rajasthan |
A vector of (non-negative integer) count values. |
The data show the frequency distribution of female childbirth in Rajasthan. Recently, they were used by Kumar (2020) and fitted by a probability model for the number of female childbirths.
data_rajasthan gives the frequency distribution of female childbirth in Rajasthan.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Kumar, A. (2020). A probability model for the number of female childbirths. Journal of Statistics Applications & Probability. 9(3), 525-534.
x<-data_rajasthan summary(x) table (x)x<-data_rajasthan summary(x) table (x)
The function gives the frequency distribution of female childbirth in West Bengal.
data_bengaldata_bengal
data_bengal |
A vector of (non-negative integer) count values. |
The data show the frequency distribution of female childbirth in West Bengal. Recently, they were used by Kumar (2020) and fitted by a probability model for the number of female childbirths.
data_bengal gives the frequency distribution of female childbirth in West Bengal.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Kumar, A. (2020). A probability model for the number of female childbirths. Journal of Statistics Applications & Probability. 9 (3), 525-534.
x<-data_bengal summary(x) table (x)x<-data_bengal summary(x) table (x)
The function gives the number of movements made by a fetal lamb.
data_fetalmdata_fetalm
data_fetalm |
A vector of (non-negative integer) count values. |
The data correspond to a certain order of counts in a study of fetal lambs' breathing and movement patterns to look at potential changes in the amount and pattern of fetal activity throughout the last two-thirds of gestation. Recently, they were used by Zhang et al. (2016) and fitted by the zero-and-one inflated Poisson distribution.
data_fetalm gives many movements made by a fetus during the last two-thirds of gestation.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Zhang, C., Tian, G. L., & Ng, K. W. (2016). Properties of the zero-and-one inflated Poisson distribution and likelihood-based inference methods. Statistics and its Interface, 9(1), 11-32.
Leroux, B. G., & Puterman, M. L. (1992). Maximum penalized likelihood estimation for independent and Markov-dependent mixture models. Biometrics, 545-558.
x<-data_fetalm summary(x) table (x)x<-data_fetalm summary(x) table (x)
The function gives the observed number of high explosive shell manufacture accidents.
data_acciddata_accid
data_accid |
A vector of (non-negative integer) count values. |
The data show the observed number of high explosive shell manufacture accidents. They were used by Greenwood and Yule (1920) and underlined an inquiry into the nature of frequency distributions.
data_accid gives the observed number of High explosive shell manufacture accidents.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Greenwood, M., & Yule, G. U. (1920). An inquiry into the nature of frequency distributions representative of multiple happenings with particular reference to the occurrence of multiple attacks of disease or of repeated accidents. Journal of the Royal Statistical Society, 83(2), 255-279.
x<-data_accid summary(x) table (x)x<-data_accid summary(x) table (x)
The function gives the number of deaths due to horse kicks excluding crops G, I, VI, and XI.
data_edeathdata_edeath
data_edeath |
A vector of (non-negative integer) count values. |
A data set of size n = 200 concerning the number of deaths due to horse-kicks excluding crops G, I, VI, and XI. It was used by Preece et al. (1988) and studied by the Bortkewitsch's horse-kicks and the generalized linear model.
data_edeath gives the number of deaths from horse-kicks excluding crops G, I, VI, and XI.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Preece, D. A., Ross, G. J. S., & Kirby, S. P. J. (1988). Bortkewitsch's horse-kicks and the generalized linear model. Journal of the Royal Statistical Society: Series D (The Statistician), 37(3), 313-318.
x<-data_edeath summary(x) table (x)x<-data_edeath summary(x) table (x)
The function gives the frequency distribution of the length of hospital stay.
data_staysdata_stays
data_stays |
A vector of (non-negative integer) count values. |
The data set consists of the number of hospital stays by United States residents aged 66 and over. Recently, it was used by Aryuyuen et al. (2014) and fitted by the zero-inflated negative binomial-generalized exponential distribution.
data_stays gives the observed frequencies of the number of hospital stays by United States residents aged 66 and over.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Aryuyuen, S., Bodhisuwan, W., & Supapakorn, T. (2014). Zero-inflated negative binomial-generalized exponential distribution and its applications. Songklanakarin Journal of Science and Technology, 36(4), 483-91.
