sampleone

sampleone provides R functions for planning, selecting, and estimating from surveys under the main probability sampling designs taught in introductory sampling courses: simple random sampling (with and without replacement), stratified sampling (proportional, Neyman, and cost-based allocation), systematic sampling, one- and two-stage cluster sampling, and ratio/regression estimators.

Developed by Miguel Luiz de Almeida Pinto to accompany the “Noções de Amostragem” course at DEs-ICET-UFMT (Prof. Dr. Mariano Martínez Espinosa, co-author of the package as author of the source course material).

Status

✅ R CMD check --as-cran: 0 errors, 0 warnings, 0 relevant notes. ✅ 136 testthat tests passing, reproducing worked numerical examples from the source course notes. ✅ Formulas catalog complete (all 6 technical chapters) and cross-validated. ✅ Core implementation complete: Cap. 2-7, 45 exported functions.

• Cap. 2 – Conceitos base (pop_*(), amostra_estimadores())
• Cap. 3 – Amostragem Aleatória Simples (aas_*())
• Cap. 4 – Amostragem Estratificada (estr_*(), amostragem_ajustar_resposta())
• Cap. 5 – Amostragem Sistemática (sist_*())
• Cap. 6 – Conglomerados (cong1_*(), cong2_*(), cong_estima())
• Cap. 7 – Razão e Regressão (razao_*(), reg_*())
• Função transversal amostragem_comparar_planos()
• Vinheta “catalogo-formulas” (vignettes/catalogo-formulas.Rmd)
• R CMD check --as-cran local (0 errors, 0 warnings)
• Cobertura de testes com covr
• Publicação no GitHub
• Submissão ao CRAN

Installation (development version)

# install.packages("devtools")
devtools::install_github("mdelapi/sampleone")

Documentation

• Full function reference (input / description / how to call): docs/REFERENCIA_FUNCOES_sampleone.md or the equivalent spreadsheet docs/sampleone_referencia_funcoes.xlsx
• Formula-to-function catalog (vignette): vignette("catalogo-formulas", package = "sampleone")
• Blind validation against an independent exercise list: docs/VALIDACAO_LISTA01.md ## Example

library(sampleone)

# Tamanho de amostra para estimar uma media, populacao finita conhecida
aas_tamanho_media(sigma2 = 1240, d = 7, conf = 0.95, N = 10000)

# Alocacao otima de Neyman em amostragem estratificada
estr_alocacao_neyman(n = 261, Nk = c(1500, 2700, 3300), pk = c(0.10, 0.20, 0.40))

License

MIT © Miguel Luiz de Almeida Pinto. Course content basis: Prof. Dr. Mariano Martínez Espinosa (DEs-ICET-UFMT), used with attribution – see inst/CITATION.