README.md
In sampleone: Survey Sampling Methods for Teaching and Practice

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).

✅ 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.

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

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

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

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))

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