Flynn, M., & Francis, L. A. (2009). More flexible GLMs zero-inflated models and hybrid models. Casualty Actuarial Society, 2009, 148-224.
x<-data_stays summary(x) table (x)x<-data_stays summary(x) table (x)
The function gives the observed number of Iranian household sizes.
data_householddata_household
data_household |
A vector of (non-negative integer) count values. |
A data set that comes from a pseudo panel constructed from information from the 2010-2011 Household Expenditure and Income Survey, which includes details on household size but excludes the head of the family. Therefore, given these data, 0 indicates that there is just one resident of the house. They were used by Mersad et al. (2015) and fitted by the zero-inflated models.
data_household gives the observed number of Iranian household size.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Mersad, M., Ganjali, M., & Rivaz, F. (2015). Some extensions of zero-inflated models and Bayesian tests for them. Journal of Statistical Computation and Simulation, 85(18), 3792-3810.
x<-data_household summary(x) table (x)x<-data_household summary(x) table (x)
The function gives the observed number of industrial accidents.
data_indusaccidata_indusacci
data_indusacci |
A vector of (non-negative integer) count values. |
The data show the observed number of industrial accidents. They were used by Greenwood and Yule (1920) and underlined an inquiry into the nature of frequency distributions.
data_indusacci gives the observed number of industrial accidents.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Greenwood, M., & Yule, G. U. (1920). An inquiry into the nature of frequency distributions representative of multiple happenings with particular reference to the occurrence of multiple attacks of disease or of repeated accidents. Journal of the Royal Statistical Society, 83(2), 255-279.
x<-data_indusacci summary(x) table (x)x<-data_indusacci summary(x) table (x)
The function gives the observed number of females in 100 queues.
data_queuedata_queue
data_queue |
A vector of (non-negative integer) count values. |
The data show the observed number of females in 100 queues of length 10 in a London underground station. They were used by Conigliani et al. (2000) and fitted by the zero-inflated models.
data_queue gives the observed number of females in 100 queues.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Conigliani, C., Castro, J. I., & O'Hagan, A. (2000). Bayesian assessment of goodness of fit against nonparametric alternatives. Canadian Journal of Statistics, 28(2), 327-342.
x<-data_queue summary(x) table (x)x<-data_queue summary(x) table (x)
The function gives the frequancy distribution of lost shoes at a Museum gate.
data_lostdata_lost
data_lost |
A vector of (non-negative integer) count values. |
The data show the frequancy distribution of lost shoes at a Museum gate. They were used by Chandra and Ghosh (2013) and fitted by the generalized Poisson distribution.
data_lost gives the frequancy distribution of lost shoes at a Museum gate.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Chandra, N. K., Roy, D., & Ghosh, T. (2013). A generalized Poisson distribution. Communications in Statistics-Theory and Methods, 42(15), 2786-2797.
x<-data_lost summary(x) table (x)x<-data_lost summary(x) table (x)
The function gives the observed number of machinist accidents in six months of study.
data_machinistdata_machinist
data_machinist |
A vector of (non-negative integer) count values. |
The data show the observed number of machinists accidents six months study. They were used by Greenwood and Yule (1920) and underlined an inquiry into the nature of frequency distributions.
data_machinist gives the observed number of Machinists accidents in six monthly studies.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Greenwood, M., & Yule, G. U. (1920). An inquiry into the nature of frequency distributions representative of multiple happenings with particular reference to the occurrence of multiple attacks of disease or of repeated accidents. Journal of the Royal Statistical Society, 83(2), 255-279.
x<-data_machinist summary(x) table (x)x<-data_machinist summary(x) table (x)
The function gives the number of major derogatory reports in the credit history of individual credit card applicants.
data_derogatorydata_derogatory
data_derogatory |
A vector of (non-negative integer) count values. |
The data set consists of the number of major derogatory reports in the credit history of individual credit card applicants. Recently, it was used by Saengthong et al. (2015) and fitted by the zero-inflated negative binomial-Crack distribution.
