PyPI 官网下载 | NNS-0.1.5.tar.gz

# NNS-Python Nonlinear Nonparametric Statistics From Beta R Version of 2021-12-13 (Version: 8.4-Beta, Date: 2021-12-13) Implemented Functions: * ANOVA * NNS.ANOVA: TODO (deps: NNS.ANOVA.bin) * ARMA * NNS.ARMA: TODO (deps: NNS.seas, ARMA.seas.weighting, NNS.meboot) * ARMA_optim: * NNS.ARMA.optim: TODO (deps: NNS.ARMA) * Binary_ANOVA * NNS.ANOVA.bin: OK * Boost * NNS.boost: TODO (deps: NNS.caus, NNS.reg, NNS.stack) * Causal_Matrix * NNS.caus.matrix: TODO (deps: NNS.caus) * Causation * NNS.caus: TODO (deps: Uni.caus, NNS.caus.matrix) * Copula * NNS.copula: OK * Dependence * NNS.dep: TODO (deps: NNS.part, NNS.dep.matrix) * Dependence_matrix * NNS.dep.matrix: TODO (deps: NNS.dep) * dy_d_wrt * dy.d_: TODO (deps: NNS.reg) * dy_dx * dy_dx: TODO (deps: NNS.dep, NNS.reg) * Internal Functions * mode: TEST * mode_class: TODO * gravity: TEST * gravity_class: TODO * factor_2_dummy: TODO * factor_2_dummy_FR: TODO * generate_vectors: TODO * ARMA_seas_weighting: TODO * is.discrete: TODO * lag_mtx: TODO * NNS_meboot_part: TODO * NNS_meboot_expand_sd: TODO * alt_cbind: TEST (not in newest version, maybe R related) * RP: TODO (not in newest version) * LPM UPM VaR * LPM_VaR: OK * UPM_VaR: OK * used np.quantile instead of tdigest, and root_scalar instead of optimize * Multivariate_Regression * NNS.M.reg: TODO (deps: NNS.part, NNS.dep, NNS::NNS.distance, NNS.copula, NNS.reg) * NNS_Distance * NNS.distance: TODO (deps: dtw, Rfast) * NNS_meboot * NNS.meboot: TODO (deps: NNS.dep, NNS.meboot.expand.sd) * NNS_term_matrix * NNS.term.matrix: OK * NNS_VAR * NNS.VAR: TODO (deps: NNS.reg, NNS.seas, NNS.ARMA.optim, NNS.ARMA, NNS.stack, NNS.dep, NNS.caus) * Normalization * NNS.norm: TODO (deps: NNS.dep, Rfast) * Nowcast * NNS.nowcast: TODO (deps: Quandl, NNS.VAR) * Numerical Differentiation * NNS.diff: TODO (nodeps) * Partition_Map * NNS.part: TODO (deps: internal functions: gravity_class, gravity, mode_class) * Partial Moments * pd_fill_diagonal: OK (Internal use) * LPM: OK Tested * numba_LPM: Numba version (Internal use) * LPM: Vectorized / pandas / numpy friendly * UPM: OK Tested * numba_UPM: Numba version (Internal use) * UPM: Vectorized / pandas / numpy friendly * Co_UPM: OK Tested * _Co_UPM: Internal Use * _vec_Co_UPM: numpy.vectorized * Co_UPM: Vectorized / pandas / numpy friendly * Co_LPM: OK Tested * _Co_LPM: Internal Use * _vec_Co_LPM: numpy.vectorized * Co_LPM: Vectorized / pandas / numpy friendly * D_LPM: OK Tested * _D_LPM: Internal User * _vec_D_LPM: numpy.vectorized * D_LPM: Vectorized / pandas / numpy friendly * D_UPM: OK Tested * _D_UPM: Internal User * _vec_D_UPM: numpy.vectorized * D_UPM: Vectorized / pandas / numpy friendly * PM_matrix: OK * LPM_ratio: OK * UPM_ratio: OK * NNS_PDF: TODO (deps: d/dx approximation, density) * NNS_CDF: TODO (deps: ecdf, density, matplotlib, NNS_reg) * Regression * NNS.reg: TODO (deps: NNS.M.reg, NNS.dep, NNS.part, Uni.caus) * SD Efficient Set * NNS_SD_efficient_set: OK (TODO: numba version?) * Seasonality_Test * NNS.seas: TODO (nodeps) * Stack * NNS.stack: TODO (deps: NNS.reg, NNS::NNS.distance) * Uni_Causation * Uni.caus: TODO (deps: NNS.norm, NNS.dep) * FSD, SSD, TSD * NNS_FSD: OK (TODO: numba version?) * NNS_SSD: OK (TODO: numba version?) * NNS_TSD: OK (TODO: numba version?) * Uni SD Routines * NNS_FSD_uni: OK (TODO: numba version?) * NNS_SSD_uni: OK (TODO: numba version?) * NNS_TSD_uni: OK (TODO: numba version?)