data_derogatory gives the number of major derogatory reports in the credit history of individual credit card applicants.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Saengthong, P., Bodhisuwan, W., & Thongteeraparp, A. (2015). The zero-inflated negative binomial-Crack distribution: some properties and parameter estimation. Songklanakarin Journal of Science and Technology, 37(6), 701-711.
x<-data_derogatory summary(x) table (x)x<-data_derogatory summary(x) table (x)
The function gives the number of major US earthquakes per year from 1950 through 2012.
data_earthqdata_earthq
data_earthq |
A vector of (non-negative integer) count values. |
The data show the number of major US earthquakes per year from 1950 through 2012. Recently, they were used by Beckett et al. (2014) and fitted by the zero-inflated Poisson (ZIP) distribution.
data_earthq gives the observed frequencies for the number of major US earthquakes per year from 1950 through 2012.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Beckett, S., Jee, J., Ncube, T., Pompilus, S., Washington, Q., Singh, A., & Pal, N. (2014). Zero-inflated Poisson (ZIP) distribution: Parameter estimation and applications to model data from natural calamities. Involve, a Journal of Mathematics, 7(6), 751-767.
x<-data_earthq summary(x) table (x)x<-data_earthq summary(x) table (x)
The function gives the number of major US wildfires per year from 1997 through 2012.
data_wildfiredata_wildfire
data_wildfire |
A vector of (non-negative integer) count values. |
The data show the number of major US wildfires per year from 1997 through 2012. Recently, they were used by Beckett et al. (2014) and fitted by the zero-inflated Poisson (ZIP) distribution.
data_wildfire gives the number of major US wildfires per year from 1997 through 2012.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Beckett, S., Jee, J., Ncube, T., Pompilus, S., Washington, Q., Singh, A., & Pal, N. (2014). Zero-inflated Poisson (ZIP) distribution: Parameter estimation and applications to model data from natural calamities. Involve, a Journal of Mathematics, 7(6), 751-767.
x<-data_wildfire summary(x) table (x)x<-data_wildfire summary(x) table (x)
The function gives the frequency distribution of male sibship.
data_sibshipdata_sibship
data_sibship |
A vector of (non-negative integer) count values. |
The data show the frequency distribution of male sibship. They were used by Sweeney et al. (2014) and fitted by the zero & N inflated binomial distribution.
data_sibship gives the frequency distribution of male sibship.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Sweeney, J., Haslett, J., & Parnell, A. C. (2014). The zero & N inflated binomial distribution with applications. arXiv preprint arXiv:1407.0064.
x<-data_sibship summary(x) table (x)x<-data_sibship summary(x) table (x)
The function gives the observed frequencies for the number of migrants from a household in the semi-urban type of village.
data_migrantsdata_migrants
data_migrants |
A vector of (non-negative integer) count values. |
The data set consists of the number of migrants from a household in the semi-urban type of village. It was used by Pandey et al. (2015) and fitted by the inflated probability model on rural out-migration.
data_migrants gives the observed frequencies for the number of migrants from a household in the semi-urban type of village.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Pandey, A., Pandey, H., & Shukla, V. K. (2015). An inflated probability model on rural out migration. Journal of Computer and Mathematical Sciences, 6(12), 702-711.
x<-data_migrants summary(x) table (x)x<-data_migrants summary(x) table (x)
The function gives the observed frequencies for the number of migrants from a household in a growth centre type of village.
data_migrantdata_migrant
data_migrant |
A vector of (non-negative integer) count values. |
The data set consists of the number of migrants from a household in a growth centre type of village. It was used by Pandey et al. (2015) and fitted by the inflated probability model on rural outmigration.
data_migrant gives the observed frequencies for the number of migrants from a household in a growth centre type of village.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Pandey, A., Pandey, H., & Shukla, V. K. (2015). An inflated probability model on rural out migration. Journal of Computer and Mathematical Sciences, 6(12), 702-711.
x<-data_migrant summary(x) table (x)x<-data_migrant summary(x) table (x)
The function gives the frequency distribution of the number of actions taken in response to a decision by the Court from 1979-1988.
data_actiondata_action
data_action |
A vector of (non-negative integer) count values. |
The data contain the frequency distribution of the number of actions taken in response to a decision by the Court from 1979-1988. They were used by Zorn (1998) and fitted by the zero-inflated and hurdle models.
data_action gives the frequency distribution of the number of actions taken in response to a decision by the Court from 1979-1988.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Zorn, C. J. (1998). An analytic and empirical examination of zero-inflated and hurdle Poisson specifications. Sociological Methods & Research, 26(3), 368-400.
x<-data_action summary(x) table (x)x<-data_action summary(x) table (x)
The function gives the total number of migrants in household cohort excluding international migrants from the rural areas of Comilla district of Bangladesh.
data_migrandata_migran
data_migran |
A vector of (non-negative integer) count values. |
The data set consists of the number of households according to the total number of migrants in the household cohort excluding international migrants from the rural areas of Comilla district of Bangladesh. It was used by Pandey and Tiwari (2011) and fitted by the inflated probability model on rural out-migration.
data_migran gives the observed frequencies for the number of migrants from a household in a growth center type of village.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Pandey, H. & Tiwari, R. (2011), An inflated probability model for the rural out-migration, Recent Research in Science and Technology 2011, 3(7): 100-103
x<-data_migran summary(x) table (x)x<-data_migran summary(x) table (x)
The function gives the number of times that the word may appear per block.
data_blockdata_block
data_block |
A vector of (non-negative integer) count values. |
A data set of size n = 262 concerns the number of times that the word may appear per block in papers by James Madison. It was used by Conigliani et al. (2000) and underlined the Bayesian assessment of goodness of fit against nonparametric alternatives.
data_block gives the number of times that the word may appear per block.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Conigliani, C., Castro, J. I., & O'Hagan, A. (2000). Bayesian assessment of goodness of fit against nonparametric alternatives. Canadian Journal of Statistics, 28(2), 327-342.
x<-data_block summary(x) table (x)x<-data_block summary(x) table (x)
The function gives the observed number of occupational injuries among post-cleaners.
data_inj2data_inj2
data_inj2 |
A vector of (non-negative integer) count values. |
The data evaluate the effectiveness of a consultative manual handling workplace risk assessment team (WRATS) in reducing the risk of occupational injury among cleaners within a 600-bed hospital. They were used by Carrivick et al. (2003) and fitted by the zero-inflated Poisson modeling to evaluate occupational safety interventions.
data_inj2 gives the observed number of occupational injuries among post-cleaners.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Carrivick, P. J., Lee, A. H., & Yau, K. K. (2003). Zero-inflated Poisson modeling to evaluate occupational safety interventions. Safety Science, 41(1), 53-63.
x<-data_inj2 summary(x) table (x)x<-data_inj2 summary(x) table (x)
The function gives the frequency distributions for orderly post-WRATS (workplace risk assessment team).
data_inj4data_inj4
data_inj4 |
A vector of (non-negative integer) count values. |
The data evaluate the effectiveness of a consultative manual handling workplace risk assessment team (WRATS) in reducing the risk of occupational injury among cleaners within a 600-bed hospital. They were used by Carrivick et al. (2003) and fitted by the zero-inflated Poisson modeling to evaluate occupational safety interventions.
data_inj4 gives the frequency distributions for orderly post-WRATS.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Carrivick, P. J., Lee, A. H., & Yau, K. K. (2003). Zero-inflated Poisson modeling to evaluate occupational safety interventions. Safety Science, 41(1), 53-63.
data_inj1, data_inj2, data_inj3
x<-data_inj4 summary(x) table (x)x<-data_inj4 summary(x) table (x)
The function gives the frequency distributions for orderly pre-WRATS (workplace risk assessment team).
data_inj3data_inj3
data_inj3 |
A vector of (non-negative integer) count values. |
The data evaluate the effectiveness of a consultative manual handling workplace risk assessment team (WRATS) in reducing the risk of occupational injury among cleaners within a 600-bed hospital. They were used by Carrivick et al. (2003) and fitted by the zero-inflated Poisson modeling to evaluate occupational safety interventions.
data_inj3 gives the frequency distributions for orderly pre-WRATS.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Carrivick, P. J., Lee, A. H., & Yau, K. K. (2003). Zero-inflated Poisson modeling to evaluate occupational safety interventions. Safety Science, 41(1), 53-63.
x<-data_inj3 summary(x) table (x)x<-data_inj3 summary(x) table (x)
The function gives the observed number of onion plants asynaptic.
data_as2data_as2
data_as2 |
A vector of (non-negative integer) count values. |
The data represent the observed number of onion plants asynaptic. They were used by Jain (1959) and fitted by the negative binomial distribution.
data_as2 gives the observed number of onion plants asynaptic.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Jain, S. K. (1959). Fitting the negative binomial distribution to some data on asynaptic behavior of chromosomes. Genetica, 30(1), 108-122.
data_p806_7, data_as7, data_p806_9, data_as1
x<-data_as2 summary(x) table (x)x<-data_as2 summary(x) table (x)
The function gives the observed number of onion plants asynaptic.
data_as7data_as7
data_as7 |
A vector of (non-negative integer) count values. |
The data show the observed number of onion plants asynaptic. They were used by Jain (1959) and fitted by the negative binomial distribution.
data_as7 gives the observed number of onion plants asynaptic.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Jain, S. K. (1959). Fitting the negative binomial distribution to some data on asynaptic behavior of chromosomes. Genetica, 30(1), 108-122.
data_as1, data_p806_8, data_p806_9, data_as1, data_as2
x<-data_as7 summary(x) table (x)x<-data_as7 summary(x) table (x)
The function gives the frequency distribution of patent citations that fall in a category of typical count data.
data_citationdata_citation
data_citation |
A vector of (non-negative integer) count values. |
The data contain the frequency distribution of patent citations that fall in a category of typical count data. They were used by Lee et al. (2007) and fitted by the zero-inflated models.
data_citation gives the frequency distribution of patent citations falling in a category of typical count data.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Lee, Y. G., Lee, J. D., Song, Y. I., & Lee, S. J. (2007). An in-depth empirical analysis of patent citation counts using zero-inflated count data model: The case of KIST. Scientometrics, 70(1), 27-39.
x<-data_citation summary(x) table (x)x<-data_citation summary(x) table (x)
The function gives tumor count frequencies from 158 NF2 patients.
data_tumordata_tumor
data_tumor |
A vector of (non-negative integer) count values. |
The data show the tumor count frequencies from 158 NF2 patients. They were used by Joe and Zhu (2005) and fitted by the generalized Poisson distribution.
data_tumor gives tumor count frequencies from 158 NF2 patients.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Joe, H., & Zhu, R. (2005). Generalized Poisson distribution: the property of mixture of Poisson and comparison with negative binomial distribution. Biometrical Journal: Journal of Mathematical Methods in Biosciences, 47(2), 219-229.
x<-data_tumor summary(x) table (x)x<-data_tumor summary(x) table (x)
The function gives the frequency of stillbirths in 402 litters of New Zealand white rabbits.
data_sbirthsdata_sbirths
data_sbirths |
A vector of (non-negative integer) count values. |
The data set consists of frequency of stillbirths in 402 litters of New Zealand white rabbits. Recently, it was used by Alshkaki (2016) and fitted by the zero-and-one inflated Poisson distribution.
data_sbirths gives the frequency of stillbirths in 402 litters of New Zealand white rabbits.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Alshkaki, R. S. A. (2016). On the zero-one inflated Poisson distribution. International Journal of Statistical Distributions and Applications, 2(4), 42-8.
Morgan, B. T., Palmer, K. J., & Ridout, M. S. (2007). Negative score test statistic. The American Statistician, 61(4), 285-288.
x<-data_sbirths summary(x) table (x)x<-data_sbirths summary(x) table (x)
The function gives the number of suicides per day during lockdown.
data_suicidesdata_suicides
data_suicides |
A vector of (non-negative integer) count values. |
The data show the number of suicides per day during lockdown. Recently, they were used by Rahman et al. (2022) and fitted by the three-inflated Poisson distribution.
data_suicides gives the number of suicides per day during the lockdown.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Rahman, T., Hazarika, P. J., Ali, M. M., & Barman, M. P. (2022). Three-inflated Poisson distribution and its application in suicide cases of India during Covid-19 pandemic. Annals of Data Science, 9(5), 1103-1127.
x<-data_suicides summary(x) table (x)x<-data_suicides summary(x) table (x)
The function gives the frequency distributions of systemic adverse events.
data_systemicdata_systemic
data_systemic |
A vector of (non-negative integer) count values. |
The data show the frequency distributions of systemic adverse events after each of the four injections for the 1005 study participants, which results in 4020 observations. They were used by Rose et al. (2006) and fitted by the zero-inflated and hurdle models for modeling vaccine adverse event count data.
data_systemic gives the frequency distributions of systemic adverse events.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Rose, C. E., Martin, S. W., Wannemuehler, K. A., & Plikaytis, B. D. (2006). On the use of zero-inflated and hurdle models for modeling vaccine adverse event count data. Journal of Biopharmaceutical Statistics, 16(4), 463-481.
x<-data_systemic summary(x) table (x)x<-data_systemic summary(x) table (x)
The function gives the observed number of incidents of international terrorism per month in the USA between 1968 and 1974.
data_terrordata_terror
data_terror |
A vector of (non-negative integer) count values. |
The data show the observed number of incidents of international terrorism per month in the USA between 1968 and 1974. They were used by Mersad et al. (2015) and fitted by the zero-inflated models.
data_terror gives the observed number of incidents of international terrorism per month in the USA between 1968 and 1974.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Mersad, M., Ganjali, M., & Rivaz, F. (2015). Some extensions of zero-inflated models and Bayesian tests for them. Journal of Statistical Computation and Simulation, 85(18), 3792-3810.
Conigliani, C., Castro, J. I., & O'Hagan, A. (2000). Bayesian assessment of goodness of fit against nonparametric alternatives. Canadian Journal of Statistics, 28(2), 327-342.
x<-data_terror summary(x) table (x)x<-data_terror summary(x) table (x)
The function gives the frequency distribution of the word length of a Turkish poem.
data_poemdata_poem
data_poem |
A vector of (non-negative integer) count values. |
The data show the frequency distribution of the word length of a Turkish poem. Recently, they were used by Cueva et al. (2021) and fitted by the Waring distribution.
data_poem gives the frequency distribution of the word length of a Turkish poem.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Cueva-Lopez, V., Olmo-Jimenez, M. J., & Rodriguez-Avi, J. (2021). An over and under dispersed Biparametric extension of the Waring Distribution. Mathematics, 9(2), 170.
x<-data_poem summary(x) table (x)x<-data_poem summary(x) table (x)
The function gives the number of tick counts on each of the 82 sheep.
data_ticksdata_ticks
data_ticks |
A vector of (non-negative integer) count values. |
The data show the number of ticks counted on each of the 82 sheep. They were used by Ross and Preece (1985) and fitted by the negative binomial distribution.
data_ticks gives the number of ticks count on each of the 82 sheep.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Ross, G. J. S., & Preece, D. A. (1985). The negative binomial distribution. Journal of the Royal Statistical Society: Series D (The Statistician), 34(3), 323-335.
x<-data_ticks summary(x) table (x)x<-data_ticks summary(x) table (x)
The function gives the number of tornado occurrences in Lafayette.
data_tornadodata_tornado
data_tornado |
A vector of (non-negative integer) count values. |
The data show the number of tornado occurrences in Lafayette Parish, Louisiana, US per year from 1950 through 2012. Recently, they were used by Beckett et al. (2014) and fitted by the zero-inflated Poisson (ZIP) distribution.
data_tornado gives the number of tornado occurrences in Lafayette.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Beckett, S., Jee, J., Ncube, T., Pompilus, S., Washington, Q., Singh, A., & Pal, N. (2014). Zero-inflated Poisson (ZIP) distribution: Parameter estimation and applications to model data from natural calamities. Involve, a Journal of Mathematics, 7(6), 751-767.
x<-data_tornado summary(x) table (x)x<-data_tornado summary(x) table (x)
The function gives the observed frequencies for the heavy vehicle traffic accident.
data_accidentdata_accident
data_accident |
A vector of (non-negative integer) count values. |
The data consist of the observed frequencies for the heavy vehicle traffic accident in India. Recently, they were used by Alshkaki (2016) and fitted by the zero-and-one inflated Poisson distribution.
data_accident gives the observed frequencies for the heavy vehicle traffic accident.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Alshkaki, R. S. A. (2016). On the zero-one inflated Poisson distribution. International Journal of Statistical Distributions and Applications, 2(4), 42-8.
Sharma, A. K., & Landge, V. S. (2013). Zero inflated negative binomial for modeling heavy vehicle crash rate on Indian rural highway. International Journal of Advances in Engineering & Technology, 5(2), 292.
x<-data_accident summary(x) table (x)x<-data_accident summary(x) table (x)
The function gives the claim frequency for automobile portfolios of a Turkish insurance company occurred between 2012 and 2014.
data_autodata_auto
data_auto |
A vector of (non-negative integer) count values. |
The data contain claim frequency for the automobile portfolios of a Turkish insurance company that occurred between 2012 and 2014. They were used by Sarul and Sahin (2015) and fitted by the zero-inflated and hurdle models in general insurance.
data_auto gives the claim frequency for automobile portfolios of a Turkish insurance company occurred between 2012 and 2014.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Sarul, L. S., & Sahin, S. (2015). An application of claim frequency data using zero-inflated and hurdle models in general insurance. Journal of Business Economics and Finance, 4(4).
data_claims, data_claim1, data_claim2, data_claim3
x<-data_auto summary(x) table (x)x<-data_auto summary(x) table (x)
The function gives the daily COVID-19 new cases in Uganda 37 days.
data_ugacoviddata_ugacovid
data_ugacovid |
A vector of (non-negative integer) count values. |
The data show the daily COVID-19 new cases of Uganda of 37 days, that is recorded from 17 August to 22 September 2020. Recently, they were used by Ibrahim and Almetwally (2021) and fitted by the discrete Marshall-Olkin Lomax distribution.
data_ugacovid gives the daily COVID-19 new cases in Uganda of 37 days.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Ibrahim, G. M., & Almetwally, E. M. (2021). Discrete marshall-Olkin Lomax distribution application of covid-19. Biomedical journal of Scientific & Technical Research, 32(5), 2021.
data_argcovid, data_Algeriacovid, data_Bosniacovid
x<-data_ugacovid summary(x)x<-data_ugacovid summary(x)
The function gives the number of units of consumers goods purchased by households over 26 weeks.
data_unitsdata_units
data_units |
A vector of (non-negative integer) count values. |
The data show the number of units of consumer goods purchased by households over 26 weeks. Recently, they were used by Aryuyuen et al. (2014) and fitted by the zero-inflated negative binomial-generalized exponential distribution.
data_units gives the number of units of consumers goods purchased by households over 26 weeks.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Aryuyuen, S., Bodhisuwan, W., & Supapakorn, T. (2014). Zero-inflated negative binomial-generalized exponential distribution and its applications. Songklanakarin Journal of Science and Technology, 36(4), 483-91.
Lindsey, J. K. (1995). Modeling frequency and count data (Vol. 15). Oxford University Press.
x<-data_units summary(x) table (x)x<-data_units summary(x) table (x)
The function gives the observed number of occupational injuries among cleaners.
data_inj1data_inj1
data_inj1 |
A vector of (non-negative integer) count values. |
The data evaluate the effectiveness of a consultative manual handling workplace risk assessment team in reducing the risk of occupational injury among cleaners within a 600-bed hospital. They were used by Carrivick et al. (2003) and fitted by the zero-inflated Poisson modeling to evaluate occupational safety interventions.
data_inj1 gives the observed number of occupational injuries among cleaners.
Muhammad Imran
R implementation and documentation: Muhammad Imran [email protected].
Carrivick, P. J., Lee, A. H., & Yau, K. K. (2003). Zero-inflated Poisson modeling to evaluate occupational safety interventions. Safety Science, 41(1), 53-63.
data_inj2, data_inj3, data_inj4
x<-data_inj1 summary(x) table (x)x<-data_inj1 summary(x) table (